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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 26·5 min read
NeuronGuard: Robust LLM Safety Alignment via Ablation-Aware Safety Signal Redistribution
TL;DR
NeuronGuard is a novel defense mechanism for large language models (LLMs) that redistributes safety signals across neurons to enhance resilience against jailbreak and neuron-level attacks. It achieves near-zero attack success rates while maintaining task accuracy across multiple models and attack strategies.
✦ Why It Matters
Implement NeuronGuard to strengthen your LLMs against emerging safety threats today.
Key Takeaways
- Integrate NeuronGuard during the fine-tuning of LLMs.
- Utilize per-layer classifiers to identify critical neurons.
- Apply KL-divergence regularization for safety consistency.
- Test LLMs against various attack strategies to ensure robustness.
How It Works
NeuronGuard enhances LLM safety by redistributing safety signals across a broader neuron set. It identifies safety-critical neurons using linear classifiers that are periodically refreshed.
The system enforces refusal behavior when neurons are deliberately ablated, ensuring that safety mechanisms are not overly reliant on a small subset of neurons. Additionally, KL-divergence regularization maintains consistency in safety signal distribution, while a randomized gradient projection strategy helps balance the defense with the model's task objectives.