LLM features
Secrets or private data in the system prompt
Keys, internal URLs or other users' records are placed in the prompt and can be extracted by asking.
TermsLLM feature (a product feature that calls a large language model, such as an assistant or a summariser) · golden set (a fixed list of model inputs with recorded expectations, re-run on every model or prompt change) · fault injection (making a dependency fail on purpose in staging to see what the user and the logs show)
highseverity if found in a releasesources verified 2026-09-21
Why AI-assisted code produces it
OWASP lists system prompt leakage (LLM07) and sensitive information disclosure (LLM02). Putting context in the prompt is how features get built quickly.
The OWASP Top 10 for LLM Applications 2025 lists prompt injection (LLM01), sensitive information disclosure (LLM02), improper output handling (LLM05), excessive agency (LLM06), system prompt leakage (LLM07) and unbounded consumption (LLM10) among the ten risks.
Source: OWASP GenAI Security Project, 2025-03-12 · verified 2026-09-21
How we detect it
Read the assembled prompt; run extraction probes.
Most associated with
Tools: Cursor, GitHub Copilot, Claude Code, Windsurf, Lovable, Bolt
Checklist items that catch it
System prompt and retrieved context contain no secrets
manual Read the assembled prompt for each feature; run prompt-extraction probes against the feature.
No key, internal URL, other user's data or unpublished business rule is present in the prompt or returned by a probe.
Dots: Starter, Growth, Scale. The item runs on every release for the plans marked.
More in LLM features
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