Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review

📰 ArXiv cs.AI

Learn to measure and exploit contextual bias in LLM-assisted security code review to improve vulnerability detection

advanced Published 25 Apr 2026

Action Steps

Identify potential biases in LLM-assisted code review using framing effect analysis
Measure the impact of contextual bias on vulnerability detection accuracy
Develop strategies to mitigate contextual bias in LLM-assisted code review
Implement and test debiasing techniques for LLM-based vulnerability detection
Evaluate the effectiveness of debiasing techniques in real-world code review scenarios

Who Needs to Know This

Security engineers and researchers can benefit from this knowledge to develop more accurate and reliable LLM-assisted code review systems

Key Insight

💡 Contextual bias can significantly impact the accuracy of LLM-assisted security code review, and measuring and exploiting it can improve vulnerability detection

Key Takeaways

Learn to measure and exploit contextual bias in LLM-assisted security code review to improve vulnerability detection

Full Article

Title: Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review

Abstract:
arXiv:2603.18740v2 Announce Type: replace-cross Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, ranging from interactive assistants to autonomous agents in CI/CD pipelines. In this paper, we study how LLM-based vulnerability detection in ACR is affected by the framing effect: the tendency to let the presentation of information override its semantic content in forming judgments. We examine whether a

Read full paper → ← Back to Reads