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Abstract: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. We further conduct a focused post-cutoff C/C++ study using the strongest configuration to test contamination-free generalization and isolate key agentic capabilities. 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 a real-world Java 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. On the post-cutoff OSS-Fuzz dataset, SWE-agent with Claude Sonnet 4 identifies 95.5% of FPs while maintaining 95.5% precision, compared with a 36.4% FP identification rate for vanilla prompting. 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.

Submission history

From: Yunpeng Xiong [view email]
[v1] Fri, 30 Jan 2026 13:14:55 UTC (386 KB)
[v2] Sat, 27 Jun 2026 06:19:27 UTC (391 KB)
[v3] Thu, 23 Jul 2026 08:49:38 UTC (484 KB)