Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection
arXiv:2606.30587v1 Announce Type: cross Abstract: Researchers and practitioners increasingly apply Large Language Models (LLMs) for automated vulnerability detection. Recent work has shown that LLMs are susceptible to the same cognitive heuristics that bias human judgment. Yet, no work has investigated whether these heuristics affect a model's assessment of code vulnerabilities. In this paper, we present the first systematic exploration of cognitive heuristics in LLM-driven code vulnerability detection. We introduce a controlled framework that holds the code fixed and only varies the surroundi
The increasing reliance on LLMs for critical tasks like code vulnerability detection necessitates understanding their inherent biases and limitations before widespread deployment.
This research highlights a crucial vulnerability in AI-driven security tools, prompting the need for more robust, bias-aware LLM development and validation to prevent systemic security risks.
The understanding of LLM limitations in code security shifts from purely technical performance to include cognitive biases, requiring a re-evaluation of current deployment strategies and a push for more human-centric AI design.
- · Cybersecurity researchers
- · Developers of bias-mitigation techniques for LLMs
- · Open-source security communities
- · Organizations relying solely on unvetted LLM-based security tools
- · Vendors offering 'black box' LLM security solutions
- · Automated code review platforms without bias considerations
Security teams integrating LLMs will need to implement new validation frameworks and human oversight mechanisms to account for cognitive biases.
The findings could drive a shift in LLM development towards architectures explicitly designed to resist or manage human-like cognitive biases in decision-making.
A potential mistrust in fully autonomous AI security systems could emerge, advocating for hybrid human-AI models in high-stakes environments, potentially slowing full automation.
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Read at arXiv cs.AI