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)
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 |