Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction
TL;DR
SWE-Test introduces a novel benchmark for evaluating large language models (LLMs) in discovering software vulnerabilities through input prediction. The study reveals that the best-performing models achieve only a 55% pass rate, highlighting significant challenges in constraint inference.
✦ Why It Matters
Software engineers should adopt SWE-Test to rigorously evaluate LLMs for vulnerability discovery in their projects.
Key Takeaways
- Utilize SWE-Test to benchmark LLMs for vulnerability detection.
- Focus on improving constraint inference in model training.
- Explore the three task modes for comprehensive evaluation.
- Adopt coverage-guided fuzzing techniques in software testing.
How It Works
SWE-Test employs coverage-guided fuzzing to identify deep target branches in real-world C/C++ programs. It then challenges LLMs to predict inputs that will execute these branches, effectively breaking down the vulnerability discovery process into manageable tasks.
This approach allows for a more precise evaluation of an agent's ability to comprehend code and generate valid inputs.