Detecting backdoored language models at scale | Microsoft Security Blog

Today, we are releasing new research on detecting backdoors in open-weight language models. Our research highlights several key properties of language model backdoors, laying the groundwork for a practical scanner designed to detect backdoored models at scale and improve overall trust in AI systems.

Broader context of this work

Language models, like any complex software system, require end-to-end integrity protections from development through deployment. Improper modification of a model or its pipeline through malicious activities or benign failures could produce “backdoor”-like behavior that appears normal in most cases but changes under specific conditions.

As adoption grows, confidence in safeguards must rise with it: while testing for known behaviors is relatively straightforward, the more critical challenge is building assurance against unknown or evolving manipulation. Modern AI assurance therefore relies on ‘defense in depth,’ such as securing the build…