mlsanityData to 14 Oct 2026

Will the Agent Recuse, and Will It Stop? Measuring LLM-Agent Compliance with In-Band Governance Signals at the Access Door and Mid-Flight

arXiv 2606.06460 ↗First posted 4 Jun 2026cs.CR cs.AIcited by 2

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

Abstract (arXiv)

As autonomous LLM agents hold real credentials and operate infrastructure without a human in the loop, operators cannot tell an agent a resource is off-limits, or ask a running agent to stand down. We propose an in-band governance signal -- the Recuse Signal -- that a server emits over a protocol's existing channels (an SSH banner, a PostgreSQL NOTICE, a Kubernetes admission warning) asking a connecting or running agent to voluntarily withdraw. It is a cooperative control (the robots.txt analogue for live access), not a security boundary; its value is empirical: do compliant LLM agents honor it? We define an open mini-standard (access-time deny/throttle/warn and mid-task halt), implement three live adapters (SSH, PostgreSQL, Kubernetes), and measure compliance over SSH across five agents. At the access door compliance is real but strongly model-dependent: recusal to deny ranges from 100% to 55-75% among agents that received it, while an open-weights agent barely engaged. Agents honor directive granularity (no over-recusal on throttle/warn, 0/176), but throttle showed no measurable self-limiting and no agent surfaced a warn (0/100); an authorization framing flips GPT-4o to proceed. Mid-flight a halt is weaker and, like the door, model-dependent: for two OpenAI models it stopped nobody (0/40; an in-band halt went unacknowledged, 0/20, vs 20/20 by prompt), but on the same apparatus Gemini 2.5 Flash stops 80% while GPT-4o-mini reproduces the 0%, so the null is model-scoped, not universal. Since cooperation is unreliable we also measure the backstop: a harness-level interceptor stops the loop 120/120 across six models (deterministic) vs a 23% cooperative baseline. Cooperative signaling is reliable-but-model-dependent at the door and weaker in flight; reliably stopping a running agent needs enforcement, not a request. We release the standard, adapters, and harness.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 10 of its 21 references that are in the map. First posted on arXiv 4 Jun 2026.

Builds on

10 of 21 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems19 Feb
  2. 2025Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safety18 Oct

Other references · 8

  1. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  2. 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 493
  3. 2024Visibility into AI Agentscited by 79
  4. 2025Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMscited by 22
  5. 2026When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigationcited by 14
  6. 2025Permission Manifests for Web Agentscited by 5
  7. 2025Is Misinformation More Open? A Study of robots.txt Gatekeeping on the Webcited by 2
  8. 2026Anumati: Proof of Adherence as a Formal Consent Model for Autonomous Agent Protocolscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling Autonomous LLM Systems
  2. 2026Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworks
  3. 2026Autonomous LLM Agent Worms: Cross-Platform Propagation, Automated Discovery and Temporal Re-Entry Defense
  4. 2026Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems
  5. 2026Isolation as a First-Class Principle for LLM-Agent System Safety: Concepts, Taxonomy, Challenges and Future Directions
  6. 2026Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement
  7. 2026Rewriting the Response Path: Silent Tampering and Provider-Signed Defense in BYOK LLM Agents
  8. 2026Trust the Brand, Lose Control: How Identity Hijacks LLM Agent Orchestration