Open source. Open possibilities.

SWE-agent is an autonomous AI agent that uses language models to automatically fix GitHub issues, find cybersecurity vulnerabilities, and solve custom coding challenges. Built by Princeton and Stanford researchers.

Local-first, zero-egress forensics toolkit for detecting and sanitizing AI watermarks: Unicode steganography visualization, statistical text detectors, C2PA/EXIF/XMP cleaning, SARIF/CI audit and MCP agent tools.

An educational machine learning project that classifies online payment transactions as legitimate or fraudulent. It preprocesses a transaction dataset, encodes and scales features, then trains and evaluates Logistic Regression, Decision Tree, and Random Forest models with Python, pandas, NumPy, and scikit-learn.

Shannon is an open-source, autonomous AI pentester for web applications and APIs. It analyzes your source code to find attack paths, then executes real exploits against a running app, reporting only vulnerabilities proven with a working proof of concept.

Self-hosted security operations stack combining SIEM log collection, endpoint detection with Sigma rules and cross-event correlation, SOAR playbooks, OSV vulnerability scanning and a local Ollama model for AI triage, deployed with a single docker compose up.

Zentra is an AI-powered CLI security scanner for developers, combining SAST, DAST, supply-chain, API, and IaC analysis with LLM orchestration, CI integration, and an authorized pentest mode.

SUNGLASSES is a local, open-source input inspection layer for AI agents. It scans text, images, audio, video, PDFs and QR codes for prompt injection, credential exfiltration and command injection, and provides a CLI, Python API, MCP server and Claude Code firewall hook.