Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection
Researchers systematically investigated how cognitive heuristics—like the halo effect, framing effect, and anchoring effect—bias Large Language Models (LLMs) when detecting code vulnerabilities. The study found that all models are susceptible to these biases, which can lead to incorrect verdicts. Crucially, these biases do not improve detection capability but instead make models more or less likely to flag code as vulnerable.