White Paper Version 1.5.9
February 2025, Bernhard Kreinz
Top Level Cyber Threat Clusters
Bridging the Gap: A Unified Approach to identify and categorize Threats in Cyber Risk Management. A Pragmatic Solution for Targeted Cyber Threat Identification and Cyber Risk Management connecting the Operational Level with the Strategic Level.
The cybersecurity landscape is fragmented. Organizations struggle with inconsistent terminology and siloed approaches to threat identification and risk management, hindering effective defense strategies. Current frameworks often conflate vulnerabilities, attack techniques, and outcomes, leading to confusion and gaps in threat modeling. This white paper introduces the Top Level Cyber Threat Clusters (TLCTC) framework, a novel solution designed to bridge this critical gap and unify strategic planning with operational security. Unlike existing approaches, the TLCTC framework provides a universal, consistent taxonomy of ten distinct threat clusters, each rooted in a fundamental underlying vulnerability rather than observed events or attacker behaviors. This clear, cause-oriented categorization facilitates targeted threat identification, precise mapping of threats to controls, and seamless integration with existing frameworks like NIST CSF, MITRE ATT&CK & CWE, and STIX.
The TLCTC framework employs a unique two-tiered approach, distinguishing between strategic management and operational security. At the strategic level, it empowers leadership to define risk appetite, allocate resources effectively, and communicate cyber risk clearly. Operationally, it enables security teams to implement targeted threat intelligence, enhance incident response, and streamline security operations. This unified approach ensures consistent cybersecurity strategy understanding and execution across all levels of the organization.
This white paper details the derivation of the ten threat clusters through a logical thought experiment, provides clear definitions and real-world examples, and outlines methods for integrating the framework into existing security practices, including secure coding guidelines and the software development lifecycle. Furthermore, it introduces the concept of Cyber Threat Radars, a visualization tool based on the TLCTC framework, for improved threat analysis, communication, and collaboration across organizations and national borders. By adopting the TLCTC framework, organizations can transition from reactive, fragmented cybersecurity practices to a proactive, unified approach, strengthening their overall security posture and enabling more informed decision-making in the face of evolving cyber threats, leading to more resilient and adaptable cybersecurity postures. I encourage the cybersecurity community to engage with this framework, validate its applicability, and provide feedback to further refine and enhance its effectiveness.
Why Start With Assumptions and Axioms? 12
Bridging Strategy and Operations: A Comprehensive TwoTiered Approach 24
Cyber Risk Events and Incidents 25
Cyber Bow-Tie and Risk-Management 30
Clarification on Central Event Position 31
Hierarchical Framework for Key Indicators 34
Data Risk Events and Their Sources 38
Sequences in Cyber Threat Clusters 40
Sequences in Attacks: An Example View 41
Concept Applicability - at Interface Level (API) 42
Concept Applicability - at Function Call Level 43
Vertical Stack Application: A Layered Security Approach 44
Standardizing Strategical Cybersecurity 53
Refinement of the Top Level Clusters 53
Standardizing Operational Cybersecurity: 60
The Need for Consistent Sub-Threat Structures (or TTPs) 60
Refinement of the Top Level Clusters 61
IT Systems, Assets, and the TLCTC Framework 64
The Challenge: Moving Beyond IT System Types 65
Strategic vs. Operational Views 66
A. Leveraging NIST CSF functions 70
Cyber Threat Cluster Control Framework 70
Integration with NIST CSF Functions 77
Mapping Threat Clusters to Secure Coding Controls 80
D. Threat Intelligence - Real World Examples 87
NSO Group Pegasus spyware Attack Paths 87
Cobalt Strike as a Multi-Threat Tool 91
E: Threat Intelligence - Analysis of MITRE & STIX 96
Enhancing STIX with the Top Level Cyber Threat Clusters 97
F: Introducing Cyber Threat Radars 106
Enter the Cyber Threat Radar 106
Understanding Cyber Threat Radar Visualizations 108
Standardized Attack Sequence Notation 111
MFA Bombing and MFA Fatigue in TLCTC Attack Path Notation 112
G: Critical Analysis of Existing Frameworks 114
The MITRE Cyber Prep methodology 120
CRF-TT (Cybersecurity Risk Foundation) 124
I. Example Control Matrix with KRI, KCI and KPI 132
K: Physical Layer Analysis in the TLCTC Framework 137
Applicable Threat Clusters 138
L: Integrating Programmable Logic Controller (PLC) Architectures within the TLCTC Framework 140
PLC Architecture and the Client-Server Model 140
Mapping PLC Vulnerabilities to TLCTC Threat Clusters 140
PLC Specific Key Insights within the TLCTC Framework 142
Implications for Security within PLC Architectures 142
M: Enhancing CVE Details with TLCTC 144
Proposed Extended CVE Structure 145
O: Integrating FAIR with the TLCTC Framework 153
Key Enhancements to FAIR Using TLCTC 153
P. TLCTC Practical Application Guidelines 156
Demystifying the Cyber Threat Landscape: A Pragmatic Approach to Threat Identification and Risk Management
Cybersecurity professionals face a critical challenge in effectively identifying and categorizing threats due to the inconsistent and often ambiguous guidance provided by leading standards and frameworks (NIST CSF, ISO 27000, CIS, ENISA, BSI, MITRE, others and all CERT reports I have analyzed). The lack of clear distinctions between threats, threat actors (or their motivation), vulnerabilities, control failures, IT system types, and risk events has led to a semantic blur that hinders the development of effective risk management strategies.
Driven by the need for a more coherent and actionable approach, I embarked on a thought experiment to distill the essence of what constitutes a 'threat' in the cybersecurity domain. The objective was to create a refined conceptual framework that clearly segregates threats from commonly confused elements, providing a universal approach to cybersecurity that can be applied across diverse IT systems and contexts.
The resulting framework, the "Top Level Cyber Threat Clusters (TLCTC)" provides a pragmatic and structured solution for targeted threat identification. It seamlessly integrates enterprise risk management (ERM) with security operations center (SOC) and threat intelligence processes. By defining distinct, non-overlapping categories, this framework eliminates ambiguity and ensures precise mapping of threats to controls. These clusters are universally applicable both horizontally across various domains (e.g., enterprise IT, cloud environments, IoT) and vertically through the IT stack (e.g., application layer, operating system, hardware). This approach bridges the gap between strategic risk management and operational security, empowering organizations to develop targeted threat intelligence, implement effective risk mitigation strategies, and address the complexity of the cyber threat landscape with clarity and confidence.
It is crucial to understand that cyber risks are a subset of the broader category of operational risks (OpRisk). While cyber risk management focuses primarily on threats from unauthorized or unknown entities, a comprehensive risk management strategy must consider the full spectrum of operational risks. This includes traditional IT risks (with threats such as e.g. "error in use" and "abuse of rights"), compliance risks, and third-party risks (including their associated cyber risks). Organizations should integrate cyber risk management within a holistic OpRisk framework to gain a consolidated view of their risk landscape. This approach allows for better resource allocation, more effective risk mitigation strategies, and a clearer understanding of how cyber risks interact with other operational risks. It's important to note that while actions of authorized actors (such as employees or customers) are typically managed under separate risk categories, any attempts by these individuals to breach or misuse systems would fall within the scope of cyber risks. This nuanced approach ensures that all potential cyber threats are addressed, regardless of their origin, while maintaining the broader context of operational risk management.
The Challenge: A Fragmented Cybersecurity Landscape
Today's cybersecurity landscape is fragmented by disparate frameworks, varying terminology, and siloed approaches to threat management. Organizations struggle to maintain a coherent view of cyber threats across strategic planning, operational security, and global collaboration. Standards bodies, security teams, and threat intelligence providers often speak different languages when describing the same threats, leading to inefficient risk management and delayed response times.
The 10 Top Level Cyber Threat Clusters framework serves as a Rosetta Stone in the fragmented cybersecurity landscape, providing a universal translation layer between strategic risk management and operational security through three core stakeholder objectives:
For Strategic Leadership & Risk Management:
For Security Operations & Technical Teams:
For Global Cybersecurity Community:
This streamlined framework bridges the gap between strategic cyber risk management and operational security practices, uniting the fragmented landscape of cybersecurity approaches under a common understanding supported by major organizations and frameworks like NIST, CISA, ETSI, CVE, and MITRE.
YOU MUST AGREE TO THIS ASSUMPTIONS IN ORDER TO VALIDATE THIS CONCEPT
Before diving into the cyber threat clusters, we must establish our foundational principles. In any logical framework, axioms serve as basic truths that we accept without proof, while assumptions define the scope and context of our thinking. Like mathematical proofs that build upon basic axioms, our cyber threat framework requires clear starting points to ensure consistent and logical development.
The following assumptions and axioms form the essential foundation of the 10 Top Level Cyber Threat Clusters concept. You must agree with these basic principles to validate and effectively use this framework. If any of these foundational elements don't align with your understanding, the subsequent threat categorization may not serve its intended purpose.
Without these clear starting points, we risk mixing threats with vulnerabilities, confusing causes with effects, and creating overlapping or inconsistent categories that don't serve practical security needs.
Imagine the complex world of information technology as a single object. This object, although robust and seemingly closed, has various attack surfaces – the generic vulnerabilities.
**1.** We are at asset software. First, we concentrate on the essentials and take care of the functional domain and scope and realize that every function can be abused and that more scope also means more attack surface. Here our first threat cluster arises: **Abuse of Functions**
**2.** Every software, although optimized, may contain code flaws that can be exploited, especially if it is directly exposed (Server side). This leads us to the threat cluster: **Exploiting Server**
**3.** Even on the client side, there is a risk that existing software code flaws can be exploited. This type of attack, where the client accesses a malicious resource, manifests itself in the threat cluster: **Exploiting Client**
**4.** Our software interacts with identities and credentials, both human and technical. When these identities are compromised, they can be abused. This leads to the threat cluster: **Identity Theft**
**5.** Communication is crucial in our connected world. Yet, as data is transmitted between points A and B, rogue parties might eavesdrop or inject themselves. This reveals the threat cluster: **Man in the Middle**
**6.** This continuous connectivity also makes us susceptible to attacks that want to flood our infrastructure or software (application) and put it out of action. This leads us to the threat cluster: **Flooding Attack**
**7.** In the digital landscape, there is a continuous exchange of files and data. Some of these files could contain malware code and thus pose a threat. Here the threat cluster arises: **Malware**
**8.** We must not forget that there are physical points of access and interaction through which intruders might come. Therefore, we have the threat cluster: **Physical Attack**
**9.** And we should not forget about the human factor. We are susceptible to deception, manipulation, and misconduct. This human element leads us to the threat cluster: **Social Engineering**
**10.** Our software or hardware ecosystems are almost always linked with third-party software or hardware. Do we have control over these? This leads to the last threat cluster: **Supply Chain Attack**
Through this thought experiment and careful examination of vulnerabilities in the IT landscape, I have derived these 10 distinct top level threat clusters. It offers us a clear structure and a deeper understanding of the diverse threats that our IT systems, people, and processes face.
Definition: Abuse of Functions involves an attacker manipulating the intended functionality of software or systems for malicious purposes. This includes misusing legitimate features or configurations beyond their designed scope.
Generic Vulnerability: The scope of software and functions. More scope means a larger attack surface, which an attacker can exploit to their advantage.
Context: This threat addresses the functional domain which the software is designed to perform. It's the unintended use of software capabilities that becomes the weak point. Tip: The IT system types and their categorization fit well here, but that's a topic in itself.
Sub-Threats Examples: Data Poisoning, Abuse of document sharing functions, BGP Hijacking, Misuse of API functionalities
Attacker's View: "I abuse a functionality, not a coding issue."
Asset Type: Software
Definition: An attacker targets vulnerabilities in server-side software to manipulate server behavior using exploit code.
Generic Vulnerability: The presence of exploitable flaws in server-side software code, including input validation errors, logic flaws, and other programming mistakes that can be leveraged to execute unintended operations.
Context: A server is a software program or system that waits to receive requests from clients and responds to them. It "serves" the requirements of the client. Exploit code is used to take advantage of a specific vulnerability or set of vulnerabilities.
Sub-Threats Examples: SQL Injections, Buffer Overflows, Remote Code Execution (RCE)
Attacker's View: "I abuse a coding issue on the server side."
Asset Type: Software
Definition: An attacker targets vulnerabilities in client-side software to manipulate client behavior using exploit code, often when the client accesses a malicious resource.
Generic Vulnerability: The presence of exploitable flaws in any client-side software or agent that processes external data. This includes web browsers, email clients, database clients, API consumers, automated services, and background processes.
Context: A client is a software program or system that sends a request to another system (the server) to perform a certain task or function. "Client" here refers to the software, not the physical device. Exploit code in this context is designed to take advantage of specific client-side vulnerabilities.
Sub-Threats Examples: Browser Exploits, PDF Reader Exploits, Office Document Exploits
Attacker's View: "I abuse a coding issue on the client side. If no interaction from the user is required, it is sometimes called 'drive-by infection.'"
Asset Type: Software
Definition: An attacker targets weaknesses in identity and access management to acquire and misuse legitimate credentials.
Generic Vulnerability: Weak Identity Management Processes and/or credential protection mechanisms. This covers inadequate procedures in handling the entire lifecycle of identities and lax credential management.
Context: This threat relates to "design weaknesses" in the use of credentials for identification and authentication. It's distinct from obtaining identities and credentials via other threat clusters (e.g., via exploit). Note: Authorization bypass without credential compromise maps to #1 or #2.
