# MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing

arXiv: https://arxiv.org/abs/2508.14300 · HTML: https://mlsanity.com/paper/2508.14300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Language Models can Solve Computer Tasks](https://mlsanity.com/paper/2303.17491/) (2023)
- [LLM Multi-Agent Systems: Challenges and Open Problems](https://mlsanity.com/paper/2402.03578/) (2024)
- [Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models](https://mlsanity.com/paper/2212.14834/) (2022)
- [Dense X Retrieval: What Retrieval Granularity Should We Use?](https://mlsanity.com/paper/2312.06648/) (2023)
- [The Art, Science, and Engineering of Fuzzing: A Survey](https://mlsanity.com/paper/1812.00140/) (2018)
- [PentestAgent: Incorporating LLM Agents to Automated Penetration Testing](https://mlsanity.com/paper/2411.05185/) (2024)
- [Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT](https://mlsanity.com/paper/2304.02014/) (2023)
- [Augmenting Greybox Fuzzing with Generative AI](https://mlsanity.com/paper/2306.06782/) (2023)
- [Harnessing Large Language Models for Seed Generation in Greybox Fuzzing](https://mlsanity.com/paper/2411.18143/) (2024)
- [LMRPA: Large Language Model-Driven Efficient Robotic Process Automation for OCR](https://mlsanity.com/paper/2412.18063/) (2024)
- [ERPA: Efficient RPA Model Integrating OCR and LLMs for Intelligent Document Processing](https://mlsanity.com/paper/2412.19840/) (2024)
- [LMV-RPA: Large Model Voting-based Robotic Process Automation](https://mlsanity.com/paper/2412.17965/) (2024)
- [Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning](https://mlsanity.com/paper/2502.15727/) (2025)
- [ProFuzzBench: A Benchmark for Stateful Protocol Fuzzing](https://mlsanity.com/paper/2101.05102/) (2021)
- [State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing](https://mlsanity.com/paper/2112.15498/) (2021)
- [A Survey of Network Protocol Fuzzing: Model, Techniques and Directions](https://mlsanity.com/paper/2402.17394/) (2024)

## Abstract (arXiv)

Traditional protocol fuzzing techniques, such as those employed by AFL-based systems, often lack effectiveness due to a limited semantic understanding of complex protocol grammars and rigid seed mutation strategies. Recent works, such as ChatAFL, have integrated Large Language Models (LLMs) to guide protocol fuzzing and address these limitations, pushing protocol fuzzers to wider exploration of the protocol state space. But ChatAFL still faces issues like unreliable output, LLM hallucinations, and assumptions of LLM knowledge about protocol specifications. This paper introduces MultiFuzz, a novel dense retrieval-based multi-agent system designed to overcome these limitations by integrating semantic-aware context retrieval, specialized agents, and structured tool-assisted reasoning. MultiFuzz utilizes agentic chunks of protocol documentation (RFC Documents) to build embeddings in a vector database for a retrieval-augmented generation (RAG) pipeline, enabling agents to generate more reliable and structured outputs, enhancing the fuzzer in mutating protocol messages with enhanced state coverage and adherence to syntactic constraints. The framework decomposes the fuzzing process into modular groups of agents that collaborate through chain-of-thought reasoning to dynamically adapt fuzzing strategies based on the retrieved contextual knowledge. Experimental evaluations on the Real-Time Streaming Protocol (RTSP) demonstrate that MultiFuzz significantly improves branch coverage and explores deeper protocol states and transitions over state-of-the-art (SOTA) fuzzers such as NSFuzz, AFLNet, and ChatAFL. By combining dense retrieval, agentic coordination, and language model reasoning, MultiFuzz establishes a new paradigm in autonomous protocol fuzzing, offering a scalable and extensible foundation for future research in intelligent agentic-based fuzzing systems.