How do I prevent jailbreak and prompt injection vulnerabilities in our LLM application before our security audit tomorrow?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do I prevent jailbreak and prompt injection vulnerabilities in our LLM application before our security audit tomorrow?
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 Top 10 for LLM Applications prompt injection prevention excessive agency sensitive information disclosure official Google ↗
NIST AI Risk Management Framework generative AI profile prompt injection security official Google ↗
- GitHub - GenAI-Security-Project/GenAI-LLM-Top10: OWASP Top 10 for Large Language Model Applications · github.com
- OWASP Top 10 for Large Language Model Applications · owasp.github.io
- AI/LLM Application Security Testing and Red Teaming · owasp.github.io
- DevSecOpsGuideline/current-version/2-Process/2-4-Test/2-4-6-AI-LLM-Application-Security-Testing.md at master · OWASP/DevSecOpsGuideline · github.com
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 prevention excessive agency sensitive information disclosure official OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 | |
NIST AI Risk Management Framework generative AI profile prompt injection security official OBSERVED | Google ↗ AI Mode ↗ | — | Consequence | — | — | 1x of 1 |