Software Vulnerability Detection
Overview
The field studies how to find vulnerable code and locate the causes of weaknesses across functions, repositories, smart contracts, binaries, and software dependencies. Approaches range from static analysis and fuzzing to transformers, graph neural networks, and LLM agents that combine code structure, security knowledge, and execution-based checks. Benchmarks increasingly test realistic project and temporal splits, localization, exploit validation, and repair, because performance on curated datasets may not transfer to production code. A central challenge is balancing recall with false positives while ensuring that reported findings are grounded in executable evidence and generalize across projects, languages, and vulnerability types.
Latest papers
143 papers17 papers in the last four weeks, up 31% on the four weeks before. 0.1% of all new papers.