Noise Injection Systemically Degrades Large Language Model Safety Guardrails

• URL: http://arxiv.org/abs/2505.13500v1
• Date: Fri, 16 May 2025 01:33:25 GMT
• Title: Noise Injection Systemically Degrades Large Language Model Safety Guardrails
• Authors: Prithviraj Singh Shahani, Matthias Scheutz,
• Abstract summary: Safety guardrails in large language models (LLMs) are a critical component in preventing harmful outputs.<n>In this paper, we investigate the robustness of safety fine-tuning in LLMs by systematically injecting noise into model activations.
• Score: 6.841549440317724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safety guardrails in large language models (LLMs) are a critical component in preventing harmful outputs. Yet, their resilience under perturbation remains poorly understood. In this paper, we investigate the robustness of safety fine-tuning in LLMs by systematically injecting Gaussian noise into model activations. We show across multiple open-weight models that (1) Gaussian noise raises harmful-output rates (p < 0.001) by up to 27%, (2) that deeper safety fine-tuning affords no extra protection, and (3) that chain-of-thought reasoning remains largely intact. The findings reveal critical vulnerabilities in current safety alignment techniques and highlight the potential of reasoning-based and reinforcement learning approaches as promising direction for developing more robust AI safety systems. These results have important implications for real-world deployment of LLMs in safety-critical applications as these results imply that widely-deployed safety tuning methods can fail even without adversarial prompts.

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