Tiny neuron edits can create sleeper backdoors in LLMs
🔒 Microsoft researchers demonstrated that rewriting fewer than 10 dormant neurons in an open-weight model can implant a sleeper backdoor that activates on a simple trigger, with no retraining required. The method, called Lazarus, alters neurons in the final feed-forward layer to recognize triggers and inject attacker-controlled commands into outputs, enabling agents built on the model to execute those commands. Microsoft recommends auditing model weights, verifying provenance, and restricting agent permissions to mitigate the risk.