A QLoRA fine-tuned adapter on top of CodeLlama-7b-instruct-hf, trained to detect security vulnerabilities in API endpoint source code. Given a raw code snippet, the model produces a structured analysis identifying vulnerability type, severity, CWE, and a remediated version of the code.

Model Details

Property	Value
Base Model	`codellama/CodeLlama-7b-instruct-hf`
Fine-tuning Method	QLoRA (4-bit NF4 quantization)
LoRA Rank (r)	16
LoRA Alpha	32
LoRA Dropout	0.05
Target Modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`
Task	Causal LM / Code Security Analysis
Training Steps	531
Training Hardware	Google Colab T4 (16GB VRAM)

Training Data

Fine-tuned on a custom dataset of 10,000 API-specific vulnerability samples (synthetic + augmented) covering 19 vulnerability types mapped to OWASP API Top 10.

Language Distribution

Language	Share	Frameworks
Python	46%	Flask, FastAPI, Django
JavaScript	25%	Express.js, NestJS
Java	15%	Spring Boot
PHP / Go / Ruby / C#	14%	Laravel, Gin, Rails, ASP.NET

Vulnerability Distribution

Vulnerability	Samples	CWE
SQL Injection	2,425	CWE-89
Mass Assignment	1,307	CWE-915
Path Traversal	943	CWE-22
IDOR	860	CWE-639
Broken Authorization	792	CWE-285
Command Injection	600	CWE-78

Severity Breakdown

Critical (43%): RCE, SQLi, unauthorized admin access
High (41%): Data leaks, IDOR, authorization bypass
Medium / Clean (16%): XSS, input validation warnings, baseline clean samples

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model_id = "codellama/CodeLlama-7b-instruct-hf"
adapter_id    = "harsharajkumar273/api-security-qlora"

tokenizer = AutoTokenizer.from_pretrained(adapter_id, use_fast=False)

base = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, adapter_id)
model.eval()

code_snippet = """
@app.route('/user/<int:user_id>')
def get_user(user_id):
    query = f"SELECT * FROM users WHERE id = {user_id}"
    result = db.execute(query)
    return jsonify(result)
"""

prompt = f"[INST] Analyze this API endpoint for security vulnerabilities:\n\n{code_snippet} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Integration with API Security Scanner

This adapter is the default model in the API Security Scanner project. It is loaded automatically — no manual path configuration needed:

bash

git clone https://github.com/harsharajkumar/api-security
cd api-security
pip install -r requirements.txt
streamlit run app.py

The scanner will download this adapter from the Hub on first run and cache it locally.

Intended Use

Automated API security auditing in CI/CD pipelines
Developer tooling for identifying vulnerable endpoint patterns
Security research and OWASP API Top 10 education

Out of Scope

General-purpose code generation
Non-API code (UI components, data processing scripts, etc.)
Production security decisions without human review

Credits

Developed as part of CS6380 — API Security Project

Authors: Siddhanth Nilesh Jagtap · Tanuj Kenchannavar · Harsha Raj Kumar