Correlated failures of AI approval monitors
Original title: Correlated Failures of AI Approval Monitors
Counting evidence sources blocked 0.9% of correct payments versus 7.4% for the best vote-counting rule, at equal safety.
A paper treats AI approval monitors as fail-prone systems that can fail together when they share a base model or a poisoned input. Twelve small open-weights monitors were measured on payment approval under two pre-registered protocols. Without pre-registration, the authors also tested Claude Opus 5.5, GPT-6 Astra, Gemini 4 Argon and DeepSeek-V3.2. Reliable fraud approvals came through one evidence source, and no aggregation rule over the twelve monitors survived every measured attack until a non-persuadable rule engine was added.
Unconfirmed: Tests of the three closed frontier models and DeepSeek-V3.2 were not pre-registered.
- Small models and DeepSeek obeyed instructions planted in their data.
- The three closed models ignored planted notes in all but one case.
- Opus also resisted persuasion tailored to it.
- Adding a rule engine restored a safe and live rule on the measured attacks.
From the original article
1 min read at arxiv.orgBefore an AI agent takes a consequential action, such as paying, messaging or changing code, several monitors check it. Their verdicts are combined by aggregating scores or by counting approvals, whatever inputs the monitors share. Monitors that share a base model or a poisoned input, however, may fail together.
By Luca Zanolini · arxiv.org
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