rag-security
CommunitySecure RAG workflows against ingestion poisoning.
Software Engineering#security#rag#retrieval-augmented-generation#citation-verification#data-leakage#cross-tenant
Authormaruakshay
Version1.0.0
Installs0
System Documentation
What problem does it solve?
In a retrieval augmented generation (RAG) system, untrusted documents and weak metadata handling create surfaces for ingestion poisoning, cross-tenant leakage, and hallucinated grounding that misleads users.
Core Features & Use Cases
- Detect and mitigate ingestion poisoning, ensure chunk provenance, and validate metadata to keep retrieved context trustworthy.
- Enforce retrieval boundaries with server-side filters, provenance persistence, and robust citation verification across multi-tenant deployments.
- Provide a repeatable review workflow with guardrails, checklists, and recommended quick wins to harden RAG pipelines in codebases and design patterns.
Quick Start
Apply the RAG security review to your codebase to identify ingestion poisoning, boundary violations, and metadata handling weaknesses.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: rag-security Download link: https://github.com/maruakshay/mii-ai-security/archive/main.zip#rag-security Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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