Real ChatGPT answer · index seedWhP6OnFvUvgcompleteB2B software · AI and automation toolsinformational
What are the best practices for structuring system prompts to prevent LLMs from leaking proprietary training data during adversarial probing?
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The searches the AI actually ran for this prompt.
SEED QUERY
What are the best practices for structuring system prompts to prevent LLMs from leaking proprietary training data during adversarial probing?
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
LLM data leakage prevention system prompt prompt injection defense best practices OWASP LLM Top 10 sensitive information disclosure 2025 Google ↗
NIST generative AI data privacy leakage guidance prompt injection system instructions defense Google ↗
- LLM07: 2025 System Prompt Leakage · owasp.org
- Prompt Leakage · genai.owasp.org
- CheatSheetSeries/cheatsheets/LLM_Prompt_Injection_Prevention_Cheat_Sheet.md at master · OWASP/CheatSheetSeries · 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 |
|---|---|---|---|---|---|---|---|
LLM data leakage prevention system prompt prompt injection defense best practices OWASP LLM Top 10 sensitive information disclosure 2025 OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
NIST generative AI data privacy leakage guidance prompt injection system instructions defense OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |