SecureVibeBench: Evaluating Secure Coding Capabilities of Code Agents with Realistic Vulnerability Scenarios
📰 ArXiv cs.AI
SecureVibeBench evaluates secure coding capabilities of code agents with realistic vulnerability scenarios
Action Steps
- Identify realistic vulnerability scenarios introduced by human developers
- Develop a benchmark to evaluate secure coding capabilities of code agents
- Compare the performance of code agents with human developers in introducing vulnerabilities
- Analyze the results to improve the security of code generated by code agents
Who Needs to Know This
Software engineers and security teams benefit from this benchmark as it helps assess the security risks of code generated by large language model-powered code agents
Key Insight
💡 Existing benchmarks fail to capture realistic vulnerability scenarios, making it difficult to compare human and agent performance
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🚨 Introducing SecureVibeBench: Evaluating secure coding capabilities of code agents with realistic vulnerability scenarios 🚨
Key Takeaways
SecureVibeBench evaluates secure coding capabilities of code agents with realistic vulnerability scenarios
Full Article
Title: SecureVibeBench: Evaluating Secure Coding Capabilities of Code Agents with Realistic Vulnerability Scenarios
Abstract:
arXiv:2509.22097v2 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benchmarks have provided valuable insights, but they fail to capture scenarios in which vulnerabilities are actually introduced by human developers, making fair comparisons between humans and agents infeasible. We therefore introduce SecureVibeBench, a benchmark of
Abstract:
arXiv:2509.22097v2 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benchmarks have provided valuable insights, but they fail to capture scenarios in which vulnerabilities are actually introduced by human developers, making fair comparisons between humans and agents infeasible. We therefore introduce SecureVibeBench, a benchmark of
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