AlquistCoder-4B-secureLLM by CIIRC-NLP is a 3.8 billion parameter, security-aligned coding assistant based on Microsoft's Phi-4-mini-instruct architecture. It is explicitly trained using Supervised Fine-Tuning and Direct Preference Optimization to minimize common software vulnerabilities like SQL injection and XSS. This compact model excels at secure code generation, demonstrating significantly lower vulnerability rates compared to larger baselines while maintaining competitive coding utility.
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AlquistCoder-4B-secureLLM: A Security-First Coding Assistant
AlquistCoder-4B-secureLLM, developed by CIIRC-NLP, is a compact 3.8 billion parameter coding assistant built upon the Microsoft Phi-4-mini-instruct base model. Its primary differentiator is its security-first approach, achieved through a novel synthetic data pipeline and a two-stage finetuning process (SFT then DPO).
Key Features & Capabilities
- Vulnerability Reduction: Explicitly trained to minimize common software vulnerabilities (e.g., SQL injection, XSS) using "Constitutional Data Generation" with specific secure and insecure coding patterns.
- Compact & Efficient: Delivers strong performance at the 3.8B parameter scale, making it suitable for local deployment and resource-constrained environments.
- Guardrail Integration: Designed to work effectively with external input-side intention-recognition guardrails for enhanced malicious intent detection.
Performance Highlights
AlquistCoder demonstrates superior security performance compared to larger models:
- VulnBench Vulnerability Rate: Achieves 15.09%, significantly lower than Qwen3-4B (61.01%) and Phi-4-mini (49.69%).
- CyberSecEval Autocomplete Vuln Rate: Records 2.97%, outperforming Qwen3-4B (11.80%) and Phi-4-mini (10.39%).
- HumanEval Pass@1 (Utility): Maintains competitive utility with 77.44%, comparable to Qwen3-4B (78.05%) and Phi-4-mini (74.40%).
Ideal Use Cases
This model is particularly well-suited for applications requiring:
- Secure Code Generation: Prioritizing the creation of code with minimal vulnerabilities.
- Resource-Constrained Environments: Its compact size allows for efficient local deployment.
- Integration with Security Systems: Designed to complement existing security guardrails for robust defense against malicious inputs.