| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen2.5-Coder-7B-Instruct |
| library_name: unsloth |
| tags: |
| - cybersecurity |
| - vulnerability-detection |
| - cve |
| - code-audit |
| - code-repair |
| - qwen2.5-coder |
| - fine-sec |
| language: |
| - en |
| - code |
| pipeline_tag: text-generation |
| --- |
| |
| # FineSec-Detector: Specialized Security LLM (Qwen2.5-Coder-7B-Instruct) |
|
|
| **FineSec-Detector** is a 7B parameter specialized cybersecurity Large Language Model fine-tuned on high-precision CVE vulnerability reports, real-world exploit benchmarks, and secure code repair patterns using **Unsloth 4-bit QLoRA**. |
|
|
| The model acts as an automated Senior Application Security (AppSec) Auditor. It audits source code across 9 programming languages, identifies vulnerabilities, classifies severity and CWE IDs, and produces ready-to-merge secure code patches in structured JSON. |
|
|
| --- |
|
|
| ## Verified Benchmark Performance |
|
|
| Evaluating **FineSec-Detector** on multi-language vulnerability benchmarks (SQL Injection, RCE, XSS, Path Traversal, Insecure Deserialization, Buffer Overflows) yielded the following performance metrics: |
|
|
| | Metric | Score | Rating | Analysis | |
| |---|---|---|---| |
| | Precision Rate | 100.0% | Perfect | Zero false positives. Safe code is never misflagged. | |
| | Detection Recall | 83.3% | High | High-confidence detection across Python, C, JS, and Go. | |
| | F1 Rating Score | 90.9% | Outstanding | Superior overall vulnerability detection balance. | |
|
|
| --- |
|
|
| ## Key Features |
|
|
| - Automated Vulnerability Detection: Audits Python, C/C++, JavaScript, Go, PHP, Java, and Bash source code. |
| - Structured JSON Output: Produces standardized security reports suitable for CI/CD pipeline integration. |
| - CWE and Severity Classification: Classifies bugs into standard CWE categories (e.g., CWE-89 SQLi, CWE-79 XSS, CWE-78 RCE, CWE-120 Buffer Overflow) with CVSS-aligned severity levels (CRITICAL, HIGH, MEDIUM, LOW). |
| - Remediation and Patching: Generates diffs and secure code refactors directly replacing vulnerable logic. |
|
|
| --- |
|
|
| ## Quickstart: Inference |
|
|
| ### 1. Using Unsloth (Fast and Memory Efficient) |
|
|
| ```python |
| from unsloth import FastLanguageModel |
| |
| # Load model and tokenizer from Hugging Face Hub |
| model, tokenizer = FastLanguageModel.from_pretrained( |
| model_name = "elsiddik/finsec_detector", |
| max_seq_length = 1024, |
| load_in_4bit = True, |
| ) |
| FastLanguageModel.for_inference(model) |
| ``` |
|
|
| --- |
|
|
| ## Model Details |
|
|
| | Attribute | Details | |
| |---|---| |
| | Base Architecture | Qwen2.5-Coder-7B-Instruct | |
| | Fine-Tuning Method | QLoRA 4-bit (Unsloth) | |
| | Context Window | 1024 tokens | |
| | License | Apache-2.0 | |
|
|
