mlsanityData to 7 Oct 2026

The Gate Is Only as Honest as Its Contracts: ContractGuard for the Contract Layer of Risk-Aware Causal Gating

arXiv 2606.18550 ↗First posted 17 Jun 2026cs.CRcited by 2

On the map: LLM agents and tool use → Agent threats, guardrails and governance

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Abstract (arXiv)

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).

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. First posted on arXiv 17 Jun 2026.

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  1. 2026Influence Is Not Authority: When Causal Guardrail Signals Make Legitimate Tool Use Look Like an Attack in Tool-Using LLM Agents
  2. 2026AttriGuard: Defeating Indirect Prompt Injection in LLM Agents via Causal Attribution of Tool Invocations
  3. 2026Capability Minimization as a Safety Primitive: Risk-Aware Causal Gating for Least-Privilege LLM Agents
  4. 2026Causality Laundering: Denial-Feedback Leakage in Tool-Calling LLM Agents
  5. 2026ROPE: Routed Origin Policy Enforcement against Indirect Prompt Injection
  6. 2026ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents
  7. 2026SafeClawBench: Separating Semantic, Audit-Evidence, and Sandbox Harm in Tool-Using LLM Agents
  8. 2026APEX: Active Protection at Execution Boundaries for LLM Agents