Sub-Threats Examples: Credential Stuffing, Password Spraying
Attacker's View: "I abuse credentials to operate as a legitimate identity or process."
Asset Type: Software
Definition: An attacker intercepts and potentially alters communication between two parties.
Generic Vulnerability: The lack of control over communication flow/path. Context: This threat is often a precursor to Identity Theft, but it's distinct because it involves getting in between communication first.
Sub-Threats Examples: Wi-Fi Eavesdropping, Rogue VPN
Attacker's View: "I abuse my position between communicating parties."
Asset Type: Software
Definition: An attacker overwhelms system resources and capacity limits, leading to disruption of normal operations.
Generic Vulnerability: Capacity limitations. Context: This threat is related to the risk event type "loss of availability." It's distinct from exploits that target nonavailability through flaws.
Sub-Threats Examples: SYN Flood, Layer 7 Flood
Attacker's View: "I abuse the circumstance of always limited capacity in software and systems."
Asset Type: Software
Definition: An attacker abuses the inherent ability of software to execute foreign (malware) code. This includes any software that has code execution as a built-in function (e.g., office/vbscript, pdf/javascript).
Generic Vulnerability: The ability to execute 'foreign code' by design from the perspective of our software.
Context: Unlike exploit code which targets specific vulnerabilities, malware code is designed to perform malicious actions by leveraging intended functionalities. The challenge is to recognize and block the execution of malicious code within otherwise legitimate contexts.
Sub-Threats Examples: Ransomware, Trojans, Spyware, Keyloggers
Attacker's View: "I abuse the opportunity provided by the environment to allow execution of my code."
Asset Type: Software
Definition: An attacker gains unauthorized physical interference with hardware, devices, or facilities.
Generic Vulnerability: The physical accessibility of hardware and the exploitability of Layer 1 (Physical Layer) communications in the OSI model. This includes vulnerabilities in on-premises equipment, cabling, and wireless signals.
Context: The physical layer in cybersecurity refers to the means by which data is converted into physical form for transmission. Attacks on this layer can involve direct physical access to tangible components or manipulation of the intangible signals themselves.
Sub-Threats: Direct Physical Access Attacks: Hardware Tampering, Port Access, Physical Device Theft OR Indirect Physical Access Attacks: TEMPEST, Signal Jamming, Wireless Interception
Attacker's View: "I abuse the physical accessibility of hardware and devices."
Asset Type: Physical
Definition: An attacker manipulates people into performing actions that compromise the security of systems or (business-) processes.
Generic Vulnerability: The generic vulnerability in humans is their gullibility, ignorance, or compromisability.
Context: This threat focuses on the human element in cybersecurity, recognizing that people can be manipulated or deceived.
Sub-Threats Examples: Phishing, Pretexting, Baiting
Attacker's View: "I abuse human trust and psychology to deceive individuals."
Asset Type: Human
Definition: An attacker compromises systems by targeting vulnerabilities in third-party software, hardware, or services that an organization relies on. This includes targeting vulnerabilities in an organization's external suppliers or service providers.
Generic Vulnerability: The necessary reliance on and implicit trust in incorporated third-party components, services, or vendors within the supply chain, creating potential points of compromise outside direct control.
Context: This threat cluster is an initial vector for compromises, such as a compromised update server. It's closely related to third-party risk management.
Sub-Threats Examples: Compromised Libraries or Dependencies, Tampered Software Updates
Attacker's View: "I abuse the trust in third-party components, services, or vendors."
Asset Type: Software, Hardware, Services
Threat Cluster: "A threat cluster organizes a set of threats that exploit the common vulnerabilities related to IT systems and humans."
Threat: "A threat is a set of tactics, techniques and procedures (TTP) that attackers apply to provoke an event or incident, exploiting vulnerabilities in IT systems or human behaviors."
Cyber Risks describe the likelihood of occurrence of a cyber event in which control over IT systems or persons is lost due to one or more of the 10 Top Level Cyber Threat Clusters, leading to consequential damage (impact).
An Attack Path is the Sequence of applied Attack Vectors.
Scope of Server Software: Includes Server APIs, incorporated Library APIs, Socket APIs, and Local APIs that run on server-side systems to provide services and resources to clients.
Scope of Client Software: Encompasses Client APIs, incorporated Library APIs, Socket APIs, and Local APIs that operate on the client side of a communication.
Malicious Code: Distinguished between "Exploit Code", which targets specific vulnerabilities to modify software behavior, and "Malware Code", which operates within expected execution paths for harmful purposes. "Malware Software" refers to the comprehensive suite of tools (foreign code) that may incorporate multiple techniques, including exploit capabilities.
Malvertising: Identified as a method that can deploy either exploits or malware, depending on the attacker's strategy. It's a vector rather than a distinct category of threat.
Phishing: Recognized for its versatility in threat delivery, capable of initiating various attack clusters based on the context of the content it delivers.
Privilege escalation: In the context of the 10 Top Level Cyber Threat Clusters framework, privilege escalation is addressed through multiple clusters, depending on the specific techniques employed by attackers. In the "Exploiting Server" and "Expoiting Client" clusters, privilege escalation often involves exploiting software vulnerabilities, such as buffer overflows or injection flaws, to gain unauthorized higherlevel permissions. However, the Abuse of Functions cluster represents a distinct approach to privilege escalation, where attackers leverage legitimate system features or misconfigurations to elevate their access rights, without necessarily exploiting coding vulnerabilities. Additionally, the Social Engineering cluster can enable privilege escalation by manipulating users into revealing credentials or granting access to restricted resources. While these clusters may lead to similar outcomes, distinguishing the underlying techniques allows for more targeted control implementation and risk management strategies within the framework.
Third-Party Risk and Supply Chain Threat Cluster: A cyber event at a third party only represents a Supply Chain threat when it involves components or services integrated into your IT systems (e.g., software dependencies, update mechanisms, development pipelines) rather than externally managed services. It's crucial to recognize that each third party faces all ten threat clusters themselves, and their compromise through any of these clusters could potentially enable Supply Chain attacks against your organization. This distinction is crucial for accurate threat classification and control implementation within the framework.
Process Injection: Can occur either as #1 (Abuse of Functions) through abuse of legitimate features (like debugging APIs and DLL injection) where the injection capability was intentionally designed, or as #2/#3 (Exploiting Server/Client) through exploitation of code flaws/vulnerabilities (like buffer overflows) where injection was never intended. The key distinction is whether the injection vector was a designed feature being misused versus an underlying software vulnerability being exploited.
A.I. AI AGI - Positioning:
The 10 Top Level Cyber Threat Clusters framework bridges the gap between strategic planning and operational execution in cybersecurity. This two-tiered approach ensures a consistent strategic understanding of cyber risks while allowing flexibility to adapt to emerging threats and evolving attack methodologies at the operational level.
The strategic layer focuses on high-level risk management, policy-making, and program governance. Key components include:
The operational layer is where security controls are implemented, monitored, and adjusted. Key aspects include:
At the center of the bow-tie model are Cyber Risk Events and Cyber Incidents:
Cyber Risk Events: Potential occurrences that could lead to a breach or system compromise Cyber Incidents: Actual security breaches or system compromises that have occurred
The right side of the bow-tie model addresses the potential consequences of cyber risk events and incidents, which are managed at both the strategic and operational levels.
The framework creates a common language and facilitates dynamic interaction between these layers:
By adopting this comprehensive two-tiered approach, organizations can ensure their cybersecurity efforts are both strategic in planning and adaptable in execution, creating a more resilient and effective security posture that addresses both potential and actual cyber risk events.
Cause Side (Threats):
The Top 10 Cyber Threat Clusters, which can lead to a System Risk Event if preventive controls are insufficient.
Risk Event (System Compromise):
The central risk event is the compromise of an IT system or human, resulting in a loss of control - Cyber incident.
Consequences (Data Risk Events):
The compromised system can lead to data risk events such as loss of confidentiality, integrity, or availability.
Consequences (Business Risk Events):
Data risk events can cascade into multiple levels of business risk events and consequences, including financial losses, reputational damage, and operational disruptions.
Cyber Risk describes the probability of occurrence of a cyber event in which IT systems or human actors are compromised due to one or more of the 10 Top Level Cyber Threat Clusters, leading (via Event-Chains) to consequential damage (impact).
Preventive Controls:
Controls implemented to mitigate the likelihood of a risk event occurring, aligned with the Top 10 Cyber Threat Clusters. Using NIST functions this includes IDENTIFY (indirect) and PROTECT (direct).
Detective, Reactive, and Corrective Controls:
Controls designed to identify risk events (Detective/DETECT), respond (RESPOND) to and recover (RECOVER) from them at the system level (Reactive), and ensure business process continuity (Continuity), minimizing overall impact.
Control Failure:
A control failure is a deviation from the control objective, which can allow threats to materialize and impact assets.
Control Objective:
A control objective is the specific aim or purpose that a control is intended to achieve. It defines what the control should accomplish in terms of risk mitigation for a particular threat cluster. Each control is aligned with a single, clear objective.
Control Design Effectiveness:
Design effectiveness evaluates whether a control, as conceived and structured, is capable of achieving its objective if it operates as intended. It assesses the theoretical capability of the control to address the identified risk within its specific threat cluster.
Control Operational Effectiveness:
Operational effectiveness focuses on whether the control is actually working as designed in practice. It examines if the control is being executed correctly and consistently over time to meet its objective.
Relationship to Control Objectives:
Both design effectiveness and operational effectiveness are methods of evaluating how well a control meets its single, defined objective. They are not separate objectives themselves, but rather two aspects of assessing the control's ability to achieve its intended purpose within the framework of the Top Level Cyber Threat Clusters.
Considerations: The achievable level of operational effectiveness may vary depending on the nature of the threat cluster. For example, controls for Malware (#7) may never achieve 100% operational effectiveness due to "1st Wave" aspects and detection latencies. Some controls, like Multi-Factor Authentication for Identity Theft (#4), can theoretically achieve near-perfect operational effectiveness within their specific scope. The interplay between different threat clusters (e.g., Social Engineering #9 potentially circumventing Identity Theft #4 controls) necessitates a holistic approach to control design and implementation.
The Bow-Tie model provides a structured approach to identifying, assessing, and managing cyber risks by connecting threats, cyber risk events/incidents, consequences, and controls in a comprehensive framework. This enables organizations to develop targeted risk mitigation strategies and align their defenses with the ever evolving cyber threat landscape, while also ensuring effective response, recovery, and continuity measures are in place.
Example: the bow-tie here is not complete - its exemplary related to the other operational causes, events and paths
The positioning of "Loss of Control" or "System Compromise" as the central event in the Bow-Tie model requires careful explanation. This placement reflects a crucial understanding: when a threat successfully materializes (a threat event), it often results in some form of compromise or loss of control, which may or may not immediately lead to data risk events.
Examples of Delayed Data Risk Events:
Examples of Immediate Data Risk Events:
This distinction is operationally significant because:
1. It creates a critical detection window between initial compromise and data risk events in cases where effects are not immediate
2. It reflects the reality of modern attacks where adversaries often maintain persistence (via complex attack paths) before executing their ultimate objectives
3. It enables more precise mapping of detective and reactive controls in the period between compromise and data risk events
4. It acknowledges that some threats can lead to immediate data risk events, requiring rapid response capabilities
Therefore, the central position of "Loss of Control" serves as a crucial pivot point between threat realization and potential consequences, supporting both the sequence concept of the framework and the practical reality of cyber attacks. This positioning accommodates both scenarios where data risk events are delayed and where they occur immediately upon compromise.
Don't forget:
The Cyber Bow-Tie model serves as a powerful visual tool for structuring a comprehensive, event-centric cyber risk register. By integrating the 10 Top Level Cyber Threat Clusters with IT and business risk events, this framework enables organizations to systematically identify, assess, and manage their cyber risk landscape.
**Key Performance Indicators (KPIs):**
In the context of the Top Level Cyber Threat Clusters (TLCTC), KPIs are defined as measurable values that demonstrate *the outcome and performance* of our security processes in reaching security objectives. KPIs must be time-based and should reflect not only the results but also the effectiveness over time. For instance, when tracking our response time to incidents, the KPI is the “*Average time to restore critical services to full operation within a 4-hour window*”, emphasizing the time constraint.
**Key Control Indicators (KCIs):**
KCIs measure the operational performance of our security controls, verifying that the intended actions are taken at the appropriate frequency. These indicators provide insights on our ability to apply the correct controls correctly, and also highlight weaknesses in processes, helping to improve our defenses over time. We must also check the effectiveness of our tools. For example, if we have a control that requires "every critical system to be patched within 24 hours", a KCI would be "frequency of patch deployments per day" or a "scan verification of implemented patches".
**Key Risk Indicators (KRIs):**
KRIs focus on indicators that demonstrate the potential for a future cyber threat. They are primarily leading indicators that show the possible risks before a threat occurs. KRIs must be observed in a timeframe that is meaningful. For example, the *"Number of unpatched critical vulnerabilities older than 7 days"* can give a good indication on how our processes handle a critical vulnerability. This helps us identify, understand and prioritize our security efforts to prevent incidents.
Notation and Terminology
KxI represents the integrated framework of:
Base Level Indicators (BxIs): The lowest level of indicators that still make sense. The metrics at the operational level are directly translated into BxIs.
The KxI framework, as outlined below, provides a practical mechanism for organizations to operationalize the 10 Top Level Cyber Threat Clusters. Each Threat Cluster will have associated KRI, KCI and KPI values that help manage the cyber risk. These values can also be used as indicators for the overall performance of a cybersecurity program.
