| Model Type | |
| Use Cases |
| Areas: | | Source code vulnerability detection, Security research |
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| Applications: | | Developer workflows, Code review processes, Standalone security tool |
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| Primary Use Cases: | | Identifying security vulnerabilities in source code |
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| Limitations: | | May not identify all vulnerabilities if multiple are present, Prone to false positives, Results should be verified by human experts, Affected by code complexity and context |
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| Considerations: | | Should be integrated within a broader security review process. |
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| Supported Languages | | Go (High), Python (High), C (High), C++ (High), Fortran (High), Ruby (High), Java (High), Kotlin (High), C# (High), PHP (High), Swift (High), JavaScript (High), TypeScript (High) |
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| Training Details |
| Data Sources: | | Proprietary dataset for vulnerability detection |
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| Methodology: | | Fine-tuned for vulnerability detection; trained using Parameter-Efficient Fine-Tuning (PEFT). |
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| Hardware Used: | |
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| Input Output |
| Input Format: | | Programming code snippets |
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| Accepted Modalities: | |
| Output Format: | | Textual analysis of vulnerabilities and quality issues |
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| Performance Tips: | | Best performance with appropriate input code snippet length. |
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