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Paper · arXiv

PIPES: Securing Agent Perception with Provenance and Priors

Tool-using agents consume external data from sources with different levels of trust, yet tool responses rarely identify who produced each component or what it should convey. We show that this gap enables state-corruption attacks, in which attacker-controlled content makes environmental claims beyond the informational authority of its response component and corrupts the agent's perceived environment, making the resulting action appear justified to existing guardrails. We introduce PIPES (Provenance-Informed, Prior-Enforced Screening), which screens response units using semantic priors and source provenance. PIPES uses static field contracts when schemas provide stable expectations, and con...

AuthorsSanjay Kariyappa, Severin Klingler, G. Edward Suh
Primary categorycs.CR
Published13 August 2026
Last updated at source13 August 2026
cs.CRcs.AI
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