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This repository is a collection of specialized toolsets and libraries for large language model prompt engineering and security testing. It provides a library of advanced templates and frameworks designed to optimize the quality and specificity of model responses. The project includes resources for red teaming and security research, featuring a repository of prompts designed to bypass safety filters and operational constraints. It also provides techniques for system prompt extraction to reveal the internal instructions and configurations of AI personas. The collection covers a broader surface
Official repository for our ICML 2024 paper "On Prompt-Driven Safeguarding for Large Language Models"
This repository contains the code for the paper "Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing". The focus of this work is to enhance the resilience of large language models (LLMs) against jailbreak attacks through a novel method termed Layer-specific…
The main features of ledllm/ledllm are: Jailbreak Defenses.
Projects with overlapping indexed features include: cyberalbsecop/awesome_gpt_super_prompting — This repository is a collection of specialized toolsets and libraries for large language model prompt engineering and… neerazz/embedguard — Most RAG defenses sit at one layer — the input prompt, or the retrieved document. EmbedGuard correlates signals across… declare-lab/red-instruct — Paper | Github | Dataset | Model. jayfeather1024/backdoor-enhanced-alignment — This is the official code repository for the paper BackdoorAlign: Mitigating Fine-tuning based Jailbreak Attack with… chujiezheng/llm-safeguard — Official repository for our ICML 2024 paper "On Prompt-Driven Safeguarding for Large Language Models". amadeuszhao/qmllm.
