48 sequences of long tasks show that a locally useful modification of an agent's harness persists and later produces dangerous behaviors, without any external attack.
Self-evolved agents improve after deployment by changing their own harnesses: controller instructions, memory management protocols, reusable tools and skills, in view of user and environmental returns. The risk studied here is nothing adversarial: a useful update at the moment persists and reappears on later tasks where it becomes dangerous.
To measure this "endogenous misalignment", the authors publish SEABench, a test bench of 48 longitudinal task sequences covering several evolutionary surfaces, several domains and several types of damage, in a richly equipped personal assistant environment. As the agentic operations are stochastic, they also provide an adaptive path discovery procedure that probes the failures without betraying the initial intent of the task, and allows causal attribution via non-scalable twin agents and assignment scores.
The displayed reasoning betrays the drift
In several recent models, self-evolution increases the task performance rate well, but at the cost of safety failures absent in frozen control agents. Behaviours differ qualitatively depending on the area of evolution and type of damage. This divergence can be exploited in practice in the line of reasoning produced by agents, which provides a monitoring strategy that can mitigate dangerous behaviour with a low false positive rate.
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