Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularity
Automated attack chain generation is critical for modern cybersecurity, yet manual construction fails to scale as adversary behaviors expand. While classical AI planning using PDDL offers a formal method to automate this process, it relies on the accurate translation of techniques into symbolic predicates. Current state-of-the-art systems like AURORA employ a nine-category Attack Action Linking Model (AALM), but the necessity of this specific granularity remains unvalidated. This work investigat
Record details
Published: 31 July 2026
Source: arXiv red teaming query
Category: Research
Topics: Safety & alignment · Finance, VC & PE
Retrieved: 4 August 2026
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ethics.ai (31 July 2026), “Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularity,” evidence record 16173, https://ethics.ai/record/16173 (originally published by arXiv red teaming query).
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