Are there any foolproof defenses against prompt injection vulnerabilities when building customer-facing AI agents with custom system prompts?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Are there any foolproof defenses against prompt injection vulnerabilities when building customer-facing AI agents with custom system prompts?
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.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
OWASP LLM Prompt Injection Prevention Cheat Sheet defenses least privilege tool permissions input validation output validation Google ↗
NIST generative AI prompt injection indirect prompt injection risk mitigations official Google ↗
- LLM Prompt Injection Prevention · cheatsheetseries.owasp.org
- LLM Prompt Injection Prevention · cheatsheetseries.owasp.org
- AI Agent Security · cheatsheetseries.owasp.org
- AI Agent Security · cheatsheetseries.owasp.org
- CaMeL architecture · arxiv.org
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OWASP LLM Prompt Injection Prevention Cheat Sheet defenses least privilege tool permissions input validation output validation OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 | |
NIST generative AI prompt injection indirect prompt injection risk mitigations official OBSERVED | Google ↗ AI Mode ↗ | — | Consequence | — | — | 1x of 1 |