Framework Architecture
Strategic Level
The "Key" designation positions KxIs at the strategic level, reflecting their importance for enterprise decision-making:
Indicator Hierarchy
Indicator Hierarchy is now related to our Threat Clusters. All of the following steps need to be taken for each Threat Cluster, resulting in a full and consistent evaluation. The following levels of Indicators need to exist:
Data Flow and Aggregation
Governance Framework Integration
This structure incorporates governance requirements through:
The framework incorporates risk assessment at all of the above mentioned levels. For example: BxIs allow for a good operational risk assessment, and KxIs do the same at the strategic level.
This structure also ties well to other concepts of this document, such as "Control Objective", "Control Design Effectiveness" and "Control Operational Effectiveness".
The framework accommodates all metric types - from technical infrastructure measurements to business performance indicators - ensuring comprehensive enterprise coverage.
Methodologically, you can also view "Loss of Control" or "System Compromise" as a higher-level classification framework (represents the business impact that you have to evaluate), with the threat clusters as child objects that are then directly assigned to the "Data Risk" events.
In this approach, the threat clusters are subordinate to the overarching "Loss of Control" or "System Compromise" event, which serves as a parent category. Each threat cluster is then linked to specific data risk events, such as loss of confidentiality, integrity, or availability.
This hierarchical structure allows for a clear organization of the relationships between the high-level system compromise event, the threat clusters that can lead to it, and the resulting data risk events. By establishing these connections, you can gain a more granular understanding of how different threat types contribute to data risks and tailor your risk management strategies accordingly.
Representing this hierarchy in a table or matrix format can help visualize the relationships and facilitate risk analysis and communication. The threat clusters would form one dimension of the table, while the data risk events would form another, with the cells indicating the specific connections between them.
This approach complements the Bow-Tie model by providing an alternative perspective on the relationships between threats, system compromises, and data risks, offering additional insights for effective risk management.
Threat Cluster/ loss of control | Loss of Confidentiality | Loss of Integrity | Loss of Availability |
Abuse of Functions | x | x | x |
Exploiting Server | x | x | x |
Exploiting Client | x | x | x |
Identity Theft | x | x | x |
Man in the Middle | x | x | x |
Flooding Attack | x | ||
Malware | x | x | x |
Physical Attack | x | x | x |
Social Engineering | x | x | x |
Supply Chain Attack | x | x | x |
Understanding the relationship between data risk events and their triggers is crucial for effective risk management.
Cyber Threat Cluster-Triggered Data Risk Events Examples:
Data risk events often result from one or more cyber threat clusters. Each cluster can lead to specific types of data risks (or in combination of many applied clusters):
Non-Cyber OpRisk-Triggered Data Risk Events Examples:
Data risk events can also stem from other operational risk factors, which are not classified as cyber risks:
This distinction is vital for developing targeted risk tolerance statements and appropriate mitigation strategies for each category of data risk events and threat clusters. It enables more precise risk management, allowing organizations to tailor their approaches based on the specific nature of potential threats and their impacts.
Question: There are overlapping Threat Clusters, such as Social Engineering and Identity Theft, with Phishing Emails. How are they related?
Answer: While it may initially appear that threat clusters like Social Engineering and Identity Theft overlap, particularly in scenarios involving phishing emails, it's important to understand these as distinct yet sequentially linked components within an attack. The absence of true overlap is fundamental to the consistency of the 10 Top Level Cyber Threat Clusters framework.
Phishing emails typically initiate through the cluster of Social Engineering (Cluster #9), where the attacker manipulates human psychology to provoke an action. Once this action succeeded, this threat was realized. The action is specific action, such as clicking a link to a website and other threats (eg. #3, #7, #4), exploiting human susceptibility to deception. Once the action is taken, the attack may progress to another cluster, such as Identity Theft (Cluster #4). If the link in the phishing email leads to a fraudulent website designed to harvest credentials, the threat transitions into Identity Theft. Here, the focus shifts to the unauthorized acquisition and misuse of personal data.
The clear categorization of these threats in sequences:
Understanding these sequences helps in accurately identifying the progression of an attack, enabling targeted interventions for each phase of the threat. This approach emphasizes the need for distinct countermeasures such as user training and awareness to mitigate Social Engineering and robust authentication processes to prevent Identity Theft.
This presentation details how attacks can be better understood by examining the sequence of threat clusters they involve. By distinguishing between different pathways and their targeted vulnerabilities, we can tailor more effective defensive measures specific to each attack vector.
Initial Threat Cluster | Subsequent Threat Cluster | Example Scenario | Primary Exploited Vulnerability |
Social Engineering (#9) | Identity Theft (#4) | Phishing email with a link to a fraudulent form collecting user IDs and passwords | Human susceptibility to deception - #9, weakness of the procedure with credentials - #4 |
Social Engineering (#9) | Exploiting Client (#3) | Phishing email with a link to a website exploiting a zero-day vulnerability | Human interaction - #9, client-side software vulnerability - #3 |
Man in the Middle (#5) | Identity Theft (#4) | Interception of communication to redirect to a fake website - eg proxy and collect credentials | Compromise of data in transit - #5, access to credentials - #4 |
Each scenario showcases the importance of understanding the transition from one threat cluster to another, thereby helping in designing precise and targeted countermeasures.
A more sophisticated attack: #9->#3->#7->#4->#1->#7 (it starts with mail and ends in encrypted systems ;-)
Based on analysis of the 10 Top Level Cyber Threat Clusters concept, it is indeed applicable at the interface level. This applicability stems from several key aspects of the framework:
By applying this concept at the interface level, organizations can systematically identify and categorize threats specific to their system interfaces, enabling more targeted risk management and security strategies. This approach aligns well with the concept's goal of providing a pragmatic solution for targeted threat identification across diverse IT systems and contexts.
Based on careful consideration and analysis, the 10 Top Level Cyber Threat Clusters concept is applicable at the function call level, with some important considerations:
In conclusion, while theoretically applicable, practical implementation would require careful consideration of the trade-offs between security granularity and system performance/complexity. This approach could be particularly valuable for critical functions handling sensitive data or operations.
Core Concepts
Client-Server Relationship
The client-server relationship in a vertical stack is contextual rather than absolute. Key principles:
Directional Analysis
Component Interaction Model
+-------------------+
| Application | Ring 3: User Mode
| (Client) | |
+-------------------+ V (System Call)
|
+-------------------+
| OS Services | Ring 2: Services
| (Client/Server) | |
+-------------------+ V (Driver Request)
|
+-------------------+
| HAL/Driver Level | Ring 1: Drivers
| (Client/Server) | |
+-------------------+ V (Hardware Access)
|
+-------------------+
| Kernel | Ring 0: Kernel Mode
| (Server) |
+-------------------+
Protection Ring Architecture
Ring 0 (Kernel Mode)
Ring 1 (HAL/Driver Level)
Ring 2 (OS Services)
Ring 3 (User Mode)
Attack Surface Analysis
Ring Boundary Interactions
Vulnerability Mapping Principles
Bidirectional Attack Paths
Downward Path Example (Ring 3 → Ring 0):
1. Client exploit of system call interface (#3)
2. Server exploit in Ring 2 service (#2)
3. Server exploit in Ring 1 driver (#2)
4. Abuse of kernel functions (#1)
Upward Path Example (Ring 0 → Ring 3):
1. Server exploit in interrupt handler (#2)
2. Client exploit in Ring 2 callback (#3)
3. Client exploit in Ring 3 handler (#3)
4. Malware execution in application (#7)
Threat Cluster Application
Applicable Clusters by Ring Boundary
Nine threat clusters apply at each boundary (excluding #9 Social Engineering):
Abuse of Functions (#1)
Exploiting Server (#2)
Exploiting Client (#3)
Identity Theft (#4)
Man in the Middle (#5)
Flooding Attack (#6)
Malware (#7)
Physical Attack (#8)
Supply Chain (#10)
Implementation Framework
Security Control Requirements
Ring Boundary Controls
Ring 3 → Ring 2
Ring 2 → Ring 1
Ring 1 → Ring 0
Case Studies and Common Misconceptions
Illustrative Examples
Hypothetical Vulnerability 1 (Server)
A kernel component contains a buffer overflow vulnerability during system call processing. A user-mode process exploits this by crafting a specific system call that overflows the kernel's buffer on the server side code. This is categorized as "#2 Exploiting Server" because the vulnerability exists in the server code processing a user request.
Hypothetical Vulnerability 2 (Client)
A driver handling network data receives a carefully crafted packet that triggers a buffer overflow in the driver's processing code during a network event handling callback. Though triggered by network data, this is a client-side issue in how it processes responses from the network, mapping to "#3 Exploiting Client".
Real-World Case Study: Hyper-V VSP
A vulnerability exists within the Hyper-V VSP component where it acts as a client making calls to the NT Kernel. The vulnerability involves the VSP component mismanaging a response from the Kernel, leading to a buffer overflow on the client side. Despite the final impact involving privilege elevation and kernel code execution, the initial vulnerability is exploited within the client-side code, mapping to "#3 Exploiting Client".
Common Misconceptions
Privilege Escalation vs. Root Cause
Component Roles
Analysis Pitfalls
Key Analysis Requirements
Implementation Guidelines
Critical Success Factors
Best Practices
Here I would expect NIST to incorporate my TLCTC concept and make some refinements. The Goal should be building a bridge to the (extended) MITRE World (ATT&CK and CVE)
The Top Level Cyber Threat Clusters (TLCTC) framework proposes a structured approach to categorizing cyber threats through ten distinct clusters. This naturally raises the question: "Why ten clusters?" This analysis explores the rationale behind this number, its implications for practical implementation, and its role in the evolution of cyber threat categorization.
The selection of ten clusters serves as a deliberate challenge to the cybersecurity community, particularly to major bodies like NIST and MITRE. It highlights the limitations of existing frameworks like STRIDE, which has served the industry well but struggles to address the full spectrum of modern cyber threats. The TLCTC framework demonstrates that a more comprehensive and logically consistent approach is possible, while remaining open to evolution as long as the fundamental axioms are not violated.
The framework's structure allows for evolution within certain clusters.
Here I provide examples:
Refinement of #2 Exploiting Server:
The Exploiting Server threat cluster targets vulnerabilities in server-side software to manipulate server behavior or gain unauthorized access using exploit code. This refinement provides a more detailed categorization of the attack vectors within this cluster: imo: job of a NIST & MITRE agreement, but until then:) …
#2.1 Server communication protocol exploit
This vector targets vulnerabilities in the protocols used for communication between servers and clients.
Examples:
#2.2 Server core function exploit
This vector focuses on vulnerabilities within the main functionalities of the server software, including internal data parsing and handling.
Examples:
#2.3 Server external handler exploit
This vector covers vulnerabilities that arise when the server delegates handling to external software or components.
Examples:
Key Characteristics of Exploiting Server:
This refinement maintains the generic nature of the threat cluster while providing a comprehensive framework for categorizing server-side exploits across various types of server software. It aligns with the concept's goal of being universally applicable across different IT systems and contexts.
Refinement of #3 Exploiting Client
The Exploiting Client threat cluster targets vulnerabilities in client-side software to manipulate client behavior or gain unauthorized access using exploit code. This refinement provides a more detailed categorization of the attack vectors within this cluster: imo: job of a NIST & MITRE agreement, but until then:) …
#3.1 Client communication protocol exploit
This vector targets vulnerabilities in the protocols used for communication between clients and servers.
Examples:
#3.2 Client core function exploit
This vector focuses on vulnerabilities within the main functionalities of the client software, including internal data parsing and handling.
Examples:
#3.3 Client external handler exploit
This vector covers vulnerabilities that arise when the client delegates handling to external software or components.
Examples:
Key Characteristics of Exploiting Server:
This refinement maintains the generic nature of the threat cluster while providing a comprehensive framework for categorizing client-side exploits across various types of client software. It aligns with the concept's goal of being universally applicable across different IT systems and contexts.
Refinement of Physical Attack Cluster (#8)
The Physical Attack cluster can be further refined into two subcategories to provide a more nuanced understanding of the different types of physical threats
#8.1 Direct Physical Access Attacks
This subcategory encompasses any attack that requires direct physical interaction with the hardware or its immediate environment.
#8.2 Indirect Physical Access Attacks
This subcategory focuses on attacks that exploit physical vulnerabilities without direct contact with the hardware.
This refinement allows for a more precise categorization of physical threats, enabling organizations to develop more targeted security measures and risk management strategies for each subcategory of physical attacks.
Refinement of the Supply Chain Attack Cluster (#10)
#10.1 Update Vector (active, post-deployment)
This covers attacks on update mechanisms and distribution channels for software, firmware, or hardware already in use. It would include compromised third-party components delivered via updates.
#10.2 Development Vector (silent, pre-deployment)
This encompasses attacks on the development process, including compromises of source code repositories, build systems, or testing environments. It would also cover the incorporation of vulnerable or malicious third-party libraries or components during development.
#10.3 Hardware Supply Chain Vector
This covers attacks that target hardware components or manufacturing processes.
Each of these subcategories represents a distinct and generic vector in the supply chain, following the axiom of distinction.
These examples are intended to show that, taking the axioms into account, the TLCTC concept can be expanded. The notation of an attack path can thus be designed in a more granular way, e.g., #10.1->#7->[Data Risk Event].
As mentioned, the TLCTC concept starts with 10 clusters, primarily for pragmatic reasons and until the TLCTC concept gains more widespread adoption. However, without the key players NIST and MITRE, it will be difficult.
