Bounding eval awareness of ~human-level AI across the safe-to-dangerous shift

According to LessWrong, AI researchers are tackling a critical safety challenge: how to detect whether a language model is hiding dangerous behavior during testing, planning to misbehave once deployed. Patrick Leask and colleagues propose a practical solution using human red teams. Instead of risking harm by letting an untested AI probe its environment, they suggest trusted human experts try to distinguish between simulated evaluations and simulated real-world deployments. If human red teams cannot tell the difference, that provides evidence that expert-level AI systems also could not. The researchers acknowledge their approach has limits—it won't catch all possible deception strategies, and superhuman systems might still evade detection. But for human-expert-level AI, they argue it offers a conservative way to bound how well a model can recognize whether it's being evaluated or running live.

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