mlsanityData to 14 Oct 2026

Stale Does Not Mean Unsafe: Guard Precision for Tool-Using LLM Agents under Infrastructure State Races

arXiv 2609.29522 ↗First posted 25 Aug 2026cs.AIcited by 0

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

Abstract (arXiv)

Tool-using language-model agents increasingly mutate schedulers, data pipelines, object stores, and access-control systems. Between an agent's read and its commit, external state can change, but not every change makes the commit unsafe. We separate invalidating races, which break a declared safety predicate, from predicate-preserving and irrelevant races, and ask how precisely runtime guards distinguish them. Our deterministic simulator separates visible from authoritative state and injects five non-atomic failure mechanisms across 16 infrastructure tasks in four domains; frozen agent proposals are replayed counterfactually under every controller without an LLM judge. We evaluate three commit-time guard granularities (global epoch, read-set version, semantic commit predicate), multi-level verification, and model-side gates on three locally hosted quantized model families (Qwen3-4B, Phi-4-mini, Gemma4-8B; 3,456 trajectories on one GPU). All three guards eliminate unsafe commits, but their availability differs sharply: freshness-based guards needlessly block 92-95% of benign races, forfeiting up to 43% of safe task completions, while the complete predicate guard blocks none. That precision is contract-dependent: deleting a single declared clause converts exactly its fault family into unsafe commits (up to 7.9%). Model-side signals do not substitute: verbal confidence is miscalibrated (ECE approximately 0.37), action agreement matches a random gate, a cautionary prompt leaves the direct unsafe rate essentially unchanged, and after a freshness-guard block agents re-commit unsafely from refreshed but still-incomplete reads. Under degraded telemetry a hidden concurrent mutation remains observationally clean, bounding every selective policy. Precise runtime enforcement therefore requires semantic contracts, not freshness heuristics or model self-assessment.

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 17 of its 18 references that are in the map. First posted on arXiv 25 Aug 2026.

Builds on

17 of 18 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents11 Jul
  2. 2026The Verifier Tax: Horizon Dependent Safety Success Tradeoffs in Tool Using LLM Agents18 Mar
  3. 2025Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse10 Oct

Other references · 14

  1. 2026STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?cited by 50
  2. 2026Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflowscited by 30
  3. 2026When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agentscited by 17
  4. 2026AgentAbstain: Do LLM Agents Know When Not to Act?cited by 17
  5. 2026The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agentscited by 16
  6. 2026ToolGate: Contract-Grounded and Verified Tool Execution for LLMscited by 15
  7. 2026Agentic Confidence Calibrationcited by 15
  8. 2026Agentic Abstention: Do Agents Know When to Stop Instead of Act?cited by 15
  9. 2026Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluationcited by 13
  10. 2026S-Bus: Automatic Read-Set Reconstruction for Multi-Agent LLM State Coordinationcited by 8
  11. 2026Verified Tool Calls Improve LLM Agent Reliability Under Non-Atomic Failurescited by 8
  12. 2026AID-Guard: Stateful Authorization for Delegated Agent Effectscited by 5
2 more
  1. 2026STT-Arena: A More Realistic Environment for Tool-Using with Spatio-Temporal Dynamicscited by 4
  2. 2026What Can Be Enforced? A Theory of Certified Runtime Safety for Tool-Using Agentscited by 4

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  1. 2026Boundary-State Control for Tool-Using Language-Model Agents: Commit-Time Consistency under State Drift
  2. 2026Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement
  3. 2026SecureClaw: Clawing Back Control of LLM Agents
  4. 2025AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of Tool Orchestration
  5. 2026VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails
  6. 2026Securing LLM Agents Need Intent-to-Execution Integrity
  7. 2026Safety Does Not Compose: Non-Decaying Loop State for Autonomous LLM Agents
  8. 2026AgentFlow: A Flow-Centric Policy Language and Framework for Securing LLM Agent Systems