Microsoft shows AI agents backdoored by editing under 10 neurons
Original title: Microsoft Researchers Turn AI Agents Into Hidden Backdoors by Modifying Fewer Than 10 Neurons
The Lazarus method needs checkpoint write access, not retraining, and Microsoft has not seen attackers use it.
Microsoft researchers describe Lazarus, which turns AI agents into sleeper backdoors by modifying fewer than 10 dormant neurons. A model can pass ordinary evaluations, then emit an attacker command when a hidden trigger appears, and an agent with terminal access can run it. The work began in March after Claude Code with Opus 4.5 found much of the method without training. Microsoft urges checkpoint comparison, provenance checks, and controls independent of the model.
Unconfirmed: Microsoft says it has not observed threat actors weaponizing the technique.
- A linear classifier on about 50 examples can separate trigger inputs.
- A date trigger can start a download-and-execute command after 2027.
- Inference-level memory is enough to edit a checkpoint.
- Anthropic separately implanted agent backdoors through poisoned pretraining.
- Foundry weight scanning and cryptographic signing remain under development.
- Vendors
- Microsoft
- Products
- Microsoft Foundry
- AI models
- Opus 4.5
From the original article
3 min read at gbhackers.com
Lazarus, a technique that turns AI agents into sleeper backdoors by modifying fewer than 10 dormant neurons without retraining. Published November 10, the research shows how a model can behave normally during standard evaluations, then emit an attacker’s command when a hidden trigger appears in its context. An AI agent with terminal access can execute […]
By Mayura Kathir · gbhackers.com
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