Supporting AI/ML Security Workers through an Adversarial Techniques, Tools, and Common Knowledge (AI/ML ATT&CK) Framework

Ahmet Okutan; Mehdi Mirakhorli; Mohamad Fazelnia

Supporting AI/ML Security Workers through an Adversarial Techniques, Tools, and Common Knowledge (AI/ML ATT&CK) Framework

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arxiv 2211.05075 v1 pith:3CK6Q3WA submitted 2022-11-09 cs.CR

Mohamad Fazelnia , Ahmet Okutan , Mehdi Mirakhorli This is my paper · ORCID

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keywords securityworkersadversarialcommonframeworkknowledgesupportingtechniques

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This paper focuses on supporting AI/ML Security Workers -- professionals involved in the development and deployment of secure AI-enabled software systems. It presents AI/ML Adversarial Techniques, Tools, and Common Knowledge (AI/ML ATT&CK) framework to enable AI/ML Security Workers intuitively to explore offensive and defensive tactics.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models
cs.CR 2026-08 conditional novelty 5.0 of 10

A PRISMA-based survey of 85 papers shows agentic LLM security research is attack-heavy and perception-focused, leaving action-layer and code-execution risks understudied.

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