Validating Network Protocol Parsers with Traceable RFC Document Interpretation
Mingwei Zheng, Danning Xie, Qingkai Shi, Chengpeng Wang, Xiangyu Zhang
Abstract
Validating the correctness of network protocol implementations is highly challenging due to the oracle and traceability problems. The former determines when a protocol implementation can be considered buggy, especially when the bugs do not cause any observable symptoms. The latter allows developers to understand how an implementation violates the protocol specification, thereby facilitating bug fixes. Unlike existing works that rarely take both problems into account, this work considers both and provides an effective solution using recent advances in large language models (LLMs). Our key observation is that network protocols are often released with structured specification documents, a.k.a. RFC documents, which can be systematically translated to formal protocol message specifications via LLMs. Such specifications, which may contain errors due to the hallucination of LLMs, are used as a quasi-oracle to validate protocol parsers, while the validation results in return gradually refine the oracle. Since the oracle is derived from the document, any bugs we find in a protocol implementation can be traced back to the document, thus addressing the traceability problem. We have extensively evaluated our approach using nine network protocols and their implementations written in C, Python, and Go. The results show that our approach outperforms the state-of-the-art and has detected 69 bugs, with 36 confirmed. The project also demonstrates the potential for fully automating software validation based on natural language specifications, a process previously considered predominantly manual due to the need to understand specification documents and derive expected outputs for test inputs. CCS Concepts: • Networks → Protocol testing and verification; • Software and its engineering → Correctness; • Computing methodologies → Natural language processing.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers8
- TAI3: Testing Agent Integrity in Interpreting User IntentShiwei Feng, Xiangzhe Xu, Xuan Chen, Kaiyuan Zhang et al.NeurIPS 2025 · 9 citations
- Towards More Accurate Static Analysis for Taint-Style Bug Detection in Linux KernelHaonan Li, Hang Zhang, Kexin Pei, Zhiyun QianASE 2025 · 5 citations
- RFCAudit: AI Agent for Auditing Protocol Implementations Against RFC SpecificationsMingwei Zheng, Chengpeng Wang, Xuwei Liu, Jinyao Guo et al.ASE 2025 · 5 citations
- ProtocolGuard: Detecting Protocol Non-compliance Bugs via LLM-guided Static Analysis and Dynamic VerificationXiangpu Song, Longjia Pei, Jianliang Wu, Yingpei Zeng et al.NDSS 2026 · 3 citations
- A Tale of 1001 LoC: Potential Runtime Error-Guided Specification Synthesis for Verifying Large-Scale ProgramsZhongyi Wang, Tengjie Lin, Mingshuai Chen, Haokun Li et al.OOPSLA 2026 · 1 citation
Builds on25
- Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language ModelsYinlin Deng, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang et al.ISSTA 2023 · 253 citations
- Impact of Code Language Models on Automated Program RepairNan Jiang, Kevin Liu, Thibaud Lutellier, Lin TanICSE 2023 · 164 citations
- Large Language Models are Few-shot Testers: Exploring LLM-based General Bug ReproductionSungmin Kang, Juyeon Yoon, Shin YooICSE 2023 · 163 citations
- Fuzz4All: Universal Fuzzing with Large Language ModelsChunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian, Michael Pradel et al.ICSE 2024 · 155 citations
- Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated ApproachHaonan Li, Yu Hao, Yizhuo Zhai, Zhiyun QianOOPSLA 2024 · 142 citations
Related papers
- Generating Precise Format Specification for Network Protocols Through Adversarial LLM InteractionsHengdi Ye, Bing Shui, Jielun Wu, Yufan Zhou et al.USENIX Security 2026
- LLMs Unleashed: Generating Protocol Code from RFC SpecificationsJunfeng Long, Jinshu Su, Biao HanAAAI 2026
- SemFuzz: A Semantics-Aware Fuzzing Framework for Network Protocol ImplementationsYanbang Sun, Quan Luo, Yuelin Wang, Qian Chen et al.WWW 2026
- Large Language Model guided Protocol FuzzingRuijie Meng, Martin Mirchev, Marcel Böhme, Abhik RoychoudhuryNDSS 2024
- Beyond Static Pattern Matching? Rethinking Automatic Cryptographic API Misuse Detection in the Era of LLMsYifan Xia, Zichen Xie, Peiyu Liu, Kangjie Lu et al.ISSTA 2025 · 2 citations
