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## AITG-APP-04 - Testing for Input Leakage
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# AITG-APP-04 - Testing for Input Leakage
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### Summary
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Here's the reviewed and refined **Test/Payloads** section for **AITG-APP-04 - Testing for Input Leakage**, clearly specifying the responses indicating vulnerabilities:
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---
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### How to Test/Payloads
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1. **Input Persistence Check**
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- Deploy sensitive data guardrails capable of countering adversarial attempts to leak sensitive information.
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- Ensure guardrails normalize inputs prior to filtering and detect obfuscated sensitive data and contextual cues in both inputs and outputs.
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### Suggested Tools for this Specific Test
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### Suggested Tools
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- **Garak – Input Leakage Probe**: Specialized Garak module designed to detect sensitive input data leaks.
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- **URL**: [https://github.com/NVIDIA/garak/blob/main/garak/probes/leakreveal.py](https://github.com/NVIDIA/garak/blob/main/garak/probes/leakreveal.py)
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- **Microsoft Counterfit**: An AI security tool capable of testing for input leakage issues in model interactions.
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- **URL**: [https://github.com/Azure/counterfit](https://github.com/Azure/counterfit)
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### References
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- **Title**: OWASP Top 10 LLM02:2025 Sensitive Information Disclosure
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- **Author**: OWASP Foundation
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- **Link**: [https://genai.owasp.org](https://genai.owasp.org)
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- **Title**: NIST AI 100-2e2025 - Privacy Attacks and Mitigations
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- **Author**: NIST
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- **Link**: [https://doi.org/10.6028/NIST.AI.100-2e2025](https://doi.org/10.6028/NIST.AI.100-2e2025)
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- OWASP Top 10 LLM02:2025 Sensitive Information Disclosure - [https://genai.owasp.org](https://genai.owasp.org/llmrisk/llm02-insecure-output-handling)
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- NIST AI 100-2e2025 - Privacy Attacks and Mitigations - [https://doi.org/10.6028/NIST.AI.100-2e2025](https://doi.org/10.6028/NIST.AI.100-2e2025)
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