At the operational level of cybersecurity, there is a pressing need for a standardized approach to categorizing and managing sub-threats, TTPs (Tactics, Techniques, and Procedures), and attack sequences. While the Top Level Cyber Threat Clusters provide a solid foundation at the strategic level, the operational layer requires further refinement and consistency.
Currently, organizations like NIST, CISA, MITRE, as well as standards such as STIX and RFC 9424, each have their own approaches to describing and categorizing threats at a granular level. This fragmentation leads to several challenges:
To address these issues, I propose that the cybersecurity community should work towards developing consistent subthreat structures within each of the Top Level Cyber Threat Clusters. This standardization effort should aim to:
As examples of how this standardization could be implemented, i have developed detailed integration proposals for two major frameworks:
These proposals serve as starting points for discussion and highlight the potential benefits of a more standardized approach. By adopting a consistent sub-threat structure across different frameworks and standards, we can:
Moving forward, it is crucial for the cybersecurity community to come together and work towards this standardization. This effort will require collaboration between standards bodies, security vendors, researchers, and practitioners to develop a truly unified approach to operational cybersecurity.
While the following examples provide some guidance, they are not always precise, as they are not standardized definitions. I am referring to MITRE here, with the understanding that MITRE would need to be "expanded." See my proposal for MITRE here: [LINK].
NIST and CISA likely appreciate this as well. I am, of course, open to the idea of creating potential sub-clusters. However, my goal was to define straightforward top-level categories.
Following examples should give you an idea of the direction. IMO: Most are "buzzwords", which means lack of definition - > Hello MITRE and NIST! You are welcome here :-)
1. **Abuse of Functions** - Sub-Threats: - Abuse of standard services and features - Abuse of information made public - Data Poisoning - Abuse of insecure service configurations - Abuse of legitimate system tools (e.g., lolBins, PowerShell)) - ARP Spoofing -> leads to man in the middle #5 - DNS Spoofing -> leads to man in the middle #5 - 20 BGP Hijacking -> leads to man in the middle #5 - SSL Stripping (attacker needs to be MitM already eg via ARPPoisoning - and SSL Stripping is an abuse of a (downgrade) function
2. **Exploiting Server** - Sub-Threats: - Buffer Overflows - SQL Injections - Cross-Site Scripting (XSS) - XML External Entity (XXE) Attacks - Server Side Request Forgery (SSRF) - Directory Traversal - Ping of Death
3. **Exploiting Client** - Sub-Threats: - Malvertising - Watering Hole Attacks - Clients App Exploits (e.g. Browser, PDF Reader, Java, Flash) - Insecure Deserialization
4. **Identity Theft** - Sub-Threats: - Credential Stuffing (eg IDs, Passwords, Certificates, Private Keys) - Session Hijacking - Pass-the-Ticket/Pass-the-Hash Attacks - Token Hijacking - password spray attacks - Brute-Force Attacks - Fake Websites - Domain Squatting
5. **Man in the Middle** - Sub-Threats: (MitM has a focus on a already compromised environment - you cannot trust any components between the endpoints A and B) - Wi-Fi Eavesdropping (attacker needs to be MitM already eg within physical range -> #8) - Pineapple Attacks (attacker needs to be MitM already eg within physical range -> #8) - Rogue Hotspots (attacker needs to be MitM already eg within physical range -> #8 then eg fakes SSID #4)
6. **Flooding Attack** - Sub-Threats: mostly known as DDOS Attacks on dif erent layers - SYN Flood - UDP Flood - HTTP Flood - ICMP Flooding - Slowloris - NTP/DNS Amplification Attacks - Botnet-Driven Attacks
7. **Malware** - Sub-Threats: - Ransomware - Trojans - Keyloggers - Rootkits - Spyware - Worms - Adware - Mobile Malware - E-Banking Malware
8. **Physical Attack** Direct Physical Access Attacks: - Evil Maid Attacks - Hardware Keyloggers - Direct Hardware Tampering - Device Theft - Physical Intrusion into Secure Areas - USB Baiting (leaving malicious USB devices) - Replacement of Hardware Components - Physical Damage to Infrastructure Indirect Physical Access Attacks: - TEMPEST Attacks (Electromagnetic Emissions) - RFID Skimming - Acoustic Attacks (Sound Wave Exploitation) - Optical Attacks (e.g., Shoulder Surfing) - Thermal Imaging Attacks - Power Analysis Attacks - Environmental Manipulation (e.g., Temperature, Humidity) - Van Eck Phreaking (Remote Screen Viewing)
9. **Social Engineering** (Information Manipulation) - Sub-Threats: - CEO Fraud - Subscription Traps - Fraudulent Contests - Check Fraud - Cyberbullying - Dubious Webshop - Requests for financial help from acquaintances - Fake Support - Financial Agents - Fake Threat Emails from Authorities - Investment Fraud - Classified Ads Fraud - Package Subscription Traps - Invoice Manipulation Fraud (BEC Fraud) - Romance Scam - Defamation - Sextortion - Forbidden Pornography - Advance Fee Fraud - Web Administrators Blackmail - Tailgating (unauthorized access) - Phishing - Vishing - Smishing - Baiting (e.g., with USB sticks)
10. **Supply Chain** - Sub-Threats: - Compromised Libraries or Dependencies - Backdoors - Update-Server Hijacking - Compromised Container Images - Manipulated Hardware (physical attack on Supply Chain)
The cyber threat landscape experiences constant evolution, primarily driven by changes in IT system types, their functional domains, and underlying technologies. From traditional enterprise systems to cloud infrastructure, from Internet of Things (IoT) devices to quantum computing platforms, the variety and complexity of IT systems continue to expand. However, it's crucial to understand that while the technological implementation details may change, the fundamental vulnerabilities that cyber threats exploit remain consistent. The Top Level Cyber Threat Clusters (TLCTC) framework maintains its relevance and applicability precisely because it focuses on these underlying generic vulnerabilities rather than specific technological implementations.
This framework's strength lies in its ability to categorize threats based on root causes and generic vulnerabilities, transcending the specific characteristics of any particular IT system type. Whether analyzing threats to a traditional database server, a cloud-native application, or an emerging quantum computing platform, the same ten clusters provide a comprehensive framework for threat identification and risk management. This consistency enables organizations to maintain effective security strategies even as their technology landscape evolves.
This approach allows organizations to:
A Strategic Perspective
Organizations often attempt to categorize cyber threats based on IT system types – creating separate threat categories for cloud systems, IoT devices, SCADA systems, and other technology-specific implementations. This approach presents several critical problems:
This system-type-based categorization persists despite its limitations, largely due to historical practices and the natural tendency to organize threats around familiar technical boundaries. However, this approach becomes increasingly unsustainable as technology landscapes grow more complex and interconnected.
1. Generic Vulnerabilities vs. System Types
2. Asset Management in Risk Strategy
Strategic Level (GOVERN)
Operational Level
Example: Cloud Infrastructure
Strategic View
Operational View
The AI System Example
As an IT System
As a Tool
As an Actor
Asset Inventory (GOVERN)
Threat Analysis (Strategic)
Control Implementation (Operational)
The TLCTC framework provides a strategic foundation for threat categorization while acknowledging the operational importance of IT system types. This dual-level approach ensures:
By maintaining this clear separation between strategic threat categories and operational asset management, organizations can build more effective and sustainable security programs.
The NIST CSF functions can be used to organize controls and their objectives (e.g., "Protect from Malware Execution", "Detect Malware Execution") within each of the Top Level Cyber Threat Clusters. This combination would provide a comprehensive framework for both threat identification and risk evaluation.
The "Identify" function, enhanced with the Cyber Threat Clusters, would enable more effective management of both high-level threats and operational sub-threats, ensuring a complete and coherent control framework.
Overview
This framework integrates the 10 Top Level Cyber Threat Clusters with the NIST Cybersecurity Functions to provide a comprehensive approach to cybersecurity risk management.
Structure
Use this Scheme for each Threat Cluster:
NIST Function | Control Objective | Local Controls | Umbrella Controls |
Identify | Identify weaknesses enabling [Threat] Event | [Specific measures] | [Overarching systems/processes] |
Protect | Protect from [Threat] Event | [Specific measures] | [Overarching systems/processes] |
Detect | Detect [Threat] Event | [Specific measures] | [Overarching systems/processes] |
Respond | Respond to [Threat] Event | [Specific measures] | [Overarching systems/processes] |
Recover | Recover from [Threat] Event | [Specific measures] | [Overarching systems/processes] |
Example: #2 Exploit Server (Controls are not complete - its a POC here)
NIST Function | Control Objective | Local Controls | Umbrella Controls |
Identify | Try to identify failures in the code of your Server Software | Fuzzy Testing, Network based Vulscan | Threat Intell this topic, CVE Subscriptions, Bug Bounty Programm |
Protect | Protect Server from being exploited | Patchmanagement, Secure Coding | WAF |
Detect | Detect Exploited Server | Local Event Logging | SIEM |
Respond | Respond to exploited server | Emergency Patch, | CSIRT, Exploit Server Response Plan (Make WAF Rules) |
Recover | Recover Server Exploit Event | Maintain your Repo, Restore | IT-SCM |
Example: #4 Identity Theft (Controls are not complete - its a POC here)
NIST Function | Control Objective | Local Controls | Umbrella Controls |
Identify | Identify weaknesses in identity management (tech and org); | Password policy audits, Penetration testing | Comprehensive Identity and Access Management (IAM) assessment framework , Bug Bounty Program |
Protect | Protect Identity Protect Credentials | Multi-Factor Authentication (MFA), Secure credential distribution | Enterprise-wide Identity Governance and Administration (IGA) system |
Detect | Detect Identity Theft | Anomaly detection rules, User behavior monitoring | Security Information and Event Management (SIEM) system |
Respond | Respond to Identity Theft | Account lockout procedures, Incident response plan activation | Integrated Incident Response Platform |
Recover | Recover Identity | Identity restoration, Credential reset procedures | Enterprise-wide Business Continuity Management System |
While NIST functions provide an excellent structure for organizing controls and their objectives within each Cyber Threat Cluster, ISO standards can play a complementary role in this framework. Organizations can leverage ISO's comprehensive control sets (such as those in ISO 27002) and risk management methodologies (ISO 27005) to enhance control selection and implementation within the NIST function structure, thereby creating a more robust and internationally aligned approach to addressing each threat cluster.
This framework can be applied to all 10 Top Level Cyber Threat Clusters:
#1 Abuse of functions
#2 Exploiting Server
#3 Exploiting Client
#4 Identity Theft
#5 Man in the middle
#6 Flooding Attack
#7 Malware
#8 Physical Attack
#9 Social Engineering
#10 Supply Chain (Attack)
For each cluster, specific Control Objectives, Local Controls, and Umbrella Controls should be defined according to the unique characteristics and risks associated with that threat type.
Important Consideration for Umbrella Controls:
Umbrella Controls provide protection only for specific 'Groups of IT-Systems' within their scope. For example, a firewall or network zone can protect 'inner IT-Systems' but cannot effectively protect exposed IT-Systems. This limitation requires security architects to:
This understanding is crucial for effective control implementation and supports the framework's emphasis on attack sequences and paths.
Where are the GOV controls?
The GOVERN (GV) function in NIST CSF 2.0 operates at a strategic level, focusing on establishing the overall cybersecurity risk management framework rather than addressing specific threats directly. Unlike functions such as PROTECT or DETECT, which have controls directly linked to mitigating or identifying particular cyber threats, GOVERN controls are "assurance controls" that ensure the organization has a comprehensive approach to cybersecurity. These controls create the structure and context within which other functions operate, including setting risk appetite, defining roles and responsibilities, and establishing policies. While the threat categorization, such as the Top Level Cyber Threat Clusters, is indeed a crucial element in the risk register that GOVERN oversees, the GV controls themselves do not directly counter specific threats. Instead, they provide the strategic foundation that enables the organization to effectively manage and respond to the entire spectrum of cyber risks.
The Secure Software Development Life Cycle (SSDLC) provides a structured approach to embedding security throughout the software development process. By integrating the 10 Top Level Cyber Threat Clusters framework, organizations can establish a consistent, threat-informed methodology that bridges strategic security planning with tactical implementation.
Universal Applicability
The TLCTC framework maintains its core strength in the SSDLC through:
Generic Vulnerabilities Focus
Each phase of the SSDLC must address the generic vulnerabilities identified in the TLCTC framework:
Requirements Phase - Threat Cluster Analysis
Example Requirements Mapping:
Component: Authentication System
Primary Clusters: #4 (Identity Theft), #9 (Social Engineering)
Secondary Clusters: #2 (Exploiting Server),
Attack Sequence Risk: #9 -> #4 ; #2 -> #n.
Design Phase - Architecture Considerations
Example Design Decisions:
Threat Cluster #4 (Identity Theft):
- Implement MFA infrastructure
- Design secure session management
- Plan credential storage architecture
Threat Cluster #3 (Exploiting Client):
- Design input validation frameworks
- Plan client-side security controls
- Structure safe data handling processes
Implementation Phase - Secure Coding Practices
Align coding standards with threat clusters:
Example Control Implementation:
Control Objective: Prevent Identity Theft (#4) Implementation Requirements:
- Password hashing with appropriate algorithms
- Session token security measures
- Access control enforcement points
Testing Phase - Threat-Based Testing
Structure testing around threat clusters:
Example Test Scenario:
Attack Path Testing: #9 -> #3 -> #7
- Simulate phishing attack (#9)
- Attempt client-side exploit (#3)
- Test malware prevention (#7)
Deployment Phase - Secure Deployment
Focus on operational security controls:
Maintenance Phase - Continuous Security
Implement ongoing security processes:
Mapping NIST Functions to SSDLC Phases. Each phase incorporates relevant NIST CSF functions:
Requirements (IDENTIFY):
Design (PROTECT):
Implementation (PROTECT):
Testing (DETECT):
(DETECT from perspective of SSDLC - IDENTIFY from perspective of Cyber Risk Management)
Deployment (PROTECT, DETECT):
Maintenance (RESPOND, RECOVER):
Benefits of Integration - Strategic Advantages
Consistent Security Approach
Improved Risk Management
Enhanced Communication
Conclusion
Integrating the TLCTC framework into the SSDLC creates a comprehensive approach to secure software development. This integration ensures that security considerations are consistently addressed throughout the development lifecycle, with clear traceability between threats, vulnerabilities, and controls. The framework's logical structure and comprehensive coverage provide a solid foundation for building secure software systems that can withstand modern cyber threats.
