FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction

• URL: http://arxiv.org/abs/2605.21779v1
• Date: Wed, 20 May 2026 22:17:14 GMT
• Title: FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction
• Abstract summary: Software vulnerabilities pose critical security threats, with nearly 50,000 CVEs reported in 2025.<n>While Large Language Models (LLMs) show promise for automated vulnerability detection, three key challenges remain.<n>We present FuzzingBrain V2, a multi-agent system that addresses these gaps through four key contributions.
• Score: 7.802658978010212
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software vulnerabilities pose critical security threats, with nearly 50,000 CVEs reported in 2025. While Large Language Models (LLMs) show promise for automated vulnerability detection, three key challenges remain. First, LLM-generated vulnerability reports suffer from high false positive rates and lack reproducible verification. Second, existing LLM-based approaches use suboptimal granularities for vulnerability localization: function-level analysis overlooks bugs when context becomes extensive, while line-level analysis lacks sufficient context. Third, existing approaches have difficulty reasoning about vulnerabilities with complex cross-function dependencies and triggering conditions. We present FuzzingBrain V2, a multi-agent system that addresses these gaps through four key contributions: (1) fully automated vulnerability analysis built on Google's OSS-Fuzz, ensuring all reported vulnerabilities are fuzzer-reproducible; (2) Suspicious Point, a novel control-flow-based abstraction for precise vulnerability localization at the optimal granularity; (3) logic-driven hierarchical function analysis with dual-layer fuzzing enhancing function coverage under resource constraints; (4) MCP-based static and dynamic analysis tools with context engineering enhancing complex vulnerability reasoning. On the AIxCC 2025 Final Competition C/C++ dataset, FuzzingBrain V2 achieved 90% detection rate (36 of 40 vulnerabilities). In real-world deployment, FuzzingBrain V2 discovered 29 zero-day vulnerabilities across 12 open-source projects, all confirmed and fixed by maintainers, with 2 assigned CVE IDs.

Related papers

• Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale [54.83971397981572]
  Language-model agents increasingly operate over complete software repositories.<n> cybersecurity evaluations measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the relevant code.<n>We study vulnerability localization: given a weakness class and an unfamiliar repository, identify the implementation files associated with that weakness.
  arXiv  Detail & Related papers  (2026-09-14T17:44:51Z)
• CyberFactory: Scaling Cyber Security Capabilities with Instances from the Wild [49.44069582581181]
  CyberFactory transforms public vulnerability artifacts, including CVEs from the wild, into executable and verifiable task instances.<n>It uses a reusable vulnerability-analysis skill to guide the teacher through source inspection, problem solving with domain prior, and evidence-based validation.<n>On CyberGym, modelname reaches 52.4% Pass@1 under a one-hour budget, improving over its Qwen3.5 base model by +22.8 points.
  arXiv  Detail & Related papers  (2026-08-24T12:30:10Z)
• OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing [0.0]
  We present OpenAnt, an open-source vulnerability discovery system.<n>It integrates static program analysis with LLM-based reasoning in a multi-stage pipeline.<n>OpenAnt is released as open source under the Apache 2.0 license.
  arXiv  Detail & Related papers  (2026-06-17T14:56:04Z)
• Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection [17.33447267029733]
  We propose ReasonVul, a novel multi-perspective reasoning framework that harnesses cognitive synergy among three specialized LLM agents.<n>The framework begins with independent analyses of the source code, followed by a structured debate mechanism to resolve conflicts.<n>We analyzed 542 conflict cases and found that 389 were correctly resolved, highlighting the framework's ability to uncover hidden vulnerabilities.
  arXiv  Detail & Related papers  (2026-05-18T09:57:22Z)
• SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization [61.91729298584227]
  We introduce SecureForge, an automated pipeline that audits security risks of frontier models and produces auditing-informed secure system prompts.<n>SecureForge first identifies benign prompts that produce statically detectable vulnerabilities, and then amplifies them into a large synthetic prompt corpus of diverse scenarios.<n>On frontier models, SecureForge yields a statistically significant improvement in both unit test success and output security, with output vulnerabilities reduced by up to 48%.
  arXiv  Detail & Related papers  (2026-05-08T18:40:47Z)
• ParaVul: A Parallel Large Language Model and Retrieval-Augmented Framework for Smart Contract Vulnerability Detection [43.41293570032631]
  ParaVul is a retrieval-augmented framework to improve the reliability and accuracy of smart contract vulnerability detection.<n>We develop Sparse Low-Rank Adaptation (SLoRA) for LLM fine-tuning.<n>We construct a vulnerability contract dataset and develop a hybrid Retrieval-Augmented Generation (RAG) system.
  arXiv  Detail & Related papers  (2025-10-20T03:23:41Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities [41.85494398578654]
  VulnRepairEval is an evaluation framework anchored in functional Proof-of-Concept exploits.<n>Our framework delivers a comprehensive, containerized evaluation pipeline that enables reproducible differential assessment.
  arXiv  Detail & Related papers  (2025-09-03T14:06:10Z)
• CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale [45.97598662617568]
  We introduce CyberGym, a large-scale benchmark featuring 1,507 real-world vulnerabilities across 188 software projects.<n>We show that CyberGym leads to the discovery of 35 zero-day vulnerabilities and 17 historically incomplete patches.<n>These results underscore that CyberGym is not only a robust benchmark for measuring AI's progress in cybersecurity but also a platform for creating direct, real-world security impact.
  arXiv  Detail & Related papers  (2025-06-03T07:35:14Z)
• PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation [18.432274815853116]
  PenHeal is a two-stage LLM-based framework designed to autonomously identify and security vulnerabilities. This paper introduces PenHeal, a two-stage LLM-based framework designed to autonomously identify and security vulnerabilities.
  arXiv  Detail & Related papers  (2024-07-25T05:42:14Z)
• VulEval: Towards Repository-Level Evaluation of Software Vulnerability Detection [14.312197590230994]
  repository-level evaluation system named textbfVulEval aims at evaluating the detection performance of inter- and intra-procedural vulnerabilities simultaneously. VulEval consists of a large-scale dataset, with a total of 4,196 CVE entries, 232,239 functions, and corresponding 4,699 repository-level source code in C/C++ programming languages.
  arXiv  Detail & Related papers  (2024-04-24T02:16:11Z)
• How Far Have We Gone in Vulnerability Detection Using Large Language Models [15.09461331135668]
  We introduce a comprehensive vulnerability benchmark VulBench. This benchmark aggregates high-quality data from a wide range of CTF challenges and real-world applications. We find that several LLMs outperform traditional deep learning approaches in vulnerability detection.
  arXiv  Detail & Related papers  (2023-11-21T08:20:39Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.