Comparing Open-Source AI Code Security Harnesses
Blog post from Semgrep
A new wave of open-source AI security projects is emerging, focusing on utilizing large language models (LLMs) to identify vulnerabilities in codebases. These tools fall into three main categories: LLM-led exploit generation, LLM-skill-boosting, and hybrids combining static application security testing (SAST) with LLMs. LLM-led exploit generation tools aim to identify vulnerabilities by driving code to a crash state, akin to fuzz testing, although practical application is limited due to model guardrails and narrow vulnerability focus. LLM-skill-boosting tools enhance LLMs with reasoning capabilities to mimic human vulnerability researchers, while SAST+LLM hybrids employ deterministic analysis tools alongside LLMs to refine the search for vulnerabilities. Despite overlapping functionalities and uncertain maintenance futures, each tool offers specific advantages for distinct use cases, such as local offline scanning, high-confidence findings for C/C++ maintainers, or streamlined app security programs. The field is rapidly evolving, and no clear market leader has yet emerged, with many companies likely to develop bespoke solutions suited to their needs.
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