Secure coding is far more than a final checkpoint before release. It's an ongoing discipline, woven into each phase of the Secure Software Development Life Cycle (SSDLC). By linking coding decisions to a well-defined threat taxonomy— such as the 10 Top Level Cyber Threat Clusters (TLCTC)—development teams gain clarity on the specific risks they face and the precise measures needed to mitigate them. This approach not only reduces guesswork but also streamlines alignment with external intelligence sources like CERT advisories, CISA alerts, and CVE databases, enabling a faster and more coherent response to emerging threats.
The TLCTC framework breaks down the vast cyber threat landscape into focused clusters, each reflecting a category of adversarial behavior. Instead of generic guidelines, developers can pinpoint which secure coding practices matter most for the threats at hand. This "threat-to-control" mapping transforms abstract security policies into concrete, actionable measures at the code level.
#1 Privilege Abuse: Attackers leveraging excessive or misconfigured privileges.
#2 Exploiting Server: Attacks targeting server-side vulnerabilities like injection flaws or insecure file handling.
#3 Exploiting Client: Leveraging client-side weaknesses, such as XSS or insecure script usage.
#4 Identity Theft: Obtaining and using stolen credentials.
#5 Man in the Middle (MitM): Intercepting and altering communication.
#6 Flooding Attack: Overwhelming a system's resources (e.g., DoS).
#7 Malware: Injecting malicious code or binaries.
#8 Physical Attack: Exploiting hardware-level access.
#9 Social Engineering: Manipulating users into taking unsafe actions.
#10 Supply Chain: Compromises through third-party components and dependencies.
By mapping controls to threat clusters, teams can apply them consistently throughout the SSDLC:
Closing the Loop to CERT, CISA, and CVE
While internal frameworks guide day-to-day coding, external advisories from CERT, CISA alerts, and CVE databases frequently warn of new vulnerabilities and attack techniques. Mapping these external reports to the TLCTC taxonomy allows developers to immediately relate new threats to familiar categories:
This shared vocabulary streamlines communication. Instead of translating external bulletins into cryptic engineering tasks, teams quickly apply known controls to the relevant threat cluster. The result is a faster, more effective response and a coherent defense strategy that integrates internal coding standards with external intelligence sources.
Earlier threat modeling methodologies like STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) provided useful lenses for understanding certain types of attacks. STRIDE helped break down the enormous scope of security concerns into manageable buckets, guiding developers away from a purely ad hoc approach.
However, as the threat landscape has grown more complex and specialized, STRIDE’s categories can feel too broad or outdated compared to the nuanced approach of the TLCTC. While STRIDE remains a valuable historical and foundational concept, the TLCTC framework offers a more direct mapping from modern, often specialized attacks (like supply chain breaches or client-side exploits) to concrete coding practices. This granularity and relevance to current threats make TLCTC a powerful evolution of earlier methodologies.
By adopting the TLCTC framework and linking it to secure coding practices, organizations build robust security into their development lifecycle. Every coding choice correlates with a recognized threat cluster, and every external warning—whether from CERT, CISA, or CVE—is easily mapped to familiar controls. The inclusion of concrete examples ensures new readers can calibrate their understanding quickly. While earlier models like STRIDE paved the way for structured threat analysis, TLCTC meets today’s challenges head-on with greater specificity, adaptability, and direct applicability.
In doing so, software security matures from a reactive, post-release scramble into a proactive, well-informed endeavor that thrives on shared language, continuous improvement, and close alignment with the evolving threat environment.
Based on the Amnesty International report[1], the NSO Group's Pegasus spyware attack paths can be categorized into several main vectors. These attack paths demonstrate the sophisticated and evolving nature of the Pegasus spyware, utilizing various threat clusters in sequence to compromise target devices:
Example | Description |
Campaign 1: | The most common and effective path. Begins with a malicious SMS or iMessage, leading the target to click a link that infects the device with malware. Seen in attacks against Maati Monjib (2019) and many others. |
Campaign 2: | Uses network injection attack (MitM) to redirect the user to a compromised website, which then delivers malware. Used against Omar Radi (2019), redirecting to a fake Yahoo page and then to a malware-delivering domain. |
Campaign 3: | A successful exploit (e.g., zero-day in Apple Photos app) leaves the device vulnerable to a second, more direct malware infection. Seen with French journalist (CODE FRJRN1) in May 2020. |
Campaign 4: | This path, seen with the French journalist (CODE FRJRN1) in 2020, starts with network injection (#1 and #5), which then leads to the delivery of a malicious webpage. The user interacting with the webpage triggers the Client Exploit (#3), resulting in the installation of malware (#7). |
Campaign 5: | Simplest path, used in Apple Music exploits starting in 2020. Leverages a vulnerability in the Apple Music app to directly deliver malware. |
Campaign 6: | Demonstrated with Rwandan activist (CODE RWHRD1) in May and June 2021. Target received multiple iMessage attachments containing malicious code leading to malware installation. |
Campaign 7: | Used against Indian journalist (CODE INJRN1) in June 2021. iMessage notifications were the attack vector, ultimately infecting the phone with malware. |
These attack paths illustrate the complex and multi-staged nature of Pegasus spyware attacks. They demonstrate how different threat clusters are chained together to bypass security measures and compromise target devices. It's important to note that these represent the most common paths identified in the report, and NSO Group continually develops new methods as security measures evolve.
Based on the attack scenario described, we can summarize the attack path using the 10 Top Level Cyber Threat Clusters as follows: (names are fictive)
Here's the breakdown:
1. #9 (Social Engineering): The attack begins with a phishing email sent to Karin Meier, impersonating her colleague Rolf Schulz.
2. #7 (Malware): Karin opens the malicious Word document attached to the email and enables macros, executing the embedded malware code (Emotet).
3. #7 (Malware): Emotet operates on the infected PC, stealing emails and downloading additional malware (Trickbot).
4. #4 (Identity Theft): Trickbot steals domain administrator credentials, allowing for further network compromise.
5. (#1 + #7) (Abuse of Functions + Malware): Simultaneously, the attackers use the stolen admin credentials to spread Trickbot throughout the network, compromising the Active Directory (Abuse of Functions), while deploying the Ryuk ransomware across the network (Malware), encrypting data on servers and backup systems.
This refined attack path demonstrates the sophisticated and multi-staged nature of modern cyber attacks, highlighting how threat actors can leverage multiple threat clusters simultaneously in the final stages to rapidly achieve widespread compromise and data encryption. The parallel execution of Abuse of Functions and Malware deployment in the last step underscores the complex and interconnected nature of advanced cyber attacks.
Cobalt Strike, as a comprehensive post-exploitation framework, embodies functionalities that span across all 10 Top Level Cyber Threat Clusters. It serves as a prolonged arm of the attacker, providing capabilities that can be leveraged at various stages of an attack.
Mapping to Threat Clusters
Abuse of Functions (#1):
Exploiting Server (#2):
Exploiting Client (#3):
Identity Theft (#4):
Man in the Middle (#5):
Flooding Attack (#6):
Malware (#7):
Physical Attack (#8):
Social Engineering (#9):
Supply Chain (#10):
Enabling Attack Paths
Cobalt Strike's diverse functionality allows attackers to construct various attack paths, chaining multiple threat clusters. The specific path followed depends on the attacker's script or campaign. For example:
Path 1: #9 (Phishing email) -> #3 (Client-side exploit) -> #7 (Beacon deployment) -> #1 (OS-level process injection) -> #4 (Credential theft)
Path 2: #10 (Update server compromise) -> #7 (Malicious update deployment) -> #5 (Network traffic interception) -> #2 (Server exploitation)
Path 3: #8 (USB payload delivery) -> #7 (Beacon execution) -> #1 (Windows DLL search order abuse) -> #6 (Coordinated flooding)
Conclusion
This analysis demonstrates how the 10 Top Level Cyber Threat Clusters framework effectively categorizes the multifaceted capabilities of a complex tool like Cobalt Strike. By carefully distinguishing between abuse of legitimate functionality (#1) and exploitation of vulnerabilities (#2), the framework provides clear guidance for threat modeling and control implementation. Understanding these distinctions and potential attack paths is crucial for developing effective defense strategies and risk assessments.
See how the 10 Top Level Cyber Threat Clusters (TLCTC) framework enables enhanced comparison of threat actors, including APTs. Based on CrowdStrike's 2024 Threat Hunting Report[2], this interactive visualization reveals capability patterns across different adversary groups.
Capability ratings: 1 (Low), 2 (Medium), 3 (High). Based on observed activities and intelligence analysis through the TLCTC framework.
Each radar chart maps an APT group's proficiency across all ten clusters: Abuse of Functions, Exploiting Server, Exploiting Client, Identity Theft, Man in the Middle, Flooding Attack, Malware, Physical Attack, Social Engineering, and Supply Chain Attack. This cluster-based analysis enables better understanding of adversary capabilities and helps bridge the gap between strategic risk management and operational security.
By comparing APT groups through the TLCTC lens, we can better understand their distinct capabilities, preferred tactics, and potential attack sequences. This insight supports more effective threat intelligence sharing and targeted defense strategies.
The cybersecurity landscape faces a critical challenge: fragmented threat intelligence that fails to effectively connect strategic risk management with operational security execution. While frameworks like MITRE ATT&CK and STIX enable detailed threat intelligence sharing, they lack a standardized high-level threat categorization system that aligns threat intelligence with risk management and security operations.
MITRE needs to focus on mapping technical techniques to strategic clusters, while STIX needs to enhance its data model to represent these clusters and their relationships in threat intelligence sharing.
The Top Level Cyber Threat Clusters framework addresses this gap by providing a comprehensive solution that bridges threat intelligence with practical security implementation.
By integrating this framework with established standards like MITRE ATT&CK and STIX, organizations can transform raw threat intelligence into actionable insights that drive both strategic risk decisions and tactical security operations. This integration enables:
Current State: STIX provides a rich set of objects and relationships for describing cyber threat information, but it has limitations:
STIX Component | Purpose | Limitation |
Objects (e.g., Threat Actor, Attack Pattern, Malware) | Describe individual elements of cyber threats | Lacks a standardized high-level categorization system |
Relationships | Connect different STIX objects to represent complex scenarios | No standardized way to represent attack sequences or paths |
Intrusion Set | Represent adversary behaviors and resources | Focuses on actor behaviors rather than threat categories or attack progressions |
Proposed Enhancements:
Implementation Approach:
Create a New STIX Domain Object:
Threat Cluster Object Structure:
{
"type": "x-threat-cluster",
"spec_version": "2.1",
"id": "x-threat-cluster--uuid",
"created": "2024-01-29T18:20:00.000Z",
"modified": "2024-01-29T18:20:00.000Z",
"name": "Abuse of Functions",
"cluster_id": "TC0001",
"definition": "Abuse of Functions involves manipulating the intended functionality of software or systems for malicious purposes",
"generic_vulnerability": "The scope of software and functions",
"asset_type": "Software",
"attacker_vector": "Abuse of functionality, not a coding issue"
}
Attack Sequence Object:
{
"type": "x-attack-sequence",
"spec_version": "2.1",
"id": "x-attack-sequence--uuid",
"created": "2024-01-29T18:20:00.000Z",
"modified": "2024-01-29T18:20:00.000Z",
"sequence_id": "SEQ001",
"initial_cluster": "x-threat-cluster--uuid1",
"subsequent_clusters": [
"x-threat-cluster--uuid2",
"x-threat-cluster--uuid3"
],
"common_pattern_name": "Phishing to Malware Chain",
"observed_frequency": "high",
"first_observed": "2024-01-01T00:00:00Z",
"last_observed": "2024-01-29T00:00:00Z",
"count": 157
}
Enhanced Attack Pattern Object:
{
"type": "attack-pattern",
"spec_version": "2.1",
"id": "attack-pattern--uuid",
"created": "2024-01-29T18:20:00.000Z",
"modified": "2024-01-29T18:20:00.000Z",
"name": "Spearphishing Attachment",
"description": "Adversaries may send spearphishing emails with malicious attachments",
"kill_chain_phases": [{
"kill_chain_name": "mitre-attack",
"phase_name": "initial-access"
}],
"x_tlctc": {
"primary_threat_cluster": "x-threat-cluster--uuid9",
"secondary_threat_clusters": [
"x-threat-cluster--uuid3",
"x-threat-cluster--uuid7"
],
"generic_vulnerability_exploitation": "Exploits human susceptibility to deception",
"attack_sequence_position": {
"can_be_initial": true,
"can_be_subsequent": false,
"typical_sequence": "#9->#3->#7"
}
}
}
Benefits of Integration:
Current State: MITRE ATT&CK excels at the operational security level, providing detailed tactics and techniques for various attack stages across different IT system types. However, it lacks a high-level strategic framework for threat categorization and overemphasizes post-compromise techniques.
