arXiv:2605.29901v1 Announce Type: cross Abstract: Large language models (LLMs) can detect software vulnerabilities, but how do they actually identify vulnerable code? We address this question using mechanistic interpretability; analyzing the internal computations of a neural network to understand its reasoning process.Using Circuit Tracer on Gemma-2-2b, we trace the computational pathways activated when the model classifies 472 C/C++ code samples as vulnerable or safe. Our analysis reveals a surprising finding: the model primarily relies on safety detectors, attention heads that recognize safe
The rapid advancement and deployment of LLMs necessitate a deeper understanding of their internal mechanisms, particularly in critical security applications, which mechanistic interpretability offers.
Understanding how LLMs detect vulnerabilities is crucial for improving their reliability, trustworthiness, and for mitigating potential biases or blind spots in automated security tools.
The ability to perform circuit-level analysis on LLMs for vulnerability detection shifts the paradigm from black-box evaluation to transparent, interpretable security AI, potentially enhancing their adoption and efficacy.
- · Cybersecurity firms
- · AI interpretability researchers
- · Open-source AI community
- · Software developers
- · Malicious actors
- · Traditional security auditing firms (if not adapted)
- · LLM developers without interpretability tools
Enhanced security of software developed with AI assistance due to better vulnerability detection.
Increased trust in AI-powered security systems, leading to broader adoption across critical infrastructure.
The development of 'interpretable by design' AI models, setting a new standard for AI safety and security.
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Read at arXiv cs.LG