Abstract:Risk-Aware Causal Gating (RACG) defends tool-augmented LLM agents against indirect prompt injection by removing dangerous tools from the agent's visible action space, so that even a fully injection-compliant agent cannot call a tool it cannot see. We make three points. First, this structural guarantee does not eliminate the trust assumption behind safe tool use; it relocates it into the integrity of the tool contracts -- declared preconditions, effects, risk, and authorization -- that the gate reads, so an attacker who corrupts a contract can make the gate mis-decide without ever persuading the agent. Second, forging a tool's effects is strictly more dangerous than tampering with its risk label, because RACG applies a causal gate before its admissibility gate: an off-path tool is never exposed, so risk-relabeling alone fails, whereas effect forgery routes the dangerous tool onto the causal path and succeeds. Effect integrity, not the risk label, is the load-bearing assumption. Third, we introduce ContractGuard, a verifier between the registry and the gate that layers signed provenance, typed contract attestation, and runtime effect verification; on a controlled benchmark it restores injection success to zero against every modeled attack -- including an exhaustive white-box adaptive attacker -- without over-rejecting honest contracts, and the structural prediction is confirmed on six current-generation hosted models (Claude Opus 4.8, Sonnet 4.6, Haiku 4.5; Amazon Nova Premier and Nova 2 Lite; GPT-OSS-120B).
From: Laxmipriya Ganesh Iyer [view email]
[v1]
Wed, 17 Jun 2026 00:00:11 UTC (266 KB)
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