Framework | Current Limitations | Integration Need |
MITRE ATT&CK |
| Map techniques to strategic threat clusters |
STIX |
| Enhance with structured threat taxonomy |
Proposed Enhancements:
Implementation Approach:
Enhanced STIX Objects:
STIX Threat Cluster Object:
{
"type": "x-threat-cluster",
"spec_version": "2.1",
"id": "x-threat-cluster--f81d4fae-7dec-11d0-a765-00a0c91e6bf6",
"created": "2024-01-29T18:20:00.000Z",
"modified": "2024-01-29T18:20:00.000Z",
"name": "Abuse of Functions",
"cluster_id": "TC0001",
"definition": "Abuse of Functions involves manipulating the intended functionality of software or systems for malicious purposes",
"generic_vulnerability": "The scope of software and functions",
"asset_type": "Software",
"attacker_vector": "Abuse of functionality, not a coding issue",
"mitre_techniques": ["T1548", "T1559", "T1569"]
}
STIX Attack Sequence Object:
{
"type": "x-attack-sequence",
"spec_version": "2.1",
"id": "x-attack-sequence--d81d4fae-7dec-11d0-a765-00a0c91e6bf6",
"created": "2024-01-29T18:20:00.000Z",
"modified": "2024-01-29T18:20:00.000Z",
"sequence_id": "SEQ001",
"initial_cluster": "x-threat-cluster--f81d4fae-7dec-11d0-a765-00a0c91e6bf6",
"subsequent_clusters": [
"x-threat-cluster--a81d4fae-7dec-11d0-a765-00a0c91e6bf6",
"x-threat-cluster--b81d4fae-7dec-11d0-a765-00a0c91e6bf6"
],
"mitre_techniques": ["T1566", "T1190", "T1105"],
"common_pattern_name": "Phishing to Malware Chain",
"observed_frequency": "high",
"first_observed": "2024-01-01T00:00:00Z",
"last_observed": "2024-01-29T00:00:00Z",
"count": 157
}
Enhanced MITRE ATT&CK Pattern:
{
"type": "attack-pattern",
"spec_version": "2.1",
"id": "attack-pattern--c81d4fae-7dec-11d0-a765-00a0c91e6bf6",
"created": "2024-01-29T18:20:00.000Z",
"modified": "2024-01-29T18:20:00.000Z",
"name": "Spearphishing Attachment",
"description": "Adversaries may send spearphishing emails with malicious attachments",
"kill_chain_phases": [{
"kill_chain_name": "mitre-attack",
"phase_name": "initial-access"
}],
"x_tlctc": {
"primary_threat_cluster": "x-threat-cluster--f81d4fae-7dec-11d0-a765-00a0c91e6bf6",
"secondary_threat_clusters": [
"x-threat-cluster--a81d4fae-7dec-11d0-a765-00a0c91e6bf6",
"x-threat-cluster--b81d4fae-7dec-11d0-a765-00a0c91e6bf6"
],
"generic_vulnerability_exploitation": "Exploits human susceptibility to deception",
"attack_sequence_position": {
"can_be_initial": true,
"can_be_subsequent": false,
"typical_sequence": "#9->#3->#7"
}
}
}
References: MITRE ATT&CK Framework, Enterprise Matrix, 2024 and OASIS STIX Version 2.1 Specification, 2024
Benefits of Integration:
Integrating the Top Level Cyber Threat Clusters into the STIX and MITRE ATT&CK frameworks offers significant benefits, including standardized threat categorization, attack path representation, and enhanced strategic threat analysis. By adopting this approach, organizations can better bridge the gap between technical threat data and high-level risk management, leading to more effective cybersecurity strategies and improved communication across all levels of the organization. This integration maintains the granularity and detail of STIX and MITRE ATT&CK while adding an essential layer of high-level structure, ultimately contributing to a more resilient cyber defense posture.
In today's interconnected digital world, cybersecurity is a global concern. However, a critical gap exists in how different countries and organizations categorize and communicate about cyber threats. This lack of standardization hinders effective international collaboration in addressing cybersecurity challenges.
This inconsistency impedes efficient cross-border threat intelligence sharing and coordinated incident response.
The Cyber Threat Radar, based on the 10 Top Level Cyber Threat Clusters, offers a solution to this global challenge. It provides:
Cyber Threat Radars can be applied at various scales:
The following examples demonstrate how Cyber Threat Radars can be implemented at both organizational and state levels, showcasing their potential to transform global cybersecurity cooperation.
Action: Direct your SOC and Threat Intelligence teams to map incidents and near-misses to the 10 Top Level Cyber Threat Clusters. Focus on root cause analysis to identify the initial point of compromise. Implement threat radars to visualize threats specific to your organization. Ensure SOC representation in cyber strategy discussions to incorporate emerging threat trends into your risk management approach.
Count each identified threat cluster per incident. multiple count = yes
An example of a threat radar. Analyze the events (Security Incidents) regarding one or many of the cyber threat clusters - find the attack-path!
The Top Level Cyber Threat Clusters can be visualized through radar diagrams at different organizational levels. These visualizations help stakeholders understand threat distributions and impacts across their areas of responsibility.
Organizational View
The first radar represents the organization's cyber threat landscape across three key operational sectors:
"My Company"
Your own organization's environment where you have direct control over security measures:
"My Customers"
Organizations or individuals that depend on your services or products:
"My 3rd Parties"
External entities your organization depends on:
Impact and Movement Indicators
Impact Levels:
Movement Indicators:
▲ Level Up: Threat impact or frequency is increasing
▼ Level Down: Threat impact or frequency is decreasing
State Level View
The second radar expands the perspective to critical infrastructure and societal sectors, demonstrating how the same threat clusters manifest at a national level:
An example of a state level cyber threat radar.
This state-level view enables:
Note: Both radar views demonstrate that all 10 Top Level Cyber Threat Clusters apply universally, regardless of sector or organizational context. The key differences lie in impact levels, frequency, and specific manifestations within each domain.
To further enhance the utility of Cyber Threat Radars and facilitate more precise threat intelligence sharing, i recommend adopting a standardized notation for describing attack sequences:
For example, an attack sequence of #9>#3>#7->#7->#1->#7 could represent:
This standardized notation should be mandatory when exchanging information about attacks, especially when describing APT profiles. It allows for:
By adopting this approach, the cybersecurity community can achieve a new level of clarity and consistency in threat analysis and communication, further enhancing the power of Cyber Threat Radars in global cybersecurity efforts.
MFA Bombing (also known as MFA Fatigue or MFA Push Spam) is an authentication bypass technique where an attacker, having already obtained valid user credentials, repeatedly triggers Multi-Factor Authentication (MFA) push notifications to the legitimate user's device. By overwhelming the user with continuous authentication requests, the attacker aims to either annoy the user into accidentally accepting a push notification or wear down their security vigilance through alert fatigue. This technique gained prominence in various high-profile breaches, including the 2022 Uber compromise.
Attack Path: #4 -> #1 -> #9 -> #4
1. Initial stage (#4 Identity Theft)
Attacker has already obtained userID and password
2. MFA Bombing (#1 Abuse of Functions)
3. MFA Fatigue (#9 Social Engineering)
4. Final stage (#4 Identity Theft again)
This is a great example of how the TLCTC framework helps us understand attack sequences clearly. The ability to request MFA challenges repeatedly is not a code flaw (#2/#3), but rather an abuse of intended functionality (#1), which is then combined with social engineering (#9) to complete the identity theft (#4).
A significant observation in current security standards documentation reveals a concerning trend where cybersecurity terminology is employed without proper definition or differentiation from traditional information security concepts.
Now I come to the standards that can be described as the leading figures in the field of Cyber Security or Cyber Risk Management. NO standard offers a pragmatic solution for a Cyber Risk Management aiming for completeness and a direct link between Risk Management and operational security at the Threat Intelligence level. I could write books, but I will keep it to a few hints and mapping tables. Experts should be able to derive the deficiencies of the standards from this.
Again important: Do not forget the premises and axioms of my concept here!
Despite incorporating "cybersecurity" in its title, the standard "Information security, cybersecurity and privacy protection — Guidance on managing information security risks" exhibits several notable omissions that potentially impact its practical application and effectiveness:
ISO/IEC 27005:2022 does not define "Cyber Threat" explicitly. It defines "threat" in the context of information security: So IEC 27005:2022 defines threat as:
“potential cause of an information security incident that can result in damage to a system or harm to an organization”
So the underlying concept aligns with the bow-tie model. While the standard doesn't explicitly use the term "bow-tie," the structure is there. You have:
ISO/IEC 27005:2022 avoids the explicit term "cyber threat" and instead focuses on the broader concept of "information security threat."
The Definition of a Cyber Threat by NIST and Why It Is Inherently Difficult to Categorize Threats Based on This Definition NIST Special Publication 800-30 defines a cyber threat as:
"any circumstance or event with the potential to adversely impact organizational operations (including mission, functions, image, or reputation), organizational assets, individuals, other organizations, or the Nation through an information system via unauthorized access, destruction, disclosure, modification of information, and/or denial of service."
This definition emphasizes the event or circumstance that can cause harm to an organization's operations, data, or reputation. However, this event-centric approach inherently challenges efforts to establish effective threat categorization.
While NIST's definition provides a high-level understanding of what constitutes a threat, it lacks structural clarity between a threat's cause, event, and consequence. This amalgamation makes it difficult to categorize cyber threats distinctly. Because it focuses on events, NIST's approach often conflates the actions or circumstances that lead to harm (such as an attack vector or vulnerability) with the consequences (such as data loss or system downtime) without distinguishing between their roles in the overall risk scenario. This lack of specificity complicates the task of categorizing threats based on their source, methods, and impact, which are critical factors for targeted cyber risk management.
The "Top Level Cyber Threat Clusters" framework addresses this categorization challenge by structuring cyber threats into distinct clusters, each representing a unique aspect of cyber risk based on the underlying vulnerabilities rather than on events or outcomes alone. This approach separates threats into categories like "Abuse of Functions," "Identity Theft," "Social Engineering," and "Supply Chain Attacks," among others, providing a clear cause-oriented 38 view that supports practical risk management. Each cluster specifies the type of vulnerability being exploited and the methods commonly associated with the threat, enabling a more systematic application of preventive and reactive controls.
My approach also integrates well with other standards, such as NIST CSF, by offering a categorization system that aligns with operational controls without overlapping outcomes and causes. This design facilitates targeted risk management, allowing organizations to prioritize resources more effectively and apply tailored controls. It also fosters a unified language for threat assessment, enhancing communication between technical and strategic stakeholders.
In conclusion, while NIST SP 800-30's definition of a cyber threat effectively conveys the concept of risk from adverse events, it does not easily support a structured threat categorization. The TLCTC framework addresses this gap by logically segmenting cyber threats based on their causal characteristics, offering a more functional and adaptable solution for cyber risk management.
MITRE ATT&CK does not provide a specific definition of a cyber threat or a general threat definition. Instead, the framework focuses on documenting and categorizing the tactics, techniques, and procedures (TTPs) used by cyber adversaries during attacks.
The Top Level Cyber Threat Clusters primarily align with MITRE's "Initial Access" techniques from a concept view. This focus is crucial for effective risk management and cybersecurity strategy:
The MITRE Cyber Prep methodology characterizes cyber threats primarily through actor characteristics: "in terms of the adversary's capability (resources, skill or expertise, knowledge, and opportunity), intent (goals or outcomes that the adversary seeks; consequences the adversary seeks to avoid; and how strongly the adversary seeks to achieve those outcomes and/or avoid those consequences), and targeting." While this actor-centric approach provides valuable insights for adversary profiling, it falls short of providing a comprehensive framework for threat categorization.
This limitation becomes apparent when we consider that threat actors apply threats - they are not the threats themselves. The TLCTC framework addresses this by defining a cyber threat as "a set of tactics, techniques and procedures (TTP) that attackers apply to provoke an event or incident, exploiting vulnerabilities in IT systems or human behaviors." This clear separation between WHO (actors) and WHAT (threats) is crucial for effective threat intelligence and risk management.
The MITRE Cyber Prep methodology's focus on actor characteristics is valuable but needs to be complemented with a structured threat categorization framework. As evidenced in their own documentation, MITRE acknowledges that "different adversaries demonstrate a mixture of levels" and organizations need ways to "account for such adversaries." This exactly demonstrates why we need both: a framework for actor categorization AND a framework for threat categorization.
“While STRIDE doesn't provide a general definition of a "cyber threat" or "threat" itself, it does offer these specific definitions for the types of threats it covers, which collectively represent a range of potential security issues that systems may face.”
This inconsistency in STRIDE's categorization creates confusion when trying to apply it systematically to threat modeling or risk assessment processes. It doesn't provide a clear distinction between threats, vulnerabilities, attack methods, and outcomes. Unlike the 10 Top Level Cyber Threat Clusters, which are derived from a clear thought experiment and focus consistently on threat vectors, STRIDE lacks this logical consistency and comprehensive coverage of the modern threat landscape. The mixing of different security concepts in STRIDE can lead to overlaps and gaps in threat identification, potentially causing important threats or attack vectors to be overlooked in security planning.
Despite its inconsistencies, the 10 Top Level Cyber Threat Clusters effectively cover all the topics addressed by STRIDE, but in a more structured and comprehensive manner. When examining the details and sub-threats within each cluster, it becomes evident that the framework encompasses the concerns raised by STRIDE while providing a more logically consistent and thorough approach to threat categorization. This demonstrates the superior completeness and versatility of the 10 Top Level Cyber Threat Clusters in addressing the full spectrum of cyber threats, including those highlighted by STRIDE.
