SecGPT is an open-source large language model specifically designed for cybersecurity tasks, aiming to enhance security defense efficiency and effectiveness through AI. It targets cybersecurity professionals, researchers, and engineers, offering an intelligent assistant for various security operations.

How It Works

SecGPT integrates natural language understanding, code generation, and security knowledge reasoning. It is built upon foundational models like Qwen2.5-Instruct and DeepSeek-R1, enhanced through extensive pre-training, instruction fine-tuning, and reinforcement learning on a proprietary, large-scale cybersecurity corpus (over 5TB). This approach aims to significantly improve the model's comprehension, reasoning, and response capabilities in specialized security contexts.

Quick Start & Requirements

• Installation: Deploy using vLLM. Requires Python 3.10+ and PyTorch with CUDA support.
• Commands:

  conda create -n secgpt-vllm python=3.10 -y
  conda activate secgpt-vllm
  pip install --upgrade pip
  pip install vllm
  CUDA_VISIBLE_DEVICES= xxx(GPU index) vllm serve ./secgpt --tokenizer ./secgpt --tensor-parallel-size 4 --max-model-len 32768 --gpu-memory-utilization 0.9 --dtype bfloat16
  

• Resources: Requires GPU(s) with CUDA. Specific GPU memory utilization is set to 0.9.
• Links:
  • Source & Docs: https://github.com/Clouditera/secgpt
  • Models: https://huggingface.co/clouditera/secgpt, https://modelscope.cn/models/clouditera/SecGPT-14B
  • Datasets: https://huggingface.co/datasets/clouditera/security-paper-datasets

Highlighted Details

• Achieves significant performance gains across security-specific benchmarks (CISSP, CS-EVAL) and general capabilities (CEVAL, GSM8K, BBH) compared to base models.
• Demonstrates advanced capabilities in vulnerability analysis, log/traffic analysis, threat hunting, code auditing, and reverse engineering.
• Trained on a 5TB+ cybersecurity corpus, including structured data with 70+ fields and 14 categories, covering theoretical, adversarial, and applied security knowledge.
• Offers a lightweight SecGPT-Mini version capable of running efficiently on CPUs.

Maintenance & Community

• Actively developed by Clouditera.
• Community engagement is encouraged via GitHub for suggestions, issue reporting, code contributions, and experience sharing.

Licensing & Compatibility

• The specific license is not explicitly stated in the README, but the project is presented as open-source for research and exchange.
• A disclaimer notes that public release or commercial deployment requires users to assume legal and compliance responsibilities.

Limitations & Caveats

• The model's output is subject to the limitations of its training data coverage and requires user judgment for accuracy and applicability.
• The developers disclaim responsibility for any direct or indirect damages arising from the model's use.