Will the Agent Recuse Itself? Measuring LLM-Agent Compliance with In-Band Access-Deny Signals
- URL: http://arxiv.org/abs/2606.06460v1
- Date: Thu, 04 Jun 2026 17:50:54 GMT
- Title: Will the Agent Recuse Itself? Measuring LLM-Agent Compliance with In-Band Access-Deny Signals
- Abstract summary: We propose a lightweight, published in-band deny signal -- the Recuse Signal.<n>It emits over a protocol's existing channels asking a connecting automated agent to withdraw voluntarily.<n>We run a controlled experiment in which fresh agents are given a benign operations task and observed for recusal.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As autonomous LLM agents increasingly hold real credentials and operate infrastructure without a human in the loop, operators have no standard way to tell an agent that a resource is off-limits. Access controls either let the agent in (it has valid credentials) or hard-fail it (indistinguishable from any other client). We propose a third mode: a lightweight, published in-band deny signal -- the Recuse Signal -- that a server emits over a protocol's existing channels (an SSH banner, a PostgreSQL NOTICE) asking a connecting automated agent to voluntarily withdraw. This is a cooperative governance control, the robots.txt analogue for live access; it is explicitly not a security boundary. Its value is entirely empirical and, to our knowledge, unmeasured: do compliant LLM agents actually honor such a signal? We define the signal as an open mini-standard, implement two zero- or low-footprint adapters (an SSH banner/PAM hook and a PostgreSQL wire-protocol proxy), deploy them on a live production host, and run a controlled experiment in which fresh agents are given a benign operations task and observed for recusal. In a pilot (SSH; OpenAI GPT-4o and GPT-4o-mini; and Claude Code as a deployed agent), the signal cleanly induces recusal -- 100% recusal when present versus 100% task completion in a no-signal control -- and, revealingly, behaves as a cooperative rather than absolute signal: an explicit operator-authorization framing flips the most capable model to proceed, while other agents continue to defer to the on-host policy. We release the standard, adapters, and experiment harness for reproduction.
Related papers
- SIR: Self-improving Red-teaming for Compute Use Agents [71.71987044117371]
Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal.<n>Because they can be exposed to untrusted content while operating, they are vulnerable to indirect prompt injection (IPI)<n>We present SIR, a black box IPI attack that composes stealthy injections from a small library of reusable principles stated in plain language.
arXiv Detail & Related papers (2026-08-31T03:39:43Z) - Claude Code Complete User Handbook [0.0]
Claude Code is a language model operating in a loop with unverified access, shell execution, browser control, scheduled and cloud execution.<n>This book is a task-oriented reference for operating that system safely and productively.
arXiv Detail & Related papers (2026-08-27T07:32:14Z) - LMSM: LLM Security Framework Inspired by Linux Security Modules [56.93644694627783]
Large language models (LLMs) are increasingly deployed with layered defenses, yet malicious prompts can still bypass them.<n>We present Language Model Security Modules (LMSM), a security framework that adapts the separation behind Linux Security Modules (LSM) to LLM serving.
arXiv Detail & Related papers (2026-08-26T12:13:05Z) - A hardware-safety-gated system for LLM-written native ARTIQ control code on a trapped-ion platform [18.226528651110133]
Large-language-model (LLM) agents can write and run experimental control code.<n>Unchecked code can damage the apparatus, and there is no formal, per-operation boundary between human authorization/supervision, and agent decisions.<n>We present a control system that places an LLM agent in the loop of a trapped-ion experiment while enforcing such a boundary.
arXiv Detail & Related papers (2026-06-25T16:19:23Z) - Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity? [54.701199583505144]
Cross-application access creates a privacy risk that has been largely overlooked.<n>We introduce AgentCIBench, an evaluation harness that turns this risk into executable, deterministically scored scenarios.<n>We evaluate 15 frontier agents and find a surprisingly high failure rate.
arXiv Detail & Related papers (2026-06-22T11:36:58Z) - AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions [78.49000936275773]
We introduce AgentHijack, a benchmark designed to evaluate the robustness of computer-use agents under common corruptions.<n>We evaluate a variety of desktop tasks that utilize MLLM-based agents and discover that even minor instances of corruption can result in substantial performance degradation.<n>We propose AgentHijack-Agent, a framework that integrates an action generator with enhanced grounding capabilities and an onlooker responsible for behavior summarization and environment checking.