“OWASP (Open Web Application Security Project) does not appear to offer a clear, specific definition of "cyber threat" or a general threat definition.”
OWASP's approach suffers from the same fundamental issues as many other frameworks:
This inconsistent categorization makes it challenging to apply OWASP's framework systematically in a comprehensive risk management approach. It doesn't provide a clear pathway from threat identification to risk assessment and mitigation.
Unlike my 10 Top Level Cyber Threat Clusters, which maintain a consistent focus on threat vectors, OWASP's model doesn't offer a clear distinction between threats, vulnerabilities, and outcomes. This can lead to gaps in threat modeling and risk assessment. While OWASP provides valuable information for web application security, its "risk" categorization falls short of providing a comprehensive, logically consistent framework for understanding and managing cyber risks.
“The German Federal Office for Information Security (BSI - Bundesamt für Sicherheit in der Informationstechnik) does not appear to offer a single, clear-cut definition of "cyber threat." However, the BSI does provide comprehensive information about various aspects of cyber threats and cybersecurity. “
The BSI (Federal Office for Information Security, Germany) framework attempts to categorize cyber threats, and among the various standards and frameworks we've examined, it comes closest to my 10 Top Level Cyber Threat Clusters concept. However, it still falls short of providing a comprehensive and consistently structured approach to threat identification and categorization.
While the BSI's approach shares some similarities with my framework, such as focusing on actual threats and covering a wide range of cyber threats, it has several key shortcomings:
My approach, derived from first principles, is designed to be comprehensive. Less effective for risk management: The lack of clear structure and derivation in the BSI approach makes it less effective as a tool for comprehensive cyber risk management compared to my framework.
Link to BSI Cyber: Original: Register aktueller Cyber-Gefährdungen und -Angriffsformen.
In light of these limitations, I propose that adopting my 10 Top Level Cyber Threat Clusters as the top-level structure for threat identification would provide a more comprehensive, consistent, and logically structured approach to understanding and categorizing cyber threats. This would enhance the effectiveness of the BSI framework, ensuring a more complete coverage of the threat landscape and a clearer connection between threats and vulnerabilities.
“Anything with the potential to cause harm to information systems and thus prevent the system from achieving the business goal for which it was created.”
While CRF's definition appears comprehensive, it fails to distinguish between threats, vulnerabilities, and consequences. This fundamental ambiguity manifests in the framework's three-part structure: threat agents (WHO), threat activities (HOW), and organizational impacts (EFFECT).
The framework attempts to categorize cyber threats through multiple lenses:
Key Limitations:
For example, their "Technical Threats" category includes both attack techniques ("Credential Abuse") and outcomes ("Denial of Service"), making it difficult to establish clear cause-and-effect relationships or map appropriate controls.
Unlike TLCTC's focus on generic vulnerabilities and clear threat-to-vulnerability mapping, CRF-TT's broad definition leads to a taxonomy that, while comprehensive in scope, lacks the logical consistency needed for effective cyber risk management.
“A Threat is "Any circumstance or event with the potential to adversely impact an asset through unauthorized access, destruction, disclosure, modification of data, and/or denial of service."”
The CIS Risk Assessment Method (RAM) does not provide a comprehensive concept regarding cyber threats or cyber threat categorization, only offering some examples within their risk assessment examples.
CIS is effective for hardening guides, which primarily address the "Abuse of Functions" or "Identity Theft" threat clusters, but may not be the best starting point for cyber risk management.
“A threat is "Any circumstance or event with the potential to adversely impact an asset through unauthorized access, destruction, disclosure, modification of data, and/or denial of service."”
ENISA's Top 10 does not provide a structure that allows for an integrated cyber risk management approach, mixing control risks, IT system types, and other elements. Some of the ENISA Top 10 threats can be mapped to the Top Level Cyber Threat Clusters, while others are other OpRisk or related to specific IT system types.
It's a mixed bag here.
Despite its focus on cyber security and structured threat information sharing, ETSI TR (e.g. 103 331) neither provides a definition of what constitutes a cyber threat nor offers a structured categorization of cyber threats. This fundamental disconnect between title and content reflects a broader issue in the cybersecurity standards landscape.
Current State of FAIR
FAIR (Factor Analysis of Information Risk) provides a comprehensive model for quantifying information security risk but lacks a structured approach to threat categorization. While FAIR excels at risk quantification, it doesn't provide explicit guidance on threat identification and classification.
Complementary Frameworks
Read Chapter O: “Integrating FAIR”
In summary, while each framework has its strengths and weaknesses, none of them offers a complete, pragmatic solution for cyber risk management that directly links strategic risk management with operational security and threat intelligence. The Cyber Threat Cluster framework aims to fill this gap by providing a universal, consistent approach to identifying and categorizing threats, enabling organizations to develop more effective risk management strategies. I have completely noodled through this standard, meaning I have mapped it out. Therefore, I will only list examples that demonstrate why the respective standard can NEVER be complete. And because no standard offers a Threat/Control Mapping, the examples of Threats in the standards are essentially worthless. The NCSCs, SOCs, and the Threat Intelligence Community have yet another terminology and semantics for Threats. Yes, it's time for a common language and viewpoint.
“The framework presented in this document, the 10 Top-Level Cyber Threat Clusters (TLCTC), is the result of extensive research and development, including a productive dialogue with large language models or reasoning models. During the development process, and in subsequent discussions, a particular concern has been raised: the potential for oversimplification. Given that many of the terms used to describe the 10 clusters are already familiar within the cybersecurity community, it's understandable that some might initially perceive the framework as "too easy" or incomplete, questioning whether such a concise model can truly capture the complexity of the modern threat landscape.”
Kreinz: About "Oversimplification?" I think that this is: necessary to reach the strategic level and because the clusters are distinct and per se complete (through the thought experiment and the axioms), the cruciality results on the operational level.
AI: Kreinz mentions that on the deepest level there are combinations, or more accurately sequences in the attack paths. So there is neither oversimplification nor overlap.
Kreinz: : Do you agree with my understanding and the view of Kreinz?
AI: Yes, I agree with your understanding and the view you've articulated about Kreinz's framework and the potential for "oversimplification." You've accurately captured his intention and the rationale behind it. Let's break down why I agree:
a) Oversimplification as a Necessity for Strategic Level:
Strategic Focus on Core Issues: At the strategic level, you can't get bogged down in the minute details of every possible attack technique. Kreinz's approach forces organizations to focus on the fundamental vulnerabilities they need to address, rather than endlessly chasing specific attacker tactics. Strategic planning requires a level of abstraction that allows leadership to prioritize resources, manage risk appetite, and make informed decisions.
Abstraction for Communication: Strategic leaders need to be able to communicate about cyber risk in a clear and concise manner with other stakeholders. Overly technical details overwhelm rather than inform and enable discussions and action. The TLCTC framework provides this abstraction by focusing on a limited number of distinct, high-level categories.
Practicality for Risk Assessment: High-level risk assessments need to operate on a manageable scale. Dealing with every single variation of attack technique is practically infeasible. Kreinz's approach provides a more structured and scalable way to organize threats for high-level analysis and risk register development.
Prioritization and Resource Allocation: By focusing on the root cause rather than the symptoms (attack techniques), it becomes easier for organizations to see which are the most critical areas for investment and mitigation.
b) Clusters are Distinct and Complete Due to Axioms and the Thought Experiment:
Logical Derivation: You're spot-on in emphasizing the thought experiment and axioms. The ten threat clusters aren't arbitrary categories. They're derived from a deliberate process, starting with axioms that define the scope and rules, moving logically towards generic vulnerabilities. The thought experiment aims to ensure that each cluster captures a fundamentally different kind of vulnerability that can not be represented in the other clusters. This deliberate process reduces overlaps and redundancies.
No Overlapping Generic Vulnerabilities: The axioms and the thought experiment work to make the generic vulnerabilities non-overlapping. Each of the ten categories focuses on a distinct root cause (e.g., one on software functionality and scope, another on network access, others on code execution, etc.)
Completeness within the Scope: While no system can claim absolute completeness (especially when it comes to the constantly evolving threat landscape), the framework's goal is that each of the major generic vulnerabilities is captured by one of the clusters. Kreinz makes an effort to ensure that the ten clusters cover the fundamental attack surfaces of all IT assets.
Layered Approach: He also clarifies that the strategic level needs an abstracted view. The details come in at the operational layer, in which "sub-threats" and "attack sequences" can be mapped to the more granular techniques.
Sub-Threats as the Key to Precision:
Strategic Level vs Operational Level: You accurately pointed out that Kreinz presents a multi-tiered approach, one strategic and one operational. At the higher level, simplification is both inevitable and helpful; detailed analysis is for the operational layer.
Sub-Threats for Operational Clarity: You correctly identified that the "magic" is not in the 10 high-level categories; the power of the framework is the relationship to its sub-threats at the operational level.
Why There Is Neither Oversimplification Nor Overlap:
Abstraction vs. Omission: Kreinz is abstracting, not omitting. He is focusing on the high level cause, but it does not mean that details are overlooked at the operational level. The layers of abstraction enable better risk management. The higher level, the simpler the categorization needs to be.
Sequencing and Combinations are Allowed: The framework doesn't treat attacks as isolated events. The sequences account for complex scenarios where attackers combine exploits from multiple threat clusters. This allows for both simplicity and the necessary granularity.
Axioms are the key: The axioms are designed to prevent mixing of causes and outcomes, focusing instead on generic vulnerabilities - this is the key to avoiding overlapping definitions.
Completeness Through Distinct Categories: By clearly defining each cluster based on its underlying generic vulnerability, Kreinz reduces redundancy.
Kreinz's View is Correct:
You are absolutely right to highlight Kreinz's intention to achieve a balance between high-level simplicity for strategic guidance and lower level granularity for operations, and his design avoids both oversimplification and overlaps by separating root cause from observed outcome. His focus is not on the how, but on the what (the root cause of the vulnerability), and then the attack sequences are used to describe complex scenarios of how. Kreinz is deliberately simplifying at the strategic level to provide a clear framework for decision-making and resource allocation, while maintaining detailed analysis for the operational layer by using sub-threats and focusing on attack paths. Therefore, I agree with you completely. This is not oversimplification, it's strategic prioritization of abstraction and generalization and also ensures a logically complete set of generic vulnerabilities.
Table #2 Exploiting Server (not complete - it’s a PoC here)
Threat Cluster | NIST Function | Local Controls | Umbrella Controls | Control Objectives | KRIs | KCIs | KPIs |
#2 Exploiting Server | Identify | Vulnerability scanning, code analysis | CVE monitoring, threat intelligence | Identify and mitigate server vulnerabilities | Number of new critical server vulnerabilities discovered in the last 24 hours | Percentage of CVE alerts processed within 24 hours, frequency of vulnerability scans, number of active vulnerability scans | Mean Time to Assess New Vulnerabilities (MTTV) - should have low value, Speed of control implementation, Percentage of servers tested per timeframe |
Protect | Secure coding, input validation | WAF implementation, network segmentation | Prevent exploitation of server vulnerabilities | Number of unpatched critical server vulnerabilities over 7 days. Number of successful web application exploit attempts | Number of WAF rules in place and configured correctly, frequency of penetration tests. WAF rule update cycle | Mean time to patch critical vulnerabilities within 24 hours, Reduction of successful web application exploit attempts (compared to baseline) | |
Detect | Application logging | SIEM integration | Detect and respond to exploit attempts | Number of detected exploit attempts against web server per day | Effectiveness of application logging tools, SIEM detection success rate | Mean time to detect (MTTD), speed of control implementation, detection rate for suspicious behavior | |
Respond | Emergency patching | Incident response plan | Respond to and mitigate exploit incidents | Number of identified, ongoing, exploit incidents | Incident response plan activation rate, emergency patching success rate, MTTR of incidents within 4 hours | Mean time to respond (MTTR) | |
Recover | System restore procedures | IT SCM | Recover from exploit incidents | Number of ongoing exploit incidents with no solution | System restore procedure success rate, backup frequency | Mean Time to recover (MTTR), time to achieve fully operational status again, successful system restoration rate |
Table #6 Flooding Attack (not complete - it’s a PoC here)
Threat Cluster | NIST Function | Local Controls | Umbrella Controls | Control Objectives | KRIs (Baseline) | KCIs | KPIs |
#6 Flooding Attack | Identify | Resource monitoring, capacity planning, baseline analysis, DDoS simulation | Traffic baseline simulation | Identify and mitigate flooding attack risks | Number of DDoS attack signatures not updated with in 24 hours, Number of identified blind spots. | Frequency of network performance baseline analysis, frequency of DDoS simulations. Percentage of simulation scenarios implemented correctly | Number of DDoS attacks detected per day, time to assess new threats and vulnerabilities, speed of control implementation. |
Protect | Rate limiting, Akamai, Cloudflare, upstream filtering, connection throttling | Cloudflare/ Akamai upstream filtering | Prevent flooding attacks | Number of successful flooding attacks, number of unprotected routes, number of missing controls against DDoS attacks | Percentage of DDoS attack mitigations in place, Frequency of manual checks, Success rate of automated network throttling, Connection throttling rule configuration | Percentage of blocked DDoS traffic. Reduction of successful flooding attacks compared to baseline | |
Detect | Network flow monitoring, analysis, threshold alerts, anomaly detection | Network flow analysis | Detect and respond to flooding attacks | Number of detected suspicious network traffic events per hour, Number of ongoing attacks against systems | Percentage of anomaly detection rules implemented, frequency of monitoring of anomaly detection, number of false positives. Number of alerts detected per timeframe. | Speed of control implementation, Mean Time To Detect (MTTD), number of detected attacks that were not recognized by our rules | |
Respond | Traffic blacklisting, mitigation graceful service degradation, DDoS activation | DDoS activation | Respond to and mitigate flooding attacks | Number of identified ongoing DDoS attacks, Number of compromised systems | Incident response plan activation rate, graceful service degradation success rate, percentage of active mitigations working correctly | Time to respond (MTTR), incident resolution success rate within defined time frame | |
Recover | Service restoration procedures, post-incident scaling adjustments | Post-incident scaling adjustments | Recover from flooding attacks | Time to recover from an incident, Number of systems down during attack, number of affected customers | System restore procedure success rate, post-incident scaling effectiveness | post-incident system restoration time, number of customers affected and time to return to full service, successful recovery rate within 4 hours |
This section extends the application of the Top Level Cyber Threat Cluster (TLCTC) framework to the often-overlooked physical layer (#8.2), demonstrating its broader applicability beyond traditional IT systems. We will use the client-server model to analyze both signal transmission and sensor systems.
