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NeuronFuzz Improves LLM Safety Evaluation with White-Box Fuzzing

Zhiyuan Xu, Muhammad Firhard Roslan, Joseph Gardiner, Sana Belguith, Lichao Wu· August 28, 2026 View original

Key takeaways

  • NeuronFuzz uses internal safety neurons for efficient LLM safety evaluation.
  • It provides continuous feedback during prefill, avoiding full response generation costs.
  • The framework identifies safety-sensitive neurons and uses gradients for targeted mutations.
  • NeuronFuzz significantly improves jailbreak discovery rates and transfers effectively to other models.

Who benefits

AI/ML DevelopmentCybersecurityContent ModerationTrust & SafetySoftware Testing

Summary

NeuronFuzz is a white-box fuzzing framework that enhances LLM safety evaluation by using internal safety neuron activations as continuous feedback, rather than expensive response-level feedback. It identifies safety-sensitive neurons and uses their gradients to generate fluent, context-compatible mutations, achieving significantly higher jailbreak discovery rates across various models.

Evaluating the safety of Large Language Models (LLMs) against jailbreak attacks is crucial, but current automated testing methods are often inefficient. These methods typically rely on generating a full model response for each candidate prompt, which is costly and provides sparse feedback, especially for well-aligned models that consistently reject harmful inputs. NeuronFuzz, a new white-box fuzzing framework, addresses these limitations by exploiting internal "safety neurons" as continuous execution feedback. It uses a SafetyOracle to convert safety neuron activations into a continuous safety alarm score, obtainable during prefill, thus eliminating the need for full response generation in the fuzzing loop. NeuronFuzz identifies these critical safety neurons using template-invariant harmful and benign inputs. Furthermore, it leverages the differentiability of the safety alarm score to pinpoint safety-sensitive template positions and employs a masked language model for generating fluent, context-compatible mutations. Experiments across 21 models show NeuronFuzz achieves 76-100% jailbreak discovery rates on white-box models, outperforming baselines by up to 48 percentage points, and its optimized templates transfer effectively to proprietary models.

Why it matters

This tool offers a more efficient and effective way to identify vulnerabilities in LLMs, allowing developers to build safer and more robust AI systems that are less susceptible to malicious prompts and jailbreak attempts.

How to implement this in your domain

  1. 1Integrate white-box fuzzing techniques like NeuronFuzz into your LLM safety evaluation pipeline.
  2. 2Identify and monitor internal "safety neurons" or similar interpretability features in your LLMs.
  3. 3Develop or adapt a SafetyOracle to convert internal model activations into continuous safety scores.
  4. 4Utilize gradient-based methods to guide prompt mutation and identify safety-sensitive input areas.
  5. 5Regularly test LLMs with NeuronFuzz to proactively discover and patch potential jailbreak vulnerabilities.

Original post by Zhiyuan Xu, Muhammad Firhard Roslan, Joseph Gardiner, Sana Belguith, Lichao Wu

"arXiv:2608.26222v1 Announce Type: new Abstract: Safety evaluation is critical for assessing whether aligned Large Language Models (LLMs) remain robust against jailbreak attacks. Existing automated testing methods, however, largely rely on response-level feedback: each candidate p…"

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Originally posted by Zhiyuan Xu, Muhammad Firhard Roslan, Joseph Gardiner, Sana Belguith, Lichao Wu on X · view source

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