MITRE ATT&CK: State of the Art and Way Forward
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Abstract (arXiv)
MITRE ATT&CK is a comprehensive framework of adversary tactics, techniques and procedures based on real-world observations. It has been used as a foundation for threat modelling in different sectors, such as government, academia and industry. To the best of our knowledge, no previous work has been devoted to the comprehensive collection, study and investigation of the current state of the art leveraging the MITRE ATT&CK framework. We select and inspect more than fifty major research contributions, while conducting a detailed analysis of their methodology and objectives in relation to the MITRE ATT&CK framework. We provide a categorization of the identified papers according to different criteria such as use cases, application scenarios, adopted methodologies and the use of additional data. Finally, we discuss open issues and future research directions involving not only the MITRE ATT&CK framework but also the fields of risk analysis and cyber-threat intelligence at large.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Aug 2023.
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Citing papers per year: 2024: 1; 2025: 5; 2026: 6.
12 papers.
- 2024AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMscited by 3
- 2025Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunitiescited by 2
- 2025An Evaluation Framework for Network IDS/IPS Datasets: Leveraging MITRE ATT&CK and Industry Relevance Metricscited by 1
- 2026Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Frameworkcited by 1
- 2026A Systematic Review of Algorithmic Red Teaming Methodologies for Assurance and Security of AI Applicationscited by 1
- 2025Towards Integrated Alignmentcited by 0
- 2025Amazon Nova AI Challenge -- Trusted AI: Advancing secure, AI-assisted software developmentcited by 0
- 2025Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skillscited by 0
- 2026Stateful Online Monitoring Catches Distributed Agent Attackscited by 0
- 2026GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attackscited by 0
- 2026Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularitycited by 0
- 2026Learning Intrusion Response Strategies for OT Systemscited by 0
- 2026Learning Intrusion Response Strategies for OT Systems9 Sep 2026
- 2026Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularity31 Jul 2026
- 2026GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks4 Jun 2026
- 2026Stateful Online Monitoring Catches Distributed Agent Attacks29 May 2026
- 2026A Systematic Review of Algorithmic Red Teaming Methodologies for Assurance and Security of AI Applications24 Feb 2026
- 2026Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework12 Jan 2026
- 2025An Evaluation Framework for Network IDS/IPS Datasets: Leveraging MITRE ATT&CK and Industry Relevance Metrics16 Nov 2025
- 2025Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities8 Sep 2025
- 2025Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skills27 Aug 2025
- 2025Amazon Nova AI Challenge -- Trusted AI: Advancing secure, AI-assisted software development13 Aug 2025
- 2025Towards Integrated Alignment8 Aug 2025
- 2024AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs1 Nov 2024