Awesome LLM Security
Overview
The "Awesome LLM Security" project is a curated collection dedicated to promoting the security of Large Language Models (LLMs). This repository serves as a resource hub, offering access to valuable tools, papers, articles, and projects focused on safeguarding LLMs against various security threats. It's designed for professionals, researchers, and enthusiasts who are invested in the exploration and enhancement of LLM security measures.
Contents and Categories
The project is thoughtfully organized into several categories, each delving into different aspects of LLM security:
Research Papers
This section is composed of groundbreaking papers categorized into distinct types of attacks and defenses:
- White-box Attack: Papers that disclose methods to exploit LLMs using internal knowledge of their architecture.
- Black-box Attack: Studies focusing on attacking LLMs without prior knowledge of internal workings, often using adaptive techniques.
- Backdoor Attack: Papers revealing vulnerabilities where malicious actors might implant backdoors into LLMs.
- Defense: Articles discussing strategies to bolster LLM resilience against various forms of attacks.
- Platform Security: Papers dedicated to ensuring the security of platforms hosting LLMs.
- Survey: Comprehensive surveys that provide insights into vulnerabilities and other security challenges faced by LLMs.
Tools
This section highlights various tools designed to assess and fortify the security of LLMs. Examples include:
- Plexiglass: A security toolbox tailored for testing LLM ensuring their robustness against attacks.
- PurpleLlama: A suite of tools intended to evaluate and strengthen the security measures of LLM systems.
- Rebuff and Garak: Tools focused on detecting vulnerabilities and guarding against prompt injection.
Articles
Articles in this section provide insights into real-world applications and implications of LLM security. Topics range from exploiting vulnerabilities in AI models to best practices in AI safety. Key reads include:
- "Prompt Injection Cheat Sheet" and "Hacking Auto-GPT" offer practical insights into the security flaws and preventative measures for LLMs.
Other Projects and Resources
The project also features links to other notable projects and programs related to LLM security, including bug bounty programs and competition highlights like "0din GenAI Bug Bounty" and "Gandalf" wargame for prompt injection challenges.
Moreover, additional resources like blogs, newsletters, and Twitter accounts provide continuous updates and expert commentary on LLM security trends.
Community and Contributions
The project thrives on community involvement, and contributions are welcome from anyone with insights or tools that could enhance LLM security. The guidelines for contributing ensure that all additions maintain high-quality standards, encouraging collaboration between experts and newcomers alike.
Conclusion
"Awesome LLM Security" stands as a comprehensive, ever-evolving repository dedicated to improving the safety and efficacy of Large Language Models. By gathering cutting-edge research, practical tools, and expert analyses, it empowers users to better understand and defend against the myriad security challenges facing modern AI systems.