indirect-prompt-injection

Prevent indirect prompt injection by labeling, filtering, and isolating externally retrieved content before it influences model prompts. Apply external-content trust-scoping, delimiter breakout detection, and strict retrieval-action separation to block instruction overrides.

External-content labeling secures retrieval grounding by tagging fetched blocks with source, trust level, and allowed use. This metadata routes retrieved data through a trusted information-only channel, preventing untrusted text from modifying execution paths.

The best way to filter role-claims and delimiter breakouts is using injection-pattern filtering on externally retrieved content. This detects instruction overrides and delimiter breakouts before content enters prompts, ensuring system prompts remain isolated from untrusted external data.

Yes, you can audit suspicious content by implementing auditable logging that routes retrieved data through a trusted channel. This logs injection attempts from ingested tickets or emails for post-incident review, protecting escalation logic from malicious modifications.

Yes, this LLM safety approach works for chat assistants ingesting emails and support tickets. It isolates externally retrieved content using external-content labeling and strict separation of retrieval from action, preventing ingested tickets from altering system prompts.

You need strict separation of retrieval from action when external content influences model behavior in LLM pipelines. If fetched web pages, documents, or tool outputs risk overriding trusted prompts, isolation prevents retrieved data from modifying execution paths.