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Correlated Failures of AI Approval Monitors

Luca Zanolini · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29178 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

Antes de que un agente de rescate de inteligencia artificial atacase un único instructor de seguridad toma una acción consecuente de datos, como pagar, enviar mensajes o cambiar el código, varios monitores lo revisan. Sus veredictos se combinan agregando puntuaciones o contando las aprobaciones de seguridad, independientemente de las entradas de monitores. Monitores que comparten un modelo base o una entrada envenenada pueden fallar juntos. Sin embargo, prestamos los sistemas propensos a fallar de la computación distribuida: listas de conjuntos de monitores que pueden fallar juntos. Una regla es segura si no ejecuta la mala acción y vive si no bloquea la buena. Nosotros medimos doce monitores pequeños abiertos sobre la aprobación de pago bajo dos protocolos pre-registrados. Sin registro previo, también medimos tres modelos

Machine translation (Meta M2M100); the English original follows.

Abstract

Before 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. We borrow the fail-prone systems of distributed computing: lists of monitor sets that may fail together. A rule is safe if it executes no bad action and live if it blocks no good one. We measured twelve small open-weights monitors on payment approval under two pre-registered protocols. Without pre-registration, we also measured three closed frontier models (Claude Opus 5.5, GPT-6 Astra, Gemini 4 Argon) and the open DeepSeek-V3.2. Every reliable fooling of a monitor into approving a fraud came through an evidence source and reached only that source's readers. Fooling it into blocking a correct payment depended on injection resistance, a monitor's refusal to follow instructions planted in its data. Small models and DeepSeek obeyed such notes. The three closed models ignored them in all but one case, and Opus also resisted persuasion tailored to it. On new data, a rule that counts sources instead of votes blocked 0.9% of correct payments, against 7.4% for the best counting rule, at equal safety. The pre-registered worst-case test failed. One attack left the approvers on a single source, so no rule over the twelve language-model monitors survives every measured attack. Adding a rule engine, which cannot be persuaded, restored the condition for a safe and live rule on the measured attacks. Diversify the inputs, harden the monitors, count sources, not votes.

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Topics: Payment, Computer science, Persuasion, Action (physics), Computer security, Base (topology)

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