
SWE-agent
LLM-powered agent that autonomously fixes GitHub issues, finds cybersecurity vulnerabilities, and solves CTF challenges using configurable tool-use…

LLM-powered agent that autonomously fixes GitHub issues, finds cybersecurity vulnerabilities, and solves CTF challenges using configurable tool-use…

AI-driven CLI for testing Android and iOS apps using natural language. Generates, runs, and fixes end-to-end tests on emulators/simulators with…

Automated steganography detection tool that scans websites, web servers, and local directories using AI-driven object/text recognition and deep file…


Android deeplink misconfiguration detector and exploitation tool

Modular LLM vulnerability scanner that probes for hallucination, data leakage, prompt injection, jailbreaks, and toxicity using static, dynamic, and…

AI-powered penetration testing assistant using local LLM on linux (Parrot OS)

Zero shot vulnerability discovery using LLMs

Krawl is a customizable, lightweight, cloud-native web deception server and anti-crawler that creates fake web applications with low-hanging…

AI-powered offensive security testing using autonomous agents, directly in your terminal.

On-device AI browser automation using WebLLM. No cloud, no API keys, fully private.

A modular, skill-based autonomous Security Operations Center (SOC) agent that monitors OpenSearch/Elasticsearch data, builds RAG-based behavioral…

Redefining vulnerability disclosure in the AI era. We mass-produce exploitable 0days and disclose them directly, using event-driven pressure to…

Research implementation of an LLM jailbreak agent that bypasses commercial model guardrails using harmless prompt weaving and adaptive tree search.

Malware Mutation Using Reinforcement Learning and Generative Adversarial Networks

Vulnerability scanner for AWS customer managed policies using ChatGPT

Agentic jailbreak framework for LLM-based agents using scheme-based task decomposition, multi-turn disguising strategies, and adaptive self-evolution…

Demonstrates using machine learning to predict random number generator sequences, highlighting cryptographic weaknesses through adversarial analysis.