A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems
Compiler optimization is a critical component for improving program performance. However, the Rustc optimization process may introduce vulnerabilities due to algorithmic flaws or issues arising from component interactions. Existing testing methods face several challenges, including high randomness i...
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/15/2459 |
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| author | Kaifeng Xie Jinjing Wan Lifeng Chen Yi Wang |
| author_facet | Kaifeng Xie Jinjing Wan Lifeng Chen Yi Wang |
| author_sort | Kaifeng Xie |
| collection | DOAJ |
| description | Compiler optimization is a critical component for improving program performance. However, the Rustc optimization process may introduce vulnerabilities due to algorithmic flaws or issues arising from component interactions. Existing testing methods face several challenges, including high randomness in test cases, inadequate targeting of vulnerability-prone regions, and low-quality initial fuzzing seeds. This paper proposes a test case generation method based on large language models (LLMs), which utilizes prompt templates and optimization algorithms to generate a code relevant to specific optimization passes, especially for real-time control logic and safety-critical modules unique to the industrial control field. A vulnerability screening approach based on static analysis and rule matching is designed to locate potential risk points in the optimization regions of both the MIR and LLVM IR layers, as well as in unsafe code sections. Furthermore, the targeted fuzzing strategy is enhanced by designing seed queues and selection algorithms that consider the correlation between optimization areas. The implemented system, RustOptFuzz, has been evaluated on both custom datasets and real-world programs. Compared with state-of-the-art tools, RustOptFuzz improves vulnerability discovery capabilities by 16%–50% and significantly reduces vulnerability reproduction time, thereby enhancing the overall efficiency of detecting optimization-related vulnerabilities in Rustc, providing key technical support for the reliability of industrial control systems. |
| format | Article |
| id | doaj-art-cc45f0ffc198488abca38628ecc1bd4c |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-cc45f0ffc198488abca38628ecc1bd4c2025-08-20T04:00:54ZengMDPI AGMathematics2227-73902025-07-011315245910.3390/math13152459A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control SystemsKaifeng Xie0Jinjing Wan1Lifeng Chen2Yi Wang3Department of Anthropology and Human Genetics, Fudan University, Shanghai 200433, ChinaDepartment of Anthropology and Human Genetics, Fudan University, Shanghai 200433, ChinaDepartment of Anthropology and Human Genetics, Fudan University, Shanghai 200433, ChinaDepartment of Anthropology and Human Genetics, Fudan University, Shanghai 200433, ChinaCompiler optimization is a critical component for improving program performance. However, the Rustc optimization process may introduce vulnerabilities due to algorithmic flaws or issues arising from component interactions. Existing testing methods face several challenges, including high randomness in test cases, inadequate targeting of vulnerability-prone regions, and low-quality initial fuzzing seeds. This paper proposes a test case generation method based on large language models (LLMs), which utilizes prompt templates and optimization algorithms to generate a code relevant to specific optimization passes, especially for real-time control logic and safety-critical modules unique to the industrial control field. A vulnerability screening approach based on static analysis and rule matching is designed to locate potential risk points in the optimization regions of both the MIR and LLVM IR layers, as well as in unsafe code sections. Furthermore, the targeted fuzzing strategy is enhanced by designing seed queues and selection algorithms that consider the correlation between optimization areas. The implemented system, RustOptFuzz, has been evaluated on both custom datasets and real-world programs. Compared with state-of-the-art tools, RustOptFuzz improves vulnerability discovery capabilities by 16%–50% and significantly reduces vulnerability reproduction time, thereby enhancing the overall efficiency of detecting optimization-related vulnerabilities in Rustc, providing key technical support for the reliability of industrial control systems.https://www.mdpi.com/2227-7390/13/15/2459compiler optimization vulnerabilitiestest case generationstatic analysisdirected fuzz testingRustc |
| spellingShingle | Kaifeng Xie Jinjing Wan Lifeng Chen Yi Wang A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems Mathematics compiler optimization vulnerabilities test case generation static analysis directed fuzz testing Rustc |
| title | A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems |
| title_full | A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems |
| title_fullStr | A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems |
| title_full_unstemmed | A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems |
| title_short | A Comprehensive Approach to Rustc Optimization Vulnerability Detection in Industrial Control Systems |
| title_sort | comprehensive approach to rustc optimization vulnerability detection in industrial control systems |
| topic | compiler optimization vulnerabilities test case generation static analysis directed fuzz testing Rustc |
| url | https://www.mdpi.com/2227-7390/13/15/2459 |
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