VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilog
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917414367657984 |
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| author | Roy, Prithwish Basu Wang, Zeng Chuvashlov, Anatolii Xiao, Weihua Knechtel, Johann Sinanoglu, Ozgur Karri, Ramesh |
| author_facet | Roy, Prithwish Basu Wang, Zeng Chuvashlov, Anatolii Xiao, Weihua Knechtel, Johann Sinanoglu, Ozgur Karri, Ramesh |
| contents | Large Language Models (LLMs) have shown significant improvement in RTL code generation. Despite the advances, the generated code is often riddled with common vulnerabilities and weaknesses (CWEs) that can slip by untrained eyes. Attackers can often exploit these weaknesses to fulfill their nefarious motives. Existing RTL bug-detection techniques rely on rule-based checks, formal properties, or coarse-grained structural analysis, which either fail to capture semantic vulnerabilities or lack precise localization. In our work, we bridge this gap by proposing an embedding-based bug-detection framework that detects and classifies bugs at both module and line-level granularity. Our method achieves about 89% precision in identifying common CWEs such as CWE-1244 and CWE-1245, and 96% accuracy in detecting line-level bugs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15375 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilog Roy, Prithwish Basu Wang, Zeng Chuvashlov, Anatolii Xiao, Weihua Knechtel, Johann Sinanoglu, Ozgur Karri, Ramesh Hardware Architecture Artificial Intelligence Cryptography and Security Large Language Models (LLMs) have shown significant improvement in RTL code generation. Despite the advances, the generated code is often riddled with common vulnerabilities and weaknesses (CWEs) that can slip by untrained eyes. Attackers can often exploit these weaknesses to fulfill their nefarious motives. Existing RTL bug-detection techniques rely on rule-based checks, formal properties, or coarse-grained structural analysis, which either fail to capture semantic vulnerabilities or lack precise localization. In our work, we bridge this gap by proposing an embedding-based bug-detection framework that detects and classifies bugs at both module and line-level granularity. Our method achieves about 89% precision in identifying common CWEs such as CWE-1244 and CWE-1245, and 96% accuracy in detecting line-level bugs. |
| title | VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilog |
| topic | Hardware Architecture Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2604.15375 |