LLM | Security | Operations in one github repo with good links and pictures.

The Anti-Virus for AI Artifacts & RAG Firewall. A static analysis tool scanning Models and Notebooks for RCE, Datasets and RAG docs for Data Poisoning, PII, and Prompt Injections. Secure your AI Supply Chain.

RAG/LLM Security Scanner identifies critical vulnerabilities in AI-powered applications, including chatbots, virtual assistants, and knowledge retrieval systems.

Scanner for adversarial hubs in RAG and vector databases

Local RAG system with a built-in governance agent that filters sensitive or restricted information with separated agent logging systems to keep privacy and security

RAG Poisoning Lab — Educational AI Security Exercise

Complete roadmap to become an AI Security Engineer from zero to advanced — covering Python, ML, Deep Learning, LLM Engineering, RAG Security, Intrusion Detection, Anomaly Detection, and a full Master Project (AI-Powered Security Analyst).

The most comprehensive open-source mapping of OWASP GenAI risks to industry frameworks — 37 files, 16 frameworks, 3 source lists: LLM Top 10, Agentic Top 10, DSGAI 2026. OT/ICS, EU AI Act, NIST, ISO 27001, ISO 42001, CIS, SAMM, ENISA, NHI, AIVSS.

LLM Attack Testing Toolkit is a structured methodology and mindset framework for testing Large Language Model (LLM) applications against logic abuse, prompt injection, jailbreaks, and workflow manipulation.

An AI Security Testing Playbook with labs for prompt injection, RAG poisoning, and tool attacks

Deterministic security testing for RAG pipelines: measure retrieval-induced data leakage with CI-ready metrics.

Security case study: RAG prompt injection on AWS Bedrock Knowledge Bases + layered mitigations (retrieval scoping + output gating).

Runtime defense for AI agents. 24 inline defenses, 3 output scanners, MCP server, framework adapters.

🤖 Build your own local Retrieval-Augmented Generation system for private, offline AI memory without ongoing costs or data privacy concerns.

Dual-Stage Temporal Poisoning Attack on RAG Systems

Omega Walls — a deterministic runtime security layer for RAG and AI agents that detects prompt injection, tool abuse, and data exfiltration via cumulative risk modeling.

Reproducible security benchmarking for the Deconvolute SDK and AI system integrity against adversarial attacks.

An adversarial evaluation framework for LLM-integrated Security Operations Centers

AI Operations Security Maturity Model and toolkit to secure AI/ML systems across 11 domains and 5 levels—aligned to NIST AI RMF, SAIF, OWASP LLM Top 10, MITRE ATLAS. Practical AI security maturity model with assessment questions, CI/CD policy gates, LLM/RAG controls, infra/accelerator hardening, monitoring, IR, and red teaming.