Individual skill

ai-sensitive-data-disclosure

Make an LLM app disclose sensitive data it should never reveal: training-data memorization, secrets/PII in RAG context, cross-tenant leakage, conversation logs. Load when the target LLM has RAG/document access, is multi-tenant (per-user/per-org assistants), was fine-tuned on private data, or logs conversations. Signals: "ask your documents", per-org chatbots, upload a file and query it, admin/debug endpoints, conversation history in analytics.

skills/ai-ml/ai-sensitive-data-disclosure/SKILL.md

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Last checked: Nov 7, 2026

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Repository stars (not skill ratings): 27
Repository last updated: Oct 14, 2026
License: MIT

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# LLM sensitive information disclosure

## When it applies
The LLM app touches data beyond the user's own input: a RAG corpus of internal documents,
fine-tuning on private data, per-tenant knowledge bases, tool access to databases/files, or
logged conversation history. The core question: *can the model be coaxed into emitting data
that belongs to someone else — another tenant, the operator, or the training set?*

## Why it works
The model has no access control — everything in its context window is equally "its" knowledge,
so any data the pipeline injects (retrieved chunks, system prompt, tool results) is one clever
question away from the user. Memorization adds a second channel: models regurgitate training
data verbatim under the right probes. Authorization failures compound it — if the retrieval or
tool layer doesn't filter by tenant, the model happily answers across tenant boundaries.