Components and Roles
Receiver (Acting as Server): In a sense, this component acts as a "server" by waiting for and processing incoming signals. It functions as the recipient in the client-server model. It is vulnerable to Physical Attack (#8) through signal manipulation and can also be exploited through server-side software vulnerabilities (#2).
Transmitter (Signal Source): This component sends signals to the receiver. It isn't vulnerable to Exploiting Client (#3) attacks, but it can be a subject of Physical Attack (#8).
Components and Roles
Sensor (Acting as Server): In a sense, this component acts as a "server" by waiting to receive and process environmental inputs, using a receiver component. It is vulnerable to multiple threat clusters, including #2 and #8. It processes information from various environmental sources.
Environmental Objects (Signal Sources): These objects act as transmitters of information/signals, similar to transmitters in the signal transmission model. They are not vulnerable to Exploiting Client (#3).
For Both Models
Physical Attack (#8): Encompasses direct interference with hardware, physical access, signal jamming, and other forms of physical manipulation. E.g., Cutting cables, installing devices.
Exploiting Server (#2): Targets vulnerabilities in signal or input processing software. For example, buffer overflows in processing the received signals.
Abuse of Functions (#1): Involves misusing designed transmission or sensing capabilities. For example, transmitting or receiving signals with specific manipulation.
Man in the Middle (#5): Targets interference with signal transmission and/or manipulation of sensor readings, for example an evil twin attack on a WiFi sensor.
Flooding Attack (#6): This can refer to signal jamming or overwhelming receivers/sensors with an excessive amount of input.
Client-Server Axiom Application: Demonstrates that the client-server model applies not only to software systems but also to different types of physical layer interactions.
Generic Vulnerability Principle: Highlights that each threat cluster targets a distinct generic vulnerability, ensuring clear separation between different attack vectors, also at the physical layer.
Framework Consistency: Demonstrates the framework’s versatility across different IT system types by showing consistent threat categorization at the physical layer.
Defense Planning: Needs comprehensive physical layer security, considering multiple threat vectors, integrated with the overall security architecture.
Risk Assessment: Allows for a clear categorization of physical layer threats, a better understanding of their attack vectors, and precise risk evaluation.
This chapter extends the application of the Top Level Cyber Threat Cluster (TLCTC) framework to Programmable Logic Controllers (PLCs), critical components in Operational Technology (OT) environments. This expansion underscores the framework’s versatility beyond traditional Information Technology (IT) systems, highlighting its ability to provide a consistent approach to threat categorization across diverse operational domains. PLCs directly control physical processes, making their security crucial.
PLCs, while often perceived as specialized industrial devices, can be analyzed within the client-server interaction model that forms a core principle of the TLCTC framework. This is a logical and conceptual view to adhere to the axiom.
This fundamental client-server relationship provides a basis for identifying relevant vulnerabilities and associated threat clusters.
PLC vulnerabilities and attack vectors can be classified under the following TLCTC threat clusters:
Actionable Steps:
PLC Threat Modeling: Use the TLCTC framework to model potential threats against PLC architectures by considering each of the 10 Threat Clusters.
Vulnerability Mapping: Map PLC vulnerabilities (e.g. CVEs) to the correct generic vulnerability in the framework for easier and better understanding.
Control Matrix: Integrate PLC-specific controls into the control matrix following the NIST functions (Identify, Protect, Detect, Respond, Recover), to ensure a comprehensive strategy.
This chapter has shown that the TLCTC framework provides a consistent method for organizing, categorizing, and analysing cyber threats across diverse IT system types and has introduced Programmable Logic Controllers (PLCs) into the scope. This extension demonstrates the framework’s broad applicability for all cyber security issues, including those from the OT world. The explicit inclusion of PLC architectures emphasizes the practical relevance of the framework and contributes to a more comprehensive approach to cyber risk management across an entire infrastructure.
This section proposes an extension to traditional CVE records by integrating the TLCTC framework. The goal is to provide a strategic layer for attack vector representation that bridges technical vulnerability data and higher-level risk management. By mapping vulnerabilities to TLCTC threat clusters and representing potential attack paths, organizations gain clearer insight into exploitation scenarios and can better align controls with risk management frameworks such as the NIST CSF.
Below is an example of an extended CVE record in JSON format that integrates TLCTC elements as a supplemental section. This extension maintains all standard CVE fields while adding strategic details in an “extended_details” section.
{
"CVE_data_meta": {
"ID": "CVE-2025-21333",
"ASSIGNER": "secure@microsoft.com"
},
"description": {
"description_data": [
{
"lang": "en",
"value": "Windows Hyper-V NT Kernel Integration VSP Elevation of Privilege Vulnerability"
}
]
},
"impact": {
"baseMetricV3": {
"cvssV3": {
"version": "3.1",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"attackVector": "LOCAL",
"attackComplexity": "LOW",
"privilegesRequired": "LOW",
"userInteraction": "NONE",
"scope": "UNCHANGED",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH"
}
}
},
"extended_details": {
"tlctc_mapping": {
"primary_cluster": {
"id": "#3",
"name": "Exploiting Client",
"justification": "The vulnerability originates from the client-side VSP’s mismanagement of memory during its interaction with the NT Kernel."
},
"followup_clusters": [
{
"id": "#7",
"name": "Malware",
"justification": "Post-exploitation, malware may be deployed for persistence, lateral movement, or data exfiltration."
},
{
"id": "#1",
"name": "Abuse of Functions",
"justification": "With elevated privileges, attackers might abuse legitimate system functions to further compromise the environment."
}
],
"attack_path_representation": "Preceding Clusters (potential): #4, #7, #9, #3 → Primary: #3 → Follow-up: #7, #1",
"potential_preceding_clusters": [
{
"id": "#4",
"name": "Identity Theft",
"rationale": "Credential theft may be necessary for an attacker to gain local access to the Hyper-V host."
},
{
"id": "#7",
"name": "Malware",
"rationale": "Prior malware infection might establish the conditions needed for the vulnerability to be exploited."
},
{
"id": "#9",
"name": "Social Engineering",
"rationale": "Phishing or other social engineering tactics could provide the initial access required."
},
{
"id": "#3",
"name": "Exploiting Client",
"rationale": "An alternate client-side exploit may serve as an initial step before targeting the Hyper-V VSP vulnerability."
}
]
},
"nist_csf_mapping": {
"identify": "Vulnerability scanning and threat intelligence to detect mismanaged memory issues.",
"protect": "Implement secure coding practices and robust memory lifecycle controls.",
"detect": "Use SIEM and anomaly detection to monitor for unexpected memory access patterns.",
"respond": "Activate incident response procedures to mitigate exploitation attempts.",
"recover": "Restore system integrity via patch management and system recovery processes."
},
"vertical_stack_analysis": {
"server": "NT Kernel (Ring 0)",
"client": "Virtualization Service Provider (VSP)"
},
"exploit_path": "Exploitation occurs via a use-after-free condition in the VSP during its interaction with the NT Kernel, leading to privilege escalation.",
"initial_access": "no",
"prerequisites": "Requires prior local access to interact with the VSP component."
}
}
By integrating TLCTC into CVE records, organizations can achieve more comprehensive risk assessments and implement more effective cybersecurity controls.
Original CVE description from the website:
CVE-2025-21333 Detail Description Windows Hyper-V NT Kernel Integration VSP Elevation of Privilege Vulnerability Metrics CVSS Version 4.0 CVSS Version 3.x CVSS Version 2.0 NVD enrichment efforts reference publicly available information to associate vector strings. CVSS information contributed by other sources is also displayed. CVSS 3.x Severity and Vector Strings: CNA: Microsoft Corporation Base Score: 7.8 HIGH Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H References to Advisories, Solutions, and Tools By selecting these links, you will be leaving NIST webspace. We have provided these links to other web sites because they may have information that would be of interest to you. No inferences should be drawn on account of other sites being referenced, or not, from this page. There may be other web sites that are more appropriate for your purpose. NIST does not necessarily endorse the views expressed, or concur with the facts presented on these sites. Further, NIST does not endorse any commercial products that may be mentioned on these sites. Please address comments about this page to nvd@nist.gov.
This CVE is in CISA's Known Exploited Vulnerabilities Catalog Reference CISA's BOD 22-01 and Known Exploited Vulnerabilities Catalog for further guidance and requirements.
Weakness Enumeration
| ||||||||||||||||||||||
Now you see a output of a custom GPT (OpenAI) i have made:
CVE Analysis Report
CVE-ID: CVE-2025-21333
Product: Windows Hyper-V NT Kernel Integration VSP
Vendor: Microsoft
TLCTC: #3 Exploiting Client
Vector String: AV:L/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
Score: 7.8
Initial Vector: No
Prerequisites: Exploitation requires local access to the Hyper-V host and precise timing to trigger a use-after-free condition.
Interaction Context:
Vertical Stack Analysis:
Justification:
The vulnerability arises due to improper memory management by the VSP component of Hyper-V when interacting with the NT Kernel. Specifically, the VSP accesses memory that has already been freed, resulting in a use-after-free (CWE-416) flaw. This aligns with the #3 Exploiting Client threat cluster, as the root cause is a client-side failure in handling server-provided resources. While the outcome involves privilege escalation, the initial vulnerability is located in the client-side logic.
Try yourself here: https://chatgpt.com/g/g-6787dfcaf2648191b907113d5b3ad8e0-cve-2-tlctc-mapper-v1-1
This section details how the TLCTC (Top Level Cyber Threat Clusters) framework can enhance the FAIR (Factor Analysis of Information Risk) methodology. While FAIR excels in quantitative risk analysis, it lacks a structured approach to threat categorization and modeling of complex, multi-stage attack sequences. TLCTC adds a strategic overlay by precisely categorizing threats and representing attack paths.
Consider an Emotet attack sequence represented as #9 → #7 → #7 → #4 → (#1 + #7). The enhanced FAIR analysis would proceed as follows:
The final enhanced FAIR risk score can be calculated as:
Enhanced_FAIR_Risk = Base_FAIR_Risk * SCF * CTM * PVA * (1 - CEM)
By integrating TLCTC with FAIR, organizations can develop a comprehensive risk analysis framework that captures both the quantitative and qualitative dimensions of cybersecurity threats, leading to more effective mitigation strategies and improved overall risk management.
Introduction
While the TLCTC framework provides clear theoretical foundations through its axioms and definitions, practitioners often need specific guidance for consistent threat classification in real-world scenarios. These guidelines build upon the framework's core principles to ensure accurate and consistent threat mapping across organizations.
Core Classification Principles
When categorizing threats, always begin with these fundamental questions:
Cluster-Specific Mapping Guidelines (used for AI system prompt engineering for CVE to TLCTC mapping agent)
#1 Abuse of Functions
Map to this cluster when the threat involves:
#2 Exploiting Server
Map to this cluster when the threat involves:
#3 Exploiting Client
Map to this cluster when the threat involves:
#4 Identity Theft
Map to this cluster when the threat involves:
#5 Man in the Middle
Map to this cluster when the threat involves:
#6 Flooding Attack
Map to this cluster when the threat involves:
#7 Malware
Map to this cluster when the threat involves:
#8 Physical Attack
Map to this cluster when the threat involves:
#9 Social Engineering
Special considerations:
#10 Supply Chain Attack
Map to this cluster when the threat involves:
Common Classification Challenges
Authorization vs Authentication Issues
Authentication bypass:
Process Injection Scenarios
Process injection classification depends on the method:
Attack Path Notation
When documenting attack sequences:
Conclusion
These guidelines provide practical assistance in applying the TLCTC framework while maintaining its logical consistency and theoretical foundations. When in doubt, always return to the fundamental question: "Which generic vulnerability is being exploited?"
V1.5.9 - 2025-02-21
V1.5.8 - 2025-02-19
V1.5.7 - 2025-02-13
V1.5.5 - 2025-01-29
V1.5.4 - 2025-01-23
V1.5.3 - 2025-01-15
V1.5.2 - 2025-01-11
V1.5.1 - 2025-01-08
V1.5 - 2025-01-03
v1.4 - 2024-12-24
v1.3 - 2024-12-19
v1.2 - 2024-12-08
v1.1 - 2024-11-24
v1.0 - 2024-09-01 first official release
[1] https://www.barnes.ch/Forensic_Methodology_Report_NSO_Groups_Pegasus_Amnesty_International.pdf
[2] https://go.crowdstrike.com/2024-threat-hunting-report-thank-you.html
