offensive-ai-compilation
AI defense resources
A curated collection of resources and countermeasures to protect artificial intelligence systems from attacks
A curated list of useful resources that cover Offensive AI.
1k stars
27 watching
118 forks
Language: HTML
last commit: almost 2 years agoadversarial-machine-learningai-securityartificial-intelligencecompilationoffensive-ai
Related projects:
| Repository | Description | Stars |
|---|---|---|
| A collection of notes on hardware vulnerability assessment and exploitation | 491 | |
| An IDA PRO plugin to analyze binaries for potential vulnerabilities using AI-powered decompilation and static analysis | 353 | |
| A Docker image with pre-installed tools for creating and running penetration testing environments. | 732 | |
| Preparation and exploitation research for various web applications | 572 | |
| A platform providing automated virtualization environments for security education and vulnerability testing | 188 | |
| An educational API project designed to demonstrate various vulnerabilities and security flaws in a web application. | 32 | |
| A repository of threat intelligence data from public Volexity blog posts. | 342 | |
| An offensive information and vulnerability scanner that identifies potential security issues in web applications | 2,238 | |
| A toolbox for researching and evaluating robustness against attacks on machine learning models | 1,311 | |
| A collection of learning resources and case studies to prepare for an Offsec Web Security exam, focusing on vulnerability research and exploitation. | 37 | |
| A tool for identifying potential vulnerabilities in websites by fetching known URLs and filtering out ones with open redirects or SSRF parameters. | 168 | |
| Automated unit test generation and mutation testing tool using Large Language Models. | 252 | |
| An AI-driven tool for analyzing service descriptions and identifying security threats. | 118 | |
| An adversarial attack framework on large vision-language models | 165 | |
| An open-source tool for evaluating web application vulnerabilities by analyzing the separation of concerns in web applications. | 232 |