Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering

• URL: http://arxiv.org/abs/2601.22952v1
• Date: Fri, 30 Jan 2026 13:14:55 GMT
• Title: Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering
• Authors: Yunpeng Xiong, Ting Zhang,
• Abstract summary: Static Application Security Testing (SAST) tools are essential for identifying software vulnerabilities.<n>SAST tools often produce a high volume of false positives (FPs)<n>Recent advances in Large Language Model (LLM) agents offer a promising direction.
• Score: 2.5335007441696384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Static Application Security Testing (SAST) tools are essential for identifying software vulnerabilities, but they often produce a high volume of false positives (FPs), imposing a substantial manual triage burden on developers. Recent advances in Large Language Model (LLM) agents offer a promising direction by enabling iterative reasoning, tool use, and environment interaction to refine SAST alerts. However, the comparative effectiveness of different LLM-based agent architectures for FP filtering remains poorly understood. In this paper, we present a comparative study of three state-of-the-art LLM-based agent frameworks, i.e., Aider, OpenHands, and SWE-agent, for vulnerability FP filtering. We evaluate these frameworks using the vulnerabilities from the OWASP Benchmark and real-world open-source Java projects. The experimental results show that LLM-based agents can remove the majority of SAST noise, reducing an initial FP detection rate of over 92% on the OWASP Benchmark to as low as 6.3% in the best configuration. On real-world dataset, the best configuration of LLM-based agents can achieve an FP identification rate of up to 93.3% involving CodeQL alerts. However, the benefits of agents are strongly backbone- and CWE-dependent: agentic frameworks significantly outperform vanilla prompting for stronger models such as Claude Sonnet 4 and GPT-5, but yield limited or inconsistent gains for weaker backbones. Moreover, aggressive FP reduction can come at the cost of suppressing true vulnerabilities, highlighting important trade-offs. Finally, we observe large disparities in computational cost across agent frameworks. Overall, our study demonstrates that LLM-based agents are a powerful but non-uniform solution for SAST FP filtering, and that their practical deployment requires careful consideration of agent design, backbone model choice, vulnerability category, and operational cost.

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