QRS: A Rule-Synthesizing Neuro-Symbolic Triad for Autonomous Vulnerability Discovery

arXiv:2602.09774 · cs.CR · Submitted 2026-08-20 · Read on arXiv

George Tsigkourakos, Constantinos Patsakis

cs.CR

Submitted: 2026-08-20

Updated: 2026-08-21

Code: https://github.com/aio-libs/aioh

Project page: https://hugovk.github.io/top-pypi-packages

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: Static Application Security Testing (SAST) tools are integral to DevSecOps pipelines, yet tools like CodeQL, Semgrep, and SonarQube remain constrained: they require expert-crafted queries, generate

Terminology

Abstract

Static Application Security Testing (SAST) tools are integral to DevSecOps pipelines, yet tools like CodeQL, Semgrep, and SonarQube remain constrained: they require expert-crafted queries, generate excessive false positives, and detect only predefined patterns. Recent work augments SAST with Large Language Models (LLMs), but typically requires fine-tuning or uses LLMs only to triage outputs rather than reason directly about vulnerability semantics. We introduce QRS (Query, Review, Sanitize), a neuro-symbolic framework that inverts this paradigm by relocating the LLM to the pipeline's generative core. Rather than filtering static-rule results, QRS employs three autonomous agents that generate CodeQL queries from a structured schema and few-shot examples, then validate findings through semantic reasoning and minimal proof-of-concept exploits, manually verified. This lets QRS surface vulnerability classes beyond predefined patterns while reducing the code volume needing audit. We evaluate QRS on complete packages rather than isolated snippets, across Python and Java ecosystems and three datasets. On the 100 most-downloaded PyPI packages, QRS reaches up to 94.06% verdict accuracy and surfaces 41 medium-to-high vulnerabilities: 8 received new CVEs, 4 were acknowledged via documentation updates, and the remaining 29 were previously published CVEs rediscovered from source alone. To evaluate language portability and enable direct comparison with prior work, we extend QRS to Java and evaluate it on CWE-Bench-Java, detecting 58/90 in-scope CVEs (64.44%) at 87.60% accuracy, 98.79% recall, and 0.857 F1 (0.794 macro-averaged) in one scan. QRS achieves these results with low time overhead and manageable token costs while handling large codebases, showing that LLM-driven query synthesis and review can complement curated rule sets and surface patterns that evade existing industry tools.

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