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Detect poisoning, embedding anomalies, and backdoor triggers in RAG corpora.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "RAGSHIELD" yet — see the docs or source repo.
Use RAGSHIELD to scan this set of RAG documents, identify suspicious embedding anomalies, potential poisoning samples, and possible backdoor-triggering text snippets, then output a risk-ranked report.
A security report listing high-risk documents, anomaly reasons, trigger patterns, and remediation suggestions.
Call RAGSHIELD through MCP to automatically run security checks before an AI agent writes to or updates the knowledge base; if poisoning or backdoor signs are found, return the blocking reason and affected content.
Agent-ready check results with allow/block status, a risk summary, and details of blocked items.
After cleaning the corpus, use RAGSHIELD to compare scans of the pre-cleaned and post-cleaned versions, showing which anomalies were reduced and where residual risks remain.
A before-and-after analysis showing sanitization impact, residual threats, and follow-up hardening recommendations.
Diagnose RAG pipeline regressions by pinpointing failure modes with statistical rigor.
Build a self-hosted RAG pipeline for code search, findings, and CI integration.
Connect AI to documents, embeddings, and semantic search through PostgREST APIs.
Let AI agents research cybersecurity offline using a local security knowledge base.
Scan LLM agentic workflows for security risks and fix issues early.
Use authenticated MCP tools for graph-augmented and hybrid RAG retrieval.