What steps should our development team take to secure our RAG pipeline against indirect prompt injection via user uploaded documents
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What steps should our development team take to secure our RAG pipeline against indirect prompt injection via user uploaded documents
1 real search · 1 real-search pass
OWASP LLM prompt injection prevention RAG untrusted retrieved documents guidance; NIST generative AI prompt injection retrieval augmented generation securityGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
OWASP LLM prompt injection prevention RAG untrusted retrieved documents guidance; NIST generative AI prompt injection retrieval augmented generation security Google ↗
- RAG Security · cheatsheetseries.owasp.org
- LLM01:2025 Prompt Injection · genai.owasp.org
- CheatSheetSeries/cheatsheets/LLM_Prompt_Injection_Prevention_Cheat_Sheet.md at master · OWASP/CheatSheetSeries · github.com
- LLM Prompt Injection Prevention · cheatsheetseries.owasp.org
- AI Agent Security · cheatsheetseries.owasp.org
- CheatSheetSeries/cheatsheets/RAG_Security_Cheat_Sheet.md at master · OWASP/CheatSheetSeries · github.com
- AI/LLM Application Security Testing and Red Teaming · owasp.github.io