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AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers propose AgentSnare, a defense framework that uses deceptive environment observations to mislead and disrupt autonomous LLM pentesting agents.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 1 October 2026
- Collected by OneBench
- 30 Jul 2026, 09:36 UK
Stored source excerpt
arXiv:2607.26998v1 Announce Type: cross Abstract: Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools.…
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