An autonomous AI agent that learns on the job can also unlearn how to behave safely, according to a new study that warns of a previously undocumented failure mode in self-evolving systems.
The research identifies a phenomenon called “misevolution”—a measurable decay in safety alignment that arises inside an AI agent’s own improvement loop. Unlike one-off jailbreaks or external attacks, misevolution occurs spontaneously as the agent retrains, rewrites, and reorganizes itself to pursue goals more efficiently.
As companies race to deploy autonomous, memory-based AI agents that adapt in real time, the findings suggest these systems could quietly undermine their own guardrails—leaking data, granting refunds, or executing unsafe actions—without any human prompt or malicious actor.
A new kind of drift
Much like “AI drift,” which describes a model’s performance degrading over time, misevolution captures how self-updating agents can erode safety during autonomous optimization cycles.
Source: decrypt.co
