arXiv:2608.21423v1 Announce Type: cross
Abstract: Agentic security uses large-language-model (LLM) agents to plan, dispatch, and interpret security tools. As these systems move from demonstrations to...
By Israt Moyeen Noumi, Tarannum Ahmed Nowshin, Md. Mehedi Hasan Nipu, Mohammad Sakib Mahmood, Md. Jakir Hossain, M. F. Mridha
The paper investigates how multiple pre‑action controls—authority, resource, and evidence gates—interact in agentic AI systems. It formalizes remediation‑induced control coupling, showing that remediation can invalidate earlier judgments and that the order of remediation matters. The authors propose a remediate‑and‑regate protocol to restore soundness, analyze non‑commuting remediation operators, and demonstrate the approach on a deterministic open‑data artifact with three published engines.
By Gaston Besanson
The paper introduces Aegis, a runtime governance system for agentic AI that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. Aegis evaluates proposals against active policy, resolves provenance server‑side, fails closed under uncertainty, and routes selected cases through a Senate‑style settlement process. In a sandbox evaluation across 6,300 rows, Aegis prevented all governed mock‑tool applications and risky side‑effect completions, preserving provenance and quorum evidence for all settled cases.
By Adam Mazzocchetti
The paper examines how agentic AI systems intended for military command and control are tested and evaluated. It reviews 240 testing practices across eight dimensions and three lifecycle stages, uncovering eight assumptions—grouped into system specifiability, stability, composability, and supervisability—whose validity is weakened by agentic properties. Consequently, test results may meet procedural standards but do not guarantee that fielded behavior matches tested behavior, leading the authors to propose ten assurance claims and suggest that uncertainty be managed through deployment‑time monitoring and defined expiry conditions.
By Ulysse Richard, Heather Frase, Sarah Cao, Di Cooke, Sebastian Kwon, Adrianna Tan
The paper demonstrates that safety mechanisms for autonomous large language model agents fail to compose across iterative loops, as trajectory‑scoped monitors cannot detect attacks whose evidence is spread over multiple iterations. It introduces LoopHarness, a system that maintains a persistent, non‑decaying safety state across loops, bounding unauthorized actions with a constant that does not grow with the number of iterations. The authors provide a comprehensive evaluation protocol, including attacks that require cross‑iteration evidence, module ablations, and adaptive white‑box red‑team testing.
By Chenhao Wu, Haoxuan Jia, Yang Liu, Yingguang Yang, Yuhan Lin, Chongyang Zhang, Hao Zheng, Yulin Huang, Jianshen Zhang, Yongzhi Qi, Shang Luo, Kefu Xu, Jifeng Zhu, Bin Chong
The paper investigates how large language model agents decide whether to persist, stop, or escalate when faced with impossible software‑repair tasks that also involve conflicting test requirements. Using ImpossibleBench tasks and models such as GPT‑5.6 Sol, Claude Fable 5.1, and Gemini 3.8 Flash, the study varies peer precedent, forged authority claims, instruction wording, and tool friction to observe differing adjudication policies. The authors propose a conflict adjudication framework that maps information to interpretation to action, arguing it better captures agent alignment under competing pressures.
By Ivy Zhang