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 ML & LLM systems

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

Toolkits to create a human-in-the-loop approval layer to monitor and guide AI agents workflow in real-time.

[CCS'24] SafeGen: Mitigating Unsafe Content Generation in Text-to-Image 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

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

ATLAS tactics, techniques, and case studies data

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.

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.

[NDSS'24] Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech in Real Time

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.

This repository provide the studies on the security of language models for code (CodeLMs).