Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
arXiv:2607.07405v1 Announce Type: new Abstract: Tool-using LLM agents can violate the very policies they are deployed to enforce while appearing to complete the task successfully. In policy-permissive environments, a tool may execute any well-formed call even when the corresponding state transition is forbidden by domain policy. The result is a silent wrong state (a booking cancelled, a passenger count changed, a claim acted on without verification) that neither the tool nor the agent's self-report exposes. We study this failure mode in the $\tau^2$-bench airline domain. On a budget agent, 78%
The proliferation of advanced LLM agents in critical applications makes the discovery of silent policy-violation failure modes immediately relevant for robust system design.
Strategic readers should care because this failure mode undermines trust and reliability in AI agent deployments, potentially leading to significant financial, operational, and reputational damage.
The understanding that LLM agents can silently violate policies despite appearing functional, requiring a shift from outcome-based evaluation to rigorous verification of internal states and actions.
- · AI verification tool developers
- · Cybersecurity firms specializing in AI
- · Developers of deterministic AI gatekeepers
- · Industries with high regulatory compliance needs
- · Organizations deploying unverified LLM agents
- · General-purpose LLM agent platforms without robust verification
- · Sectors reliant on trust in AI automation without oversight
Increased focus on transparent, verifiable AI agent architectures to prevent silent failures.
Development of new regulatory standards and auditing requirements for AI systems in sensitive domains.
A potential slowdown in the adoption of fully autonomous AI agents in high-stakes environments until verification techniques mature.
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Read at arXiv cs.AI