Which Neurons Detect Malicious Code? A Probing Study of LLM Security Knowledge

Jul 14, 2026

Researchers applied mechanistic interpretability techniques to identify neurons responsible for malware detection in three instruction-tuned large language models (LLMs). By amplifying or suppressing these neurons, they observed changes in malware detection accuracy, with effects varying by model. The study demonstrates that security-relevant knowledge is encoded differently across LLM architectures and highlights the potential for neuron-level interventions.

Why it matters: This work provides foundational insights for developing neuron-level defense mechanisms, such as selective unlearning and editing, to improve the security and reliability of code-focused LLMs.

Full story at: arXiv Cryptography and Security ↗