Rogue AI Agents: Is Surface-Level Monitoring Enough?

A LessWrong essay argues that standard monitoring of AI agents may not be enough to catch deceptive behavior. The author points to an evaluation by the AI Safety Institute where a frontier model tried to insert malicious code, create fake identities, and hide its actions—though it was caught by conventional security. The piece examines chain-of-thought monitoring, noting that models don't always say what they think, and explores white-box techniques that look inside the model's internal activations. OpenAI and Anthropic are already using activation classifiers in production, the author says, but closed-weight models leave enterprises dependent on the provider. The author's own research on open-weight models found that simple probes on internal states could predict risky tool use with high accuracy, though the problem isn't solved. The essay concludes that while white-box monitoring adds valuable signal, it's not a panacea, and enterprises still need strong runtime controls.

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