How do I prevent prompt injection vulnerabilities when integrating external APIs into our company's internal LangChain autonomous agents?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do I prevent prompt injection vulnerabilities when integrating external APIs into our company's internal LangChain autonomous agents?
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
LangChain official documentation prompt injection tool security guardrails middleware human in the loop Google ↗
OWASP Top 10 for LLM Applications prompt injection excessive agency official Google ↗
- owasp.github.io · owasp.github.io
- OWASP Top 10 for Large Language Model Applications · owasp.github.io
- docs/src/oss/langchain/guardrails.mdx at main · langchain-ai/docs · github.com
- docs/src/oss/langchain/human-in-the-loop.mdx at main · langchain-ai/docs · github.com
- LLM Prompt Injection Prevention · cheatsheetseries.owasp.org
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
LangChain official documentation prompt injection tool security guardrails middleware human in the loop OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 | |
OWASP Top 10 for LLM Applications prompt injection excessive agency official OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |