A new method has been developed to fine-tune LLMs for efficiently classifying Common Vulnerabilities and Exposures (CVEs). This approach transforms a standard LLM into a decision model by adding specific classification heads for severity, attack vector, and remediation SLA. Training on a small dataset (1,000 examples) for under 10 minutes on a single RTX 3090 GPU significantly improved accuracy from 29% to over 75% on a validation set. The resulting model processes CVEs 13 times faster than a standard LLM, enabling real-time analysis in CI/CD pipelines and reducing alert fatigue through calibrated confidence scores. AI
IMPACT Accelerates security analysis in CI/CD pipelines by enabling LLMs to process vulnerabilities 13x faster.
RANK_REASON Novel method for fine-tuning LLMs for a specific classification task with performance metrics. [lever_c_demoted from research: ic=1 ai=1.0]
- CUDA
- CVSS v3.1
- FastLanguageModel
- GitHub Actions
- GitLab CI
- Jevíčko
- LoRA+
- PyTorch
- Qwen3.5 4B
- RTX 3090
- Trivy
- Unsloth
- vLLM Decision 3.0
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