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.

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

Scanner for adversarial hubs in RAG and vector databases

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).

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.

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

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

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

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

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.

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.

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

A comprehensive security and governance checklist for developers integrating AI and LLMs into production applications.

Open-source security layer for agentic AI. Monitor prompts from injection, jailbreak and more in real-time. All local.

Static analysis CLI that scans codebases for LLM prompt-injection, data-exfiltration, jailbreak, and unsafe agent/tool vulnerabilities. Runs fully offline, integrates with CI/CD, and outputs console, JSON, and SARIF reports.

Blackwall LLM Shield is an open-source AI security toolkit for JavaScript and Python that protects LLM apps from prompt injection, sensitive data leaks, unsafe tool calls, and hostile RAG content with prompt sanitization, PII masking, output inspection, policy enforcement, and audit trails.