What constitutes a successful mitigation framework against indirect prompt injection vulnerabilities when our retrieval-augmented generation system crawls external web pages?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What constitutes a successful mitigation framework against indirect prompt injection vulnerabilities when our retrieval-augmented generation system crawls external web pages?
2 real searches · 1 real-search pass
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 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
OWASP Top 10 for LLM Applications prompt injection RAG untrusted external content mitigations 2025 Google ↗
NIST AI prompt injection retrieval augmented generation security guidance indirect prompt injection Google ↗
- RAG Security · cheatsheetseries.owasp.org
- CheatSheetSeries/cheatsheets/LLM_Prompt_Injection_Prevention_Cheat_Sheet.md at master · OWASP/CheatSheetSeries · github.com
- LLM01:2025 Prompt Injection · genai.owasp.org
- Defend against indirect prompt injection attacks · learn.microsoft.com
- LLM Prompt Injection Prevention · cheatsheetseries.owasp.org
- 4. Input, Context, and Retrieval Hygiene · learn.microsoft.com
- AI/LLM Application Security Testing and Red Teaming · owasp.github.io
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OWASP Top 10 for LLM Applications prompt injection RAG untrusted external content mitigations 2025 OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
NIST AI prompt injection retrieval augmented generation security guidance indirect prompt injection OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |