
MADLIRA
Malware detection using learning and information retrieval for Android

Malware detection using learning and information retrieval for Android

C-based Android static analysis framework for decompilation, secret detection, endpoint discovery, permission analysis, and native library scanning…

GyoiThon is a growing penetration test tool using Machine Learning.

Trajectory-aware evolutionary search framework for red-teaming LLM agents over MCP servers, generating adversarial prompts to map vulnerability…

VEDAS-Driven Autonomous Generation + Community Contributions of Suricata & Nuclei Rules for over 15k CVEs

Pre-execution action-auditing defense that detects and masks indirect prompt injection in tool-using LLM agents using embedding retrieval and…

Android Antivirus which doesn't require root, adb, ca install and cloud with many features and ways to detect more zero-day malware

Curated reading list and taxonomy of attack and defense research for mobile on-device AI systems, covering adversarial, backdoor, model stealing, and…

Google Tensor Cracks

Privacy testing library for deep learning systems, enabling assessment of susceptibility to membership inference, model extraction, and model…

AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning

Hybrid ML and heuristic-based URL phishing detector with real-time analysis, explainable confidence scores, and REST API for programmatic security…

A machine learning malware analysis framework for Android apps.

Droidefense: Advance Android Malware Analysis Framework

Air-gapped cybersecurity assistant for security professionals. 100% offline AI-powered analysis tool for Nmap, Volatility, BloodHound, Metasploit,…

Image-based Android malware detection framework tackling obfuscation and concept drift. Includes curated datasets and Python code for training…

Memory-free continual learning framework for malware classification using mode connectivity-based interpolation. Supports class-incremental and…

Official code for the ISSTA 2026 paper: Is "Knowing It’s Malicious" Enough? Evaluating LLMs for Fine-Grained Malware Behavior Auditing