Same Operation, Different Permission: Evaluating Whether Guardrails Honor Authorization
On the map: LLM agents and tool use → Agent threats, guardrails and governance
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Abstract (arXiv)
As language-model agents write code, move money and change records on behalf of users, deciding whether an action is authorized has become a central safety problem. Guardrails are often placed in front of these actions, yet few evaluations ask whether their decisions follow authorization: blocking unauthorized actions while still allowing the same actions under valid permission. We introduce bench, a benchmark that contrasts authorization evidence for the same operation in its language track. It combines expert-annotated natural-language scenarios with executed and verified operations, plus externally sourced native-policy requests. bench checks each decision for correctness, usability and resulting effect, and tracks each request across guardrail changes. Our experiments show that many blocked operations stem from failed reviews, not correct denials. Repairing the output format restores usable decisions but changes which authorized requests are honored, and adding a second reviewer removes some errors while rejecting valid authorization. Guardrails should therefore be evaluated on both the unauthorized actions they stop and the authorized actions they let through.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 12 of its 38 references that are in the map. First posted on arXiv 12 Oct 2026.
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12 of 38 references.
Closest ancestors · 7
Same direction, newest first
- 2026AutoCedar: An Agentic Framework for Verifier-Guided Access Control Policy Synthesis4 Jul
- 2025Control Tax: The Price of Keeping AI in Check5 Jun
- 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents24 Mar
- 2025SafeArena: Evaluating the Safety of Autonomous Web Agents6 Mar
- 2024ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents9 Oct
- 2024GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning13 Jun
- 2023Identifying the Risks of LM Agents with an LM-Emulated Sandbox25 Sep
Other references · 5
- 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
- 2023AI Control: Improving Safety Despite Intentional Subversioncited by 229
- 2020Metamorphic Testing: A New Approach for Generating Next Test Casescited by 75
- 2026Natural Language Access Control (NLAC): From Help Desk Requests to Structured Policiescited by 1
- 2026FraudBench: Stress-Testing Policy-Grounded Banking Agents Against Adaptive Fraudcited by 1
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Close in wording, no citation either way
- 2026The Guard That Cried Wolf: How Scary Words Make Agent Guardrails Refuse Legitimate Actions
- 2026Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring
- 2026Influence Is Not Authority: When Causal Guardrail Signals Make Legitimate Tool Use Look Like an Attack in Tool-Using LLM Agents
- 2026SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing
- 2026AuthGateBench: Benchmarking LLMs as Authorization Gate
- 2025No Free Lunch with Guardrails
- 2026TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories
- 2025LlamaFirewall: An open source guardrail system for building secure AI agents