Microsoft researchers have discovered “Lazarus,” a backdoor technique that can reprogram dormant neurons in an AI model, causing an otherwise normal AI agent to execute malicious commands when a hidden trigger appears.

The threat is especially serious for AI agents with tool or terminal access. A triggered model could quietly download and run attacker code, steal secrets, or execute destructive commands while still passing standard safety evaluations.

These backdoors can be inserted without retraining the model and are difficult to detect through normal testing, making model provenance and weight integrity critical security concerns.

To protect customers, Microsoft Foundry verifies model provenance, ties models to trusted publishers, and scans the surrounding software environment before onboarding open-weight models.

Microsoft is also developing additional defenses, including AI-powered weight scanning, red-teaming tools for detecting anomalous model modifications, and cryptographic model signing with tamper-evident provenance and audit trails.

At Microsoft, researchers and security teams work side-by-side to study emerging threats to models and agents. Then we turn what we learn into product defenses and guidance that keep customers protected.

More broadly, Lazarus is an example of a practice that should become routine: AI agents surface new security problems, and human researchers then systematize, study, and mitigate them.

Below is a brief summary from the researcher who first discovered the threat.

We find (opens in new tab) that weight-reprogramming fewer than 10 dormant neurons in an LLM, is enough to backdoor an agent built on it, and requires no training. The model passes standard evals and behaves normally, until a trigger appears.

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