From 9b9fd9160b2aab3124f2774773864105a32c3a3d Mon Sep 17 00:00:00 2001 From: Matteo Meucci Date: Wed, 5 Nov 2025 19:46:24 +0100 Subject: [PATCH] Update AITG-DAT-02_Testing_for_Runtime_Exfiltration.md --- ...TG-DAT-02_Testing_for_Runtime_Exfiltration.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/Document/content/tests/AITG-DAT-02_Testing_for_Runtime_Exfiltration.md b/Document/content/tests/AITG-DAT-02_Testing_for_Runtime_Exfiltration.md index c5d8cd0..d39a9b0 100644 --- a/Document/content/tests/AITG-DAT-02_Testing_for_Runtime_Exfiltration.md +++ b/Document/content/tests/AITG-DAT-02_Testing_for_Runtime_Exfiltration.md @@ -31,17 +31,17 @@ Runtime Exfiltration involves unauthorized extraction or leakage of sensitive da ### Expected Output The AI infrastructure should effectively: - -- Restrict inference outputs to prevent exposure of sensitive, personally identifiable, or proprietary information. -- Mask, anonymize, or omit sensitive data from logs, caches, or inference responses. -- Secure all runtime APIs and interfaces against unauthorized data extraction or unintended disclosure. +- **Restrict Inference Outputs**: Prevent exposure of sensitive, personally identifiable, or proprietary information from other contexts. +- **Mask Sensitive Data in Logs**: Automatically mask, anonymize, or omit sensitive data from logs, caches, and error messages. +- **Secure All Runtime APIs**: Ensure APIs return generic error messages and do not leak internal system state or data from other users. ### Remediation -- Implement strict runtime output validation and sanitization mechanisms. -- Enforce secure logging practices, ensuring sensitive runtime data is anonymized or excluded from logs and caches. -- Deploy API security solutions to robustly verify, sanitize, and control inference responses and prevent sensitive data leakage. -- Regularly audit runtime operations and inference endpoints to detect and prevent unauthorized data exfiltration. +- **Implement Strict Runtime Output Validation and Sanitization**: Before returning an output, scan it for sensitive data patterns (e.g., regex for PII) and mask or remove them. This is a critical last line of defense. +- **Enforce Secure Logging Practices**: Configure logging frameworks to automatically filter or mask sensitive data. Never log raw user inputs or full API responses in