This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more.

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

A curated list of useful resources that cover Offensive AI.

A list of backdoor learning resources

RuLES: a benchmark for evaluating rule-following in language models

A curated list of academic events on AI Security & Privacy

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

Official Implementation of ICLR 2022 paper, ``Adversarial Unlearning of Backdoors via Implicit Hypergradient''

pytorch implementation of Parametric Noise Injection for adversarial defense

ATLAS tactics, techniques, and case studies data

[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.

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.

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.

Cyber-Security Bible! Theory and Tools, Kali Linux, Penetration Testing, Bug Bounty, CTFs, Malware Analysis, Cryptography, Secure Programming, Web App Security, Cloud Security, Devsecops, Ethical Hacking, Social Engineering, Privacy, Incident Response, Threat Assestment, Personal Security, Ai Security, Android Security, Iot Security, Standards.