NiffyHunt90/codeguard-security-7b

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

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

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 — full security operations model
• —WraithCore 7B — lightweight 616MB security adapter

Author

Adewale Babalola (Niffyhunt) — Founder, WraithWall

• —niffyhunt.online
• —wraithwall.online