skills.rest
maruakshay
maruakshayCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

langchain-rag-security

Identify and mitigate security risks in LangChain RAG pipelines.

4|Updated Apr 27, 2026
Search Tags:#security#langchain#rag#retrieval#memory#grounding
One-click install
npx skills add https://github.com/maruakshay/mii-ai-security --skill langchain-rag-security
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag-security
Source: https://github.com/maruakshay/mii-ai-security/tree/main/skills/langchain-rag-security
Command: npx skills add https://github.com/maruakshay/mii-ai-security --skill langchain-rag-security

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LangChain RAG security auditing identifies and mitigates risk points across retrieval, memory, and callback layers, preventing data leakage, hallucinations, and chain misconfigurations.

Core Features & Use Cases

  • Identify boundary violations in ConversationalRetrievalChain, document grounding, and citation provenance.
  • Validate memory interactions, tool outputs, and intermediate steps to prevent leakage and misuse.
  • Provide actionable checks and governance for secure chain composition across LangChain deployments.

Quick Start

Run a LangChain RAG security review to identify grounding, memory, and callback risks in your retrieval augmented generation pipeline.

Frequently Asked Questions about langchain-rag-security

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I secure a LangChain RAG pipeline against data leakage and hallucinations?▼

To secure a LangChain RAG pipeline, audit retrieval, memory, and callback layers for boundary violations. Validate document grounding and citation provenance to prevent data leakage and hallucinations in ConversationalRetrievalChain deployments.

What security risks exist in LangChain memory interactions and tool outputs?▼

Security risks in LangChain memory interactions include intermediate step leakage and tool output misuse. Validate memory interactions across your RAG pipeline to prevent unauthorized data access and ensure safe chain composition.

How do I add guardrails to LangChain chain composition for production use?▼

Add guardrails to LangChain chain composition by scoping security reviews to callback governance and chain misconfigurations. Provide concrete verification steps and remediation guidance to ensure secure production RAG deployments.

Does my ConversationalRetrievalChain need citation provenance validation?▼

Your ConversationalRetrievalChain needs citation provenance validation to identify boundary violations and maintain document grounding. Validating provenance prevents hallucinations and ensures accurate retrieval augmented generation outputs.

What are the limitations of relying on default LangChain callback governance?▼

Relying on default LangChain callback governance risks chain misconfigurations and intermediate step leakage. Implement explicit callback governance and actionable checks to mitigate data leakage risks in production RAG environments.

Related Skills

Explore More Software Engineering Skills→
Omar-ObandoOmar-Obando
44

langchain

Build LLM applications with LangChain using prompts, chains, agents, memory, and retrieval.

Community
Advanced
FacundoSu1986FacundoSu1986

llm-application-dev-langchain-agent

Build scalable LangChain/LangGraph agents with async patterns and observability.

Community
Advanced
jpoindexterjpoindexter
1

llm-evals-and-retrieval-quality

Evaluate and improve LLM and RAG systems with retrieval metrics and grounding checks.

Community
Advanced
uphiagouphiago
1.1k

hunt-llm-ai

Identify exploitable LLM/AI vulnerabilities crossing trust boundaries with validation gates.

Community
Advanced