
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…

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

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

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