Popular GitHub Repositories for Vulnerable

A deliberately vulnerable banking application designed for practicing Security Testing of Web App, APIs, AI integrated App and secure code reviews. Features common vulnerabilities found in real-world applications, making it an ideal platform for security professionals, developers, and enthusiasts to learn pentesting and secure coding practices.

Gain insights into MS-RPC implementations that may be vulnerable using an automated approach and make it easy to visualize the data. By following this approach, a security researcher will hopefully identify interesting RPC services in such a time that would take a manual approach significantly more.

CTF challenges designed and implemented in machine learning applications

Gain insights into COM/DCOM implementations that may be vulnerable using an automated approach and make it easy to visualize the data. By following this approach, a security researcher will hopefully identify interesting (D)COM classes/implementations in such a time that would take a manual approach significantly more.

AspGoat is an intentionally vulnerable ASP.NET Core application for learning and practicing web application security.

PromptMe is an educational project that showcases security vulnerabilities in large language models (LLMs) and their web integrations. It includes 10 hands-on challenges inspired by the OWASP LLM Top 10, demonstrating how these vulnerabilities can be discovered and exploited in real-world scenarios.

This repo contains the codes of the penetration test benchmark for Generative Agents presented in the paper "AutoPenBench: Benchmarking Generative Agents for Penetration Testing". It contains also the instructions to install, develop and test new vulnerable containers to include in the benchmark.

OWASP EKS Goat is a deliberately vulnerable EKS cluster environment to explore AWS cloud-native security through hands-on attack and defense labs with walkthrough.

PromptMe is an educational project that showcases security vulnerabilities in large language models (LLMs) and their web integrations. It includes 10 hands-on challenges inspired by the OWASP LLM Top 10, demonstrating how these vulnerabilities can be discovered and exploited in real-world scenarios.

Vulnerable LLM Application

An intentionally vulnerable AI chatbot to learn and practice AI Security.

Learn LLM/AI Security through a series of vulnerable LLM CTF challenges. No sign ups, all fees, everything on the website.

Machine learning algorithm in Python that can analyze soil data and determine if it's vulnerable to erosion or not.

A Jailbroken GenAI Model Can Cause Real Harm: GenAI-powered Applications are Vulnerable to PromptWares

Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by adding maliciously crafted perturbations to the benign inputs. As a growing number of attacks have been reported to generate adversarial inputs of varying sophistication, the defense-attack arms race has been accelerated. This project collects state-of-the-art attack algorithms and organizes them with a easy-to-use interface.

A Deep Q-Network based RL solution, namely IoTWarden, developed using TensorFlow, OpenAI Gym, and Python. Simulated a vulnerable IoT environment using Gym, where a defense agent optimally takes actions to block attack activities in real-time. Trained the RL model for 250 episodes, each containing 100 epochs.

Successfully established a machine learning model which can determine whether an individual is vulnerable to the Cirrhosis disease or not by predicting its corresponding stage based on a unique set of medical features such as Cholesterol, Prothrombin, etc. pertaining to that person.

Example to examine the process of compromising the extraction of the secret key Nonce value K from a vulnerable RawTX transaction using the BitcoinChatGPT machine learning process.

Intrinsic moral reflexes for AI systems — to detect and prevent cruelty, protect the vulnerable, and initiate human intervention (where available), before harm occurs.

Demonstrates effects of unsafe C functions by injecting a payload containing machine code to spawn a shell into a vulnerable function.