🐢 Open-Source Evaluation & Testing for LLMs and ML models
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Updated
Jun 30, 2024 - Python
🐢 Open-Source Evaluation & Testing for LLMs and ML models
RuLES: a benchmark for evaluating rule-following in language models
The official implementation of the CCS'23 paper, Narcissus clean-label backdoor attack -- only takes THREE images to poison a face recognition dataset in a clean-label way and achieves a 99.89% attack success rate.
Code for "Adversarial attack by dropping information." (ICCV 2021)
Train AI (Keras + Tensorflow) to defend apps with Django REST Framework + Celery + Swagger + JWT - deploys to Kubernetes and OpenShift Container Platform
Performing website vulnerability scanning using OpenAI technologie
pytorch implementation of Parametric Noise Injection for adversarial defense
[IJCAI 2024] Imperio is an LLM-powered backdoor attack. It allows the adversary to issue language-guided instructions to control the victim model's prediction for arbitrary targets.
ATLAS tactics, techniques, and case studies data
Learning to Identify Critical States for Reinforcement Learning from Videos (Accepted to ICCV'23)
Python library for Modzy Machine Learning Operations (MLOps) Platform
Unofficial pytorch implementation of paper: Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
The Prompt Injection Testing Tool is a Python script designed to assess the security of your AI system's prompt handling against a predefined list of user prompts commonly used for injection attacks. This tool utilizes the OpenAI GPT-3.5 model to generate responses to system-user prompt pairs and outputs the results to a CSV file for analysis.
Image Prompt Injection is a Python script that demonstrates how to embed a secret prompt within an image using steganography techniques. This hidden prompt can be later extracted by an AI system for analysis, enabling covert communication with AI models through images.
The official implementation of USENIX Security'23 paper "Meta-Sift" -- Ten minutes or less to find a 1000-size or larger clean subset on poisoned dataset.
Evaluation & testing framework for computer vision models
Datasets for training deep neural networks to defend software applications
Neural networks, but malefic! 😈
AntiNex python client for training and using pre-trained deep neural networks with JWT authentication
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