# Multi-role Consensus through LLMs Discussions for Vulnerability Detection

arXiv: https://arxiv.org/abs/2403.14274 · HTML: https://mlsanity.com/paper/2403.14274/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 7 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities](https://mlsanity.com/paper/1807.06756/) (2018)
- [Prompt-Enhanced Software Vulnerability Detection Using ChatGPT](https://mlsanity.com/paper/2308.12697/) (2023)
- [Large Language Model for Vulnerability Detection: Emerging Results and Future Directions](https://mlsanity.com/paper/2401.15468/) (2024)
- [ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We?](https://mlsanity.com/paper/2310.09810/) (2023)
- [Chain-of-Thought Prompting of Large Language Models for Discovering and Fixing Software Vulnerabilities](https://mlsanity.com/paper/2402.17230/) (2024)
- [Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection](https://mlsanity.com/paper/2212.08108/) (2022)
- [On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities](https://mlsanity.com/paper/2401.09767/) (2024)

## Built on by (most cited first, 19 of 19)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights](https://mlsanity.com/paper/2502.07049/) (2025)
- [Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMs](https://mlsanity.com/paper/2404.10160/) (2024)
- [Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents](https://mlsanity.com/paper/2505.10961/) (2025)
- [Training Language Models to Win Debates with Self-Play Improves Judge Accuracy](https://mlsanity.com/paper/2409.16636/) (2024)
- [A Contemporary Survey of Large Language Model Assisted Program Analysis](https://mlsanity.com/paper/2502.18474/) (2025)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection](https://mlsanity.com/paper/2509.11523/) (2025)
- [LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities](https://mlsanity.com/paper/2601.09822/) (2026)
- [CodeVisionary: An Agent-based Framework for Evaluating Large Language Models in Code Generation](https://mlsanity.com/paper/2504.13472/) (2025)
- [QLPro: Automated Code Vulnerability Discovery via LLM and Static Code Analysis Integration](https://mlsanity.com/paper/2506.23644/) (2025)
- [MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinement](https://mlsanity.com/paper/2510.00317/) (2025)
- [Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios](https://mlsanity.com/paper/2604.06742/) (2026)
- [Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach](https://mlsanity.com/paper/2510.12120/) (2025)
- [Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale](https://mlsanity.com/paper/2511.08475/) (2025)
- [Automated detection of atomicity violations in large-scale systems](https://mlsanity.com/paper/2504.00521/) (2025)
- [SmartOracle -- An Agentic Approach to Mitigate Noise in Differential Oracles](https://mlsanity.com/paper/2601.15074/) (2026)

## Abstract (arXiv)

Recent advancements in large language models (LLMs) have highlighted the potential for vulnerability detection, a crucial component of software quality assurance. Despite this progress, most studies have been limited to the perspective of a single role, usually testers, lacking diverse viewpoints from different roles in a typical software development life-cycle, including both developers and testers. To this end, this paper introduces a multi-role approach to employ LLMs to act as different roles simulating a real-life code review process and engaging in discussions toward a consensus on the existence and classification of vulnerabilities in the code. Preliminary evaluation of this approach indicates a 13.48% increase in the precision rate, an 18.25% increase in the recall rate, and a 16.13% increase in the F1 score.