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arXiv Computation and Language By Ruoyu Wang, Heng Zhao, Renjie Wu, Tianhang Zheng, Mengnan Zhao, Wanyu Lin, Kui Ren

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Web Penetration Agents

Read the original on arXiv Computation and Language →

AgentSnare is a trajectory‑adaptive deception system that expands a decoy environment while autonomous web‑penetration agents explore. It uses an LLM‑driven decoy constructor and a virtual kernel to generate and materialize decoy artifacts that persist across interactions, enabling long‑horizon exploration. In experiments on 15 CVE‑Bench web applications and four attacker models, AgentSnare delays 49.9% of tool calls, diverts 57.2% of post‑entry actions into decoys, and defuses 89.5% of completion reports, preventing any real target exploitation at pass@3 across 60 attacker‑CVE pairs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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