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AutoAudit—— the LLM for Cyber Security 网络安全大语言模型
| Date | Stars |
|---|---|
| 2026-07-31 | 355 |
| 2026-08-06 | 355 |
Today
— stars today
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— stars this week
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— stars this month
Momentum
0.0
growth rate 0.00%/day
# AutoAudit-LLM for Cybersecurity
[**🇨🇳中文**](./README_EN.md) | [**🌐English**](./README.md) | [**📖文档/Wiki**](https://github.com/ddzipp/AutoAudit/wiki) | [**❓提问/Issues**](https://github.com/ddzipp/AutoAudit/issues) | [**💬讨论/Discussions**](https://github.com/ddzipp/AutoAudit/discussions)
<div align="center">
<a href="https://github.com/ddzipp/AutoAudit">
<img src="https://github.com/ddzipp/AutoAudit/blob/main/images/logo.png" width="50%">
</a>
<p align="center">
<a href="https://github.com/ddzipp/AutoAudit/graphs/contributors">
<img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/ddzipp/AutoAudit" />
</a>
<a href="https://github.com/ddzipp/AutoAudit/issues">
<img alt="Issues" src="https://img.shields.io/github/issues/ddzipp/AutoAudit?color=0088ff" />
</a>
<a href="https://github.com/ddzipp/AutoAudit/stargazers">
<img alt="GitHub stars" src="https://img.shields.io/github/stars/ddzipp/AutoAudit?color=ccf" />
</a>
<br/>
<em>安全 / 审计 / 便捷 </em>
<br/>
<a href="https://huggingface.co/lilBuffaloEric/autoaudit_20230703_attempt1"><strong>HuggingFace下载</strong></a>
</p>
</p>
</div>
# AutoAudit Series Model
- [AutoAudit-7B](https://huggingface.co/lilBuffaloEric/autoaudit_20230714_attempt2), this version is a demo version trained based on [Alpaca-Lora](https://github.com/tloen/alpaca-lora). It performs admirably in the field of network security, yielding satisfactory responses in English. However, it currently lacks contextual understanding and requires a larger parameter model to address this limitation.
- [AutoAudit-8B-Instruct](https://huggingface.co/dzip/Llama3_8B_4Cybersecurity), this version is fine-tuned based on [Llama3-8B-instruct](https://github.com/meta-llama/llama3), and its performance in answering cybersecurity-related queries has significantly improved. The model's foundational capabilities have also seen a substantial enhancement compared to AutoAudit-7B.
- AutoAudit-Qwen, due to the limited availability of Chinese cybersecurity corpus, this model is still in the exploration and planning stage.
- More LLMs are coming soon
## Brief Introduction
This Projects explores the application of Large Language Models (LLMs) within cybersecurity, driven by the domain’s complexity and critical need for robust defense mechanisms. **Cybersecurity encompasses diverse areas such as operating systems, network protocols, malware analysis, and threat detection**. As cyber threats grow in sophistication and scale, LLMs present a promising avenue to enhance threat detection, analysis, and response through advanced language processing capabilities. Their ability to interpret, generate, and synthesize vast amounts of data positions LLMs as a transformative tool for addressing complex cybersecurity challenges.
Compared to traditional methods, LLMs can be fine-tuned to adapt to the ever-changing threat landscape, providing cross-domain knowledge connections and actionable emergency responses. They can also automate repetitive analytical tasks, integrate data from multiple sources, and generate risk assessments that capture subtle threats, thereby improving the adaptability and resilience of security systems. Therefore, LLMs play a crucial role in strengthening cybersecurity defense frameworks, offering experts enhanced support and enabling more comprehensive and flexible protection.
For interactive convenience and to address practical security auditing scenarios, we have coupled the AutoAudit model with ClamAV to create a security scanning platform (with the frontend inspired by Bootstrap templates). If you wish to directly download the AutoAudit model, you can visit HuggingFace to obtain the weights.
## Agent Deployment and Usage
### Environment Setup
1. Download the contents of this repository to your local or remote server:
```
git clone [email protected]:ddzipp/AutoAudit.git
cd AutoAudiExcerpt of 9,743 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:65a5beafeb787668, topic:fine-tuning, topic:lora, topic:qlora
matched fp:65a5beafeb787668, topic:gpt, topic:llama