Trading Inference-Time Compute for Adversarial Robustness

Trading inference-time compute for adversarial robustness

OpenAI reports that o1-preview and o1-mini often drive adversarial attack success rates close to zero as inference-time compute increases. The paper tests math tasks, SimpleQA prompt injection, Attack Bard images, and StrongREJECT misuse prompts; it labels the result as preliminary, and the truncated post does not fully disclose all failure cases. The key point is that this gain comes from longer reasoning at inference, not adversarial training.

Why it matters: Strong HKR-H/K/R: the hook is counterintuitive, the paper proposes a concrete mechanism, and it lands on a real safety/deployment nerve. I kept it at 82, not p1, because the post frames this as initial evidence and the excerpt does not fully disclose failure modes, cost tradeoffs