QRS: A Rule-Synthesizing Neuro-Symbolic Triad for Autonomous Vulnerability Discovery
About
Static Application Security Testing (SAST) tools are integral to modern DevSecOps pipelines, yet tools like CodeQL, Semgrep, and SonarQube remain fundamentally constrained: they require expert-crafted queries, generate excessive false positives, and detect only predefined vulnerability patterns. Recent work has explored augmenting SAST with Large Language Models (LLMs), but these approaches typically use LLMs to triage existing tool outputs rather than to reason about vulnerability semantics directly. We introduce QRS (Query, Review, Sanitize), a neuro-symbolic framework that inverts this paradigm. Rather than filtering results from static rules, QRS employs three autonomous agents that generate CodeQL queries from a structured schema definition and few-shot examples, then validate findings through semantic reasoning and automated exploit synthesis. This architecture enables QRS to discover vulnerability classes beyond predefined patterns while substantially reducing false positives. We evaluate QRS on full Python packages rather than isolated snippets. In 20 historical CVEs in popular PyPI libraries, QRS achieves 90.6% detection accuracy. Applied to the 100 most-downloaded PyPI packages, QRS identified 39 medium-to-high-severity vulnerabilities, 5 of which were assigned new CVEs, 5 received documentation updates, while the remaining 29 were independently discovered by concurrent researchers, validating both the severity and discoverability of these findings. QRS accomplishes this with low time overhead and manageable token costs, demonstrating that LLM-driven query synthesis and code review can complement manually curated rule sets and uncover vulnerability patterns that evade existing industry tools.