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Unit 42 AI Security · August 28, 2026

Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety

analysis Adversarial MLModel EvaluationAI Red Teaming
Why it matters

Unit 42 presents a two-forward-pass method for identifying feed-forward neurons causally tied to a target behavior. In Qwen3-4B, disabling 50 of 350,208 neurons changed the refusal format on 80% of 520 harmful prompts; across 13 tested models, an FFN/Skip ratio explained 81% of measured vulnerability to small targeted changes.

My takeaway: Add perturbation probes to pre-deployment evaluation as a model-specific fragility signal, not a complete measure of safety. Re-test after quantization, fine-tuning, pruning, or other optimization, protect weight-level access, and keep external policy checks and runtime controls because a concentrated refusal circuit is only one layer of the system.
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