We need to connect our internal SQL database to a local LLM interface using LangChain, what security layers should we implement first?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
We need to connect our internal SQL database to a local LLM interface using LangChain, what security layers should we implement first?
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
LangChain SQLDatabaseToolkit security least privilege read only database permissions documentation Google ↗
OWASP Top 10 for LLM Applications prompt injection sensitive information disclosure excessive agency official Google ↗
- base | langchain_community · reference.langchain.com
- SQLDatabase Toolkit · langchain.docs-hub.com
- GitHub - GenAI-Security-Project/GenAI-LLM-Top10: OWASP Top 10 for Large Language Model Applications · github.com
- LLM Prompt Injection Prevention · cheatsheetseries.owasp.org
- 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 |
|---|---|---|---|---|---|---|---|
LangChain SQLDatabaseToolkit security least privilege read only database permissions documentation OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
OWASP Top 10 for LLM Applications prompt injection sensitive information disclosure excessive agency official OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |