IRPFuzz: Fuzzing Industrial Robot Protocol via LLM-driven Traffic Semantic Analysis
Year: 2026
Type: conference-paper
Abstract: Industrial robots are widely used in modern factories, interacting with external systems via network protocols. Any vulnerabilities in these protocols could be exploited to control the robot, potentially disrupting production and even endangering human lives. Protocol fuzzing is commonly used to discover vulnerabilities in protocol implementation. However, existing fuzzers are inefficient due to the proprietary nature and complex state relationships of industrial robot protocols.In this paper, we present IRPFuzz, a state-aware fuzzer for industrial robot protocol. By integrating large language models (LLMs) to analyze network traffic, IRPFuzz infers robot states and request templates, enabling the automatic construction of state model and data model for efficient state-aware fuzzing and structured message mutation. Evaluated on a real robot, IRPFuzz discovered 36 crashes, outperforming boofuzz by 157.14%, PCFuzzer by 89.47%, and MSGFuzzer by 16.13%. Five of these crashes were confirmed and assigned vulnerability IDs, including three classified as high-severity, which demonstrates the effectiveness of IRPFuzz.
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7220847299