Why LLMs Fail: A Failure Analysis and Partial Success Measurement for Automated Security Patch Generation
Large Language Models (LLMs) show promise for Automated Program Repair (APR), yet their effectiveness on security vulnerabilities remains poorly characterized. This study analyzes 319 LLM-generated security patchesacross 64 Java vulnerabilities from the Vul4J benchmark. Using tri-axis evaluation (compilation, security via PoV tests, functionality via test suites), the analysis reveals that only 24.8% of patches achieve full correctness, while 51.4% fail both security and functionality. The dominant failure mode is semantic misunderstanding: LLMs produce syntactically valid code but apply incorrect repair strategies. The proposed Security Repair Score (SRS) quantifies this gap, showing LLM...
| Authors | Amir Al-Maamari |
|---|---|
| Primary category | cs.CR |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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