NOMOS: Compiling Written Policies into Statically Verified Tool-Call Gates for LLM Agents
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Abstract (arXiv)
Tool-using LLM agents violate the policies they are deployed to enforce, often silently. Prior defenses hand-write rules, query an LLM verifier per action, or compile policies through heavyweight formal machinery. Naive compilation fails: extracted rules block the tool satisfying their own precondition, or read arguments their tool lacks. NOMOS, a four-pass compiler, turns a natural-language policy into a deterministic tool-call gate; static verification with tool-schema-level checks alone (no prover, solver, or LLM) repairs or rejects 37% (airline) and 13% (retail) of candidates, without which most shipped rules are inoperable. Replaying compiled rules over undefended transcripts flags bindings that refuse legitimate work (a development binding refused 95.9% of task-passing calls); no evaluation binding is flagged. On τ2-bench the gate cuts violations of reference-encoded clauses among state-changing calls from 66.3% to 2.6% (airline) and 30.8% to 6.9% (retail), raising airline task success significantly for 2 ≤ k ≤ 4; a 26B on-premise compilation is not significantly worse than hand-written or frontier-compiled rules. Unlike AgentDojo's shipped defenses, it reaches a zero attack success rate (ASR) on banking, where nine attack families collapse onto three structural rules. On the other three suites its ASR is at most 3.6%, from goals with no tool call to govern and one write admitted by a binding weaker than its clause; a second agent model, Llama-3.3-70B, reproduces the effect on both benchmarks. Decisions take microseconds without an LLM call, at a domain-dependent benign-utility cost; compilation runs on-premise on open-weight gemma-4-26B.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 35 references that are in the map. First posted on arXiv 8 Oct 2026.
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References · 27
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- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
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- 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agentscited by 196
- 2024SecAlign: Defending Against Prompt Injection with Preference Optimizationcited by 179
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- 2024IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systemscited by 118
- 2024GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoningcited by 113
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- 2025Towards Enforcing Company Policy Adherence in Agentic Workflowscited by 17
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- 2026Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agentscited by 8
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- 2026PolicyGuard: A Dialogue-Grounded Sub-Agent Verifier for Policy Adherence in LLM Agentscited by 5
- 2026Autoformalization of Agent Instructions into Policy-as-Codecited by 1
- 2026Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agentscited by 1
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