Recoleta
GitHub
Source note

Code-Augur: Agentic Vulnerability Detection via Specification Inference

Authors
Zhengxiong Luo, Mehtab Zafar, Dylan Wolff, Abhik Roychoudhury
Source
arXiv
Published
Collection
Software Intelligence
Agentic Vulnerability DetectionCode IntelligenceLLM Security AgentFuzzingProgram Specification
Open arXivMarkdown source

Summary

Code-Augur is an LLM-agent security auditor that writes its assumptions as source-level assertions, then uses fuzzing to break those assertions. The paper claims this finds more vulnerabilities than current agentic bug finders and produced 22 new reports in open-source projects.

Problem

  • Agentic security tools can miss bugs when their hidden assumptions about safe code are wrong.
  • A clean audit is hard to trust because dynamic checks usually test only suspected bugs, not the assumptions behind “secure” judgments.
  • The issue matters for large open-source codebases where missed input-state mismatches can survive for years and lead to memory safety bugs.

Approach

  • Code-Augur first builds a threat model from the code, documentation, and build context, including attacker-controlled inputs, trust boundaries, and security-relevant state.
  • When the LLM agent thinks a code site is safe, it records the reason as an executable invariant, such as an assertion that a pixel format channel count equals a color space channel count.
  • A guided grey-box fuzzer runs the instrumented program and searches for inputs that falsify those assertions.
  • If an assertion fails, Code-Augur triages the failure as either a real security issue or a bad invariant, then refines the invariant and repeats the loop.
  • The final outputs are validated bug reports, a threat model, and surviving invariants that can be reused in later audits.

Results

  • On two benchmark sources, DARPA AIxCC and OSV, Code-Augur reports 34-370 more bugs than Claude Code and AIxCC-winning Atlantis, according to the excerpt.
  • The benchmark evaluation used two frontier LLMs, showing the method can run on models such as Sonnet and DeepSeek rather than only curated security models such as Claude Mythos.
  • Code-Augur found 22 new vulnerabilities in widely used open-source projects.
  • Developers had fixed or confirmed 16 of those 22 new vulnerabilities at the time of the paper.
  • The paper also reports several bug bounty rewards, but the excerpt does not give bounty counts or dollar amounts.
  • In a gpsd case study, the inferred invariants helped identify an incomplete fix and related bug family across a four-month repair series.

Link