LLM Prompt Injection Detector
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Updated
Mar 21, 2024 - TypeScript
LLM Prompt Injection Detector
The Security Toolkit for LLM Interactions
🔍 LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). 📚 Extracts signals from prompts & responses, ensuring safety & security. 🛡️ Features include text quality, relevance metrics, & sentiment analysis. 📊 A comprehensive tool for LLM observability. 👀
automatically tests prompt injection attacks on ChatGPT instances
Advanced Code and Text Manipulation Prompts for Various LLMs. Suitable for Siri, GPT-4o, Claude, Llama3, Gemini, and other high-performance open-source LLMs.
💼 another CV template for your job application, yet powered by Typst and more
⚡ Vigil ⚡ Detect prompt injections, jailbreaks, and other potentially risky Large Language Model (LLM) inputs
Every practical and proposed defense against prompt injection.
Self-hardening firewall for large language models
Prompts of GPT-4V & DALL-E3 to full utilize the multi-modal ability. GPT4V Prompts, DALL-E3 Prompts.
Dropbox LLM Security research code and results
ChatGPT Jailbreaks, GPT Assistants Prompt Leaks, GPTs Prompt Injection, LLM Prompt Security, Super Prompts, Prompt Hack, Prompt Security, Ai Prompt Engineering, Adversarial Machine Learning.
This repository provides implementation to formalize and benchmark Prompt Injection attacks and defenses
prompt attack-defense, prompt Injection, reverse engineering notes and examples | 提示词对抗、破解例子与笔记
A benchmark for prompt injection detection systems.
A prompt injection game to collect data for robust ML research
My inputs for the LLM Gandalf made by Lakera
Build production ready apps for GPT using Node.js & TypeScript
Website Prompt Injection is a concept that allows for the injection of prompts into an AI system via a website's. This technique exploits the interaction between users, websites, and AI systems to execute specific prompts that influence AI behavior.
A Python package designed to detect prompt injection in text inputs utilizing state-of-the-art machine learning models from Hugging Face. The main focus is on ease of use, enabling developers to integrate security features into their applications with minimal effort.
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