
ml-CVE-2023-1177
Machine learning-based exploit for CVE-2023-1177 vulnerability, enabling automated security testing and exploitation of the identified flaw.

Machine learning-based exploit for CVE-2023-1177 vulnerability, enabling automated security testing and exploitation of the identified flaw.

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

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

A Java Burp Plugin that performs text clustering on responses to identify outliers/groups based on the actual content of the server responses, say…

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

AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning

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

GyoiThon is a growing penetration test tool using Machine Learning.

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

Droidefense: Advance Android Malware Analysis Framework

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

Malware detection using learning and information retrieval for Android

A machine learning malware analysis framework for Android apps.

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

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

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

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