Popular GitHub Repositories for Malicious Smartcontracts

? ? Use ML to classify flows and packets as benign or malicious. ? ?

Slips, a free software behavioral Python intrusion prevention system (IDS/IPS) that uses machine learning to detect malicious behaviors in the network traffic. Stratosphere Laboratory, AIC, FEL, CVUT in Prague.

Raising the Cost of Malicious AI-Powered Image Editing

EVM AI Agent that helps users execute transactions, deploy and verify contracts, check wallet balance, and more - all on EVM compatible chains via friendly, chat-based interaction.

Desktop app that automatically scans and blocks malicious MCP traffic in AI apps like Cursor, Claude, VS Code and Windsurf.

Refund Swatter Lite significantly reduces malicious refund risks by processing Apple's CONSUMPTION_REQUEST notifications and sending detailed consumption data back to Apple within the required 12-hour window, enabling Apple to make informed decisions on refund requests.

Detect npm packages compromised in the Shai-Hulud 2.0 supply chain attack (Nov 2025). Scans for 790+ malicious packages, suspicious scripts, TruffleHog activity, SHA1HULUD runners, and secrets exfiltration. GitHub Action with SARIF support.

NLP model and tech for cyber security tasks

The official code of KDD22 paper "FLDetecotor: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients"

A Deep Learning framework that analyses Windows PE files to detect malicious Softwares.

Malicious actors often reuse code to deploy their malware, phishing website or CNC server. As a result, similiaries can be found on URLs path by inspecting internet traffic. Moreover, deep learning models or even regular ML model do not fit for inline deployment in terms of running performance. However, regexes ( or YARA rules ) can be deployed on a proxy and work in real time on all the traffic. LUDA can take a set of malicious and benign URLs and return a list of regexes ready to be deployed inline !

ExeShield AI detects malicious Windows executables using ML. Analyzes entropy, imports, and metadata for rapid classification, aiding incident response. Built with Python and scikit-learn.

This project investigates the security of large language models by performing binary classification of a set of input prompts to discover malicious prompts. Several approaches have been analyzed using classical ML algorithms, a trained LLM model, and a fine-tuned LLM model.

MCP Security Framework for Agentic AI — real-time proxying, behavior analysis, and malicious tool detection

Detecting malicious URLs using an autoencoder neural network

DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing (ICLR 2025)

End to End Malicious URL Detection Project using Machine learning and deep learning

This project is a real-time deepfake detection system implemented in PyTorch. Deepfakes are manipulated videos or images that use artificial intelligence to swap faces or modify visual content, often with malicious intent. The goal of this project is to develop a system capable of detecting deepfake videos in real-time.

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