Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularity

Computer Science > Cryptography and Security [Submitted on 31 Jul 2026] Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularity Ramya Varunsegar 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 investigates the impact of predicate representation granularity on plan validity, cost, and fidelity. Utilizing a pipeline where a Large Language Model (LLM) performs translation and the Fast Downward engine performs deterministic reasoning, the study compares the full nine-category AALM against a reduced five-category scheme derived empirically from Atomic Red Team (ART) execution evidence. Results from a sixteen-technique corpus demonstrate that plan validity and cost are largely insensitive to granularity, with 81.3% identical outcomes across both schemes. The findings suggest that higher granularity primarily enhances the internal structural resolution of a plan's justification rather than the viability of the generated attack chain itself. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.00143 [cs.CR]   (or arXiv:2608.00143v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.00143 Focus to learn more Submission history From: Ramya Varunsegar [view email] [v1] Fri, 31 Jul 2026 15:35:06 UTC (70 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)