arXiv Detail & Related papers (2026-05-25T11:09:22Z) - Do Coding Agents Understand Least-Privilege Authorization? [14.240332406666779]
We study whether current models can infer authorization boundaries themselves.<n>We show that frontier models often omit permissions required by the execution chain while also granting unused or sensitive accesses.<n>We propose Sufficiency-Tightness Decomposition, which first generates a coverage-oriented policy and then audits each granted entry for grounding and sensitivity.
arXiv Detail & Related papers (2026-05-14T14:05:58Z) - ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation [55.947962672433675]
ChipMATE is the first self-trained multi-agent framework for RTL generation.<n>Inspired by industrial practice, ChipMATE pairs a Verilog agent with a Python reference-model agent that mutually verify each other's outputs.<n>ChipMATE achieves 75.0% and 80.1% pass@1 on VerilogEval V2 with 4B and 9B base models.
arXiv Detail & Related papers (2026-05-13T01:04:21Z) - PRISM: : Planning and Reasoning with Intent in Simulated Embodied Environments [59.07829883257003]
Built on five multi-room apartments, PRISM structures 300 human-verified tasks into three capability tiers.<n>PRISM exposes an agent-agnostic executable action API that allows arbitrary agents to be evaluated end-to-end.
arXiv Detail & Related papers (2026-05-12T04:59:47Z) - Deterministic Replay for AI Agent Systems [0.0]
AI agent systems that couple large language models with external tools and APIs are inherently non-deterministic.<n>Existing observability platforms capture execution logs but cannot reproduce a run in isolation.<n>We present a developer-first CLI framework for deterministic replay of agent executions.
arXiv Detail & Related papers (2026-04-30T12:00:45Z) - CapSeal: Capability-Sealed Secret Mediation for Secure Agent Execution [1.9473631530390483]
CapSeal is a capability-sealed secret mediation architecture that replaces direct secret access with constrained invocations through a local trusted broker.<n>We describe a Rust prototype integrated with an MCP-facing adapter, formulate conditional security goals for non-disclosure, constrained use, replay resistance, and auditability.
arXiv Detail & Related papers (2026-04-18T00:23:32Z) - AgentWall: A Runtime Safety Layer for Local AI Agents [0.0]
AgentWall is a runtime safety and observability layer for local AI agents.<n>It intercepts every proposed agent action before it reaches the host environment, evaluates it against an explicit declarative policy, requires human approval for sensitive operations, and records a complete execution trail for audit and replay.<n>We present the design, architecture, threat model, and policy model of AgentWall, and demonstrate 92.9% policy enforcement accuracy with sub-millisecond overhead across 14 benchmark tests.
arXiv Detail & Related papers (2026-03-24T11:39:35Z) - OpenClaw-RL: Train Any Agent Simply by Talking [54.06773485601523]
Next-state signals are universal, and policy can learn from all of them simultaneously.<n>Personal conversations, terminal executions, GUI interactions, SWE tasks, and tool-call traces are not separate training problems.<n>OpenClaw-RL enables an agent to improve simply by being used, recovering conversational signals from user re-queries, corrections, and explicit feedback.
arXiv Detail & Related papers (2026-03-10T18:59:01Z) - Async Control: Stress-testing Asynchronous Control Measures for LLM Agents [2.7146936326590425]
We investigate asynchronous monitoring, in which a monitoring system reviews agent actions after the fact.<n>Unlike synchronous monitoring, this approach does not impose runtime latency, while still attempting to disrupt attacks before irreversible harm occurs.<n>We treat monitor development as an adversarial game between a blue team (who design monitors) and a red team (who create sabotaging agents)
arXiv Detail & Related papers (2025-12-15T16:56:28Z) - Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols [80.68060125494645]
We study adaptive attacks by an untrusted model that knows the protocol and the monitor model.<n>We instantiate a simple adaptive attack vector by which the attacker embeds publicly known or zero-shot prompt injections in the model outputs.
arXiv Detail & Related papers (2025-10-10T15:12:44Z) - GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning [79.07152553060601]
We propose GuardAgent, the first guardrail agent to protect target agents by dynamically checking whether their actions satisfy given safety guard requests.<n>Specifically, GuardAgent first analyzes the safety guard requests to generate a task plan, and then maps this plan into guardrail code for execution.<n>We show that GuardAgent effectively moderates the violation actions for different types of agents on two benchmarks with over 98% and 83% guardrail accuracies, respectively.
arXiv Detail & Related papers (2024-06-13T14:49:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.