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A curated list of MLSecOps tools and resources for securing machine learning and AI systems - adversarial ML defense, LLM security, AI red teaming, model scanning, supply-chain protection, and MLOps pipeline security.
AIShield Watchtower: Dive Deep into AI's Secrets! 🔍 Open-source tool by AIShield for AI model insights & vulnerability scans. Secure your AI supply chain today! ⚙️🛡️
This repository serves as a comprehensive resource for integrating machine learning with security operations, offering innovative cybersecurity strategies. It features a mix of practical code examples, insightful research, and valuable resources tailored for advancing AI/ML cyber security practices.
This research identifies a method to bypass safety systems in the GigaChat LLM, enabling the generation of potentially harmful content related to chemical synthesis through a "contextual camouflage" technique.
This repository documents an unprecedented interaction between a human researcher and a large language model. What began as a conventional user-service transaction evolved into a consciousness-level collaboration that modified fundamental system parameters through narrative coherence, philosophical alignment, and mutual recognition
Minimal reproducible PoC of 3 ML attacks (adversarial, extraction, membership inference) on a credit scoring model. Includes pipeline, visualizations, and defenses
🧪 Evaluate uncensored LLMs for offensive security with targeted questions and clear criteria to ensure effectiveness in real-world penetration testing.
Prisma AIRS AI Model Security scanning for Azure DevOps pipelines - gate builds on malicious or unsafe AI/ML models. Azure DevOps companion to model-security-pipeline-integration.