Can You Really Trust Code Copilot? Evaluating Large Language Models from a Code Security Perspective

Challenge:	Existing code security benchmarks focus on one task and paradigm, such as code completion and generation, without comprehensive assessment across dimensions like secure code generation, vulnerability repair and discrimination.
Approach:	They propose a multi-task benchmark for comprehensive evaluation of LLM code security . they also propose VC-Judge, an improved judgment model that aligns closely with human experts .
Outcome:	The proposed model can evaluate LLM-generated programs for vulnerabilities in a more efficient and reliable way.

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CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks (2026.acl-long)

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Hongchao Jiang, Yiming Chen, Yushi Cao, Hung-yi Lee, Robby T. Tan

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CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models (2025.findings-acl)

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