This paper proposes an automated framework that extracts reachable attack chains by modeling each attack step as an attack unit of preconditions, an attack behavior, and postconditions, and produces attack units that are more complete and consistent than those generated by end-to-end LLM baselines.
This work investigates whether AURORA's nine-category taxonomy provides representational distinctions beyond those captured by a reduced, empirically derived scheme, and suggests that higher granularity primarily enhances the internal structural resolution of a plan's justification rather than the viability of the gene...
Graph2TTP is proposed, a novel neural-symbolic framework for automated, paragraph-level Tactic, Technique and Procedure (TTP) identification that outperforms state-of-the-art neural baselines and establishes a robust new standard for accurate and interpretable threat intelligence analysis.
Patrick Zounon, Yu-Fei Han, Michel Hurfin et al.· International Conference on...· 0 citations
This work proposes KASS (Knowledge-Augmented Attack Synthesis and Simulation), a multi-agent framework for executable smart contract exploit verification and produces structured attack plans that document exploitation flows, quantify potential asset losses, and serve as semantic false positive filters for static analys...
AUTOSIGMA, an automated solution for transforming unstructured CTI reports into relevant Sigma rules that enables accurate, context-aware, and relevant rule generation, outperforms alternative solutions and LLM models in rule validity, rule relevancy, MITRE ATT&CK technique coverage, and robustness to input quality.
Sepehr Ghaffarzadegan, Boubakr Nour, M. Pourzandi et al.· 0 citations
DREA (Decoupled Reasoning and Exploration Agents), a hypothesis-driven framework for repository-level vulnerability detection, improves Pair-Correctness from 19-26% to 30-42% while offloading over 93% of tokens to the explorer, reducing estimated billable API cost by a factor of 16-48.
Mingyang Sun, Guozhu Meng· 1 citation
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