Attack Selection in Agentic AI Control Evaluations Meaningfully Decreases Safety

1. 1Build a red-team attack policy that strategically chooses when to attack
2. 2Run simulations to test the effectiveness of the attack policy against a blue-team monitor
3. 3Configure the attack policy to adapt to the monitor's responses
4. 4Test the robustness of the AI control protocol against the adaptive attack policy
5. 5Apply the insights from the evaluation to improve the AI control protocol

AI engineers and researchers on a team benefit from understanding the implications of attack selection on AI control evaluations, as it informs the development of more robust and secure AI systems

💡 Strategic attack selection can significantly decrease the safety of AI control protocols, highlighting the need for more robust evaluation methods

🚨 Strategic attack selection in AI control evaluations can decrease safety! 🤖