finsec_detector

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.

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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:

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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.

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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)

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Model Details