| --- |
| library_name: peft |
| tags: |
| - codeguard |
| - security |
| - qwen2.5 |
| - lora |
| - code-analysis |
| - vulnerability-detection |
| - cwe |
| - sql-injection |
| - command-injection |
| - hardcoded-secrets |
| - insecure-deserialization |
| - xxe |
| - path-traversal |
| - ssrf |
| - deserialization |
| license: mit |
| base_model: Qwen/Qwen2.5-7B-Instruct |
| language: |
| - en |
| metrics: |
| - accuracy |
| pipeline_tag: text-generation |
| --- |
| |
| # CodeGuard Security 7B |
|
|
| LoRA adapter fine-tuned on Qwen 2.5 7B Instruct for **code vulnerability detection**. Trained on 32 security patterns across 8 vulnerability categories to identify and explain security flaws in source code. |
|
|
| ## Vulnerabilities Detected |
|
|
| | Category | CWE | Severity | |
| |----------|-----|----------| |
| | SQL Injection | CWE-89 | Critical | |
| | Command Injection | CWE-78 | Critical | |
| | Hardcoded Secrets | CWE-798 | Critical | |
| | Insecure Deserialization | CWE-502 | Critical | |
| | XML External Entity (XXE) | CWE-611 | High | |
| | Path Traversal | CWE-22 | High | |
| | Server-Side Request Forgery | CWE-918 | High | |
| | Unsafe Deserialization | CWE-502 | High | |
|
|
| ## Dataset |
|
|
| Trained on curated code security examples from real-world vulnerability disclosures, bug bounty reports, and secure code review patterns. Covers OWASP Top 10, CWE Top 25, and SANS 25. No synthetic or GPT-generated data. |
|
|
| ## How to use |
|
|
| ```python |
| from peft import PeftModel |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| |
| base_model = AutoModelForCausalLM.from_pretrained( |
| "Qwen/Qwen2.5-7B-Instruct", |
| torch_dtype=torch.float16, |
| device_map="auto" |
| ) |
| model = PeftModel.from_pretrained(base_model, "NiffyHunt90/codeguard-security-7b") |
| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct") |
| |
| code = ''' |
| query = "SELECT * FROM users WHERE id = " + user_input |
| cursor.execute(query) |
| ''' |
| prompt = f"Analyze this code for security vulnerabilities:\n{code}" |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=200) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| ``` |
|
|
| ## Training |
|
|
| - **Base model:** Qwen 2.5 7B Instruct |
| - **Method:** LoRA |
| - **Adapter size:** 154 MB |
| - **Hardware:** 2x Tesla T4 (14.5GB VRAM) |
| - **Framework:** Unsloth + HuggingFace TRL |
|
|
| ## Related models |
|
|
| - [WraithWall Core V3](https://huggingface.co/NiffyHunt90/wraithwall-core-v3) — full security operations model |
| - [WraithCore 7B](https://huggingface.co/NiffyHunt90/wraithcore-7b) — lightweight 616MB security adapter |
|
|
| ## Author |
|
|
| **Adewale Babalola (Niffyhunt)** — Founder, WraithWall |
| - [niffyhunt.online](https://niffyhunt.online) |
| - [wraithwall.online](https://wraithwall.online) |
|
|
