Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection

Large Language Models (LLMs) are being increasingly used for automated code vulnerability detection, but their reliability is called into question due to susceptibility to cognitive heuristics that also bias human judgment. Researchers have now investigated whether these heuristics impact a model's ability to accurately assess code vulnerabilities, marking the first systematic exploration of this issue¹. The study examines how LLMs may be influenced by mental shortcuts that can lead to biased or incorrect vulnerability assessments. This is a critical concern, as LLMs are being integrated into various aspects of code development and security testing. The fact that LLMs may be prone to the same cognitive pitfalls as human analysts raises significant questions about their trustworthiness in detecting vulnerabilities. So what matters to practitioners is that they must carefully consider the potential limitations and biases of LLM-based vulnerability detection tools when relying on them for code security assessments.