#LLM Security

This study presents a comprehensive analysis of the largest in-the-wild jailbreak prompt collection from December 2022 to December 2023. Utilizing the JailbreakHub framework, it compiles data from platforms such as Reddit and Discord, focusing on the risks and behaviors linked to harmful language prompts. The dataset, containing over 15,000 prompts with 1,405 identified as jailbreak prompts, provides crucial insights into the vulnerabilities and possible safeguards of large language models.

The resource provides an extensive overview of security and privacy challenges in large language models (LLMs), focusing on vulnerabilities like prompt injection, remote code execution, and adversarial attacks. It explores various attack taxonomies, benchmarks, and the susceptibility of applications to these threats. Regular updates on GitHub and Notion make it an essential reference for researchers and developers aiming to protect LLM applications, outlining key risks and potential mitigation strategies.

Explore a curated collection of tools, papers, and resources focused on improving Large Language Model (LLM) security. This repository addresses various attack strategies, including white-box, black-box, and backdoor methods, along with defensive measures and platform evaluations. Delve into recent advancements and research on adversarial attacks and prompt injection. Learn about security tools such as Plexiglass and PurpleLlama aimed at testing and protecting LLMs. Access essential information for researchers, developers, and security professionals in the field of LLM security.