🐢 Open-Source Evaluation & Testing framework for LLMs and ML models

LLM App templates for RAG, knowledge mining, and stream analytics. Ready to run with Docker,⚡in sync with your data sources.

The Security Toolkit for LLM Interactions

⚡ Vigil ⚡ Detect prompt injections, jailbreaks, and other potentially risky Large Language Model (LLM) inputs

An easy-to-use Python framework to generate adversarial jailbreak prompts.

Papers and resources related to the security and privacy of LLMs 🤖

Prompt injection attacks and defenses in LLM-integrated applications

The fastest && easiest LLM security and privacy guardrails for GenAI apps.

AI-driven Threat modeling-as-a-Code (TaaC-AI)

intents engine

Agentic LLM Vulnerability Scanner

LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins

This project investigates the security of large language models by performing binary classification of a set of input prompts to discover malicious prompts. Several approaches have been analyzed using classical ML algorithms, a trained LLM model, and a fine-tuned LLM model.

Risks and targets for assessing LLMs & LLM vulnerabilities

Framework for LLM evaluation, guardrails and security

LLM security and privacy

A benchmark for prompt injection detection systems.

SecGPT: An execution isolation architecture for LLM-based systems

Whispers in the Machine: Confidentiality in LLM-integrated Systems

MER is a software that identifies and highlights manipulative communication in text from human conversations and AI-generated responses. MER benchmarks language models for manipulative expressions, fostering development of transparency and safety in AI. It also supports manipulation victims by detecting manipulative patterns in human communication.