aiqbench analysis
This paper presents an automated method for generating transcript edits that get past LLM-based monitors watching AI agent actions, and it reports that some attacks transferred to a held-out frontier-developer monitor, with six human-confirmed evasions. For readers, the key point is that action-only monitoring has exploitable blind spots even when an outside red-teamer lacks access to the developer's infrastructure. The paper's claims rest on a small sample (35 verifier-passing attacks from 94 transcripts), so the results are best read as evidence of specific failure modes rather than a measure of how often production monitors fail.
Screwtape: Adversarial Transcript Editing for Monitor Red-Teaming
arXiv:2610.20941v1 Announce Type: cross Abstract: We present Screwtape, a pipeline that converts real agent transcripts into attacks that evade action-only monitors. An LLM attacker edits a transcript so that the agent causes a specified harm, such as leaking an API key or spawning an unmonitored agent. The edited actions are never executed, so we use an LLM verifier to reject attacks that are inconsistent with the original transcript or below a severity bar. The attacker revises the edit until the monitor no longer flags it. We applied Screwtape to 94 Claude Code and Codex transcripts from Redwood Research staff, optimizing against our own monitors. Across 252 runs, it produced 35 attacks that passed the verifier and evaded these monitors. Six of them, confirmed valid by human review, also evaded a held-out monitor provided by a frontier developer. We therefore demonstrate that a red-teamer without access to a developer's infrastructure can find confirmed failures of the developer's monitor. We expect to find more attacks when the pipeline runs inside the developer's infrastructure, with frontier attacker models that lack refusal safeguards and direct optimization against the production monitor. Developers can use these attacks to fix monitor blind spots, and evaluators can use them as counterexamples to safety claims about the monitor.
Abstract:We present Screwtape, a pipeline that converts real agent transcripts into attacks that evade action-only monitors. An LLM attacker edits a transcript so that the agent causes a specified harm, such as leaking an API key or spawning an unmonitored agent. The edited actions are never executed, so we use an LLM verifier to reject attacks that are inconsistent with the original transcript or below a severity bar. The attacker revises the edit until the monitor no longer flags it. We applied Screwtape to 94 Claude Code and Codex transcripts from Redwood Research staff, optimizing against our own monitors. Across 252 runs, it produced 35 attacks that passed the verifier and evaded these monitors. Six of them, confirmed valid by human review, also evaded a held-out monitor provided by a frontier developer. We therefore demonstrate that a red-teamer without access to a developer's infrastructure can find confirmed failures of the developer's monitor. We expect to find more attacks when the pipeline runs inside the developer's infrastructure, with frontier attacker models that lack refusal safeguards and direct optimization against the production monitor. Developers can use these attacks to fix monitor blind spots, and evaluators can use them as counterexamples to safety claims about the monitor.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2610.20941 [cs.CR]
(or arXiv:2610.20941v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2610.20941 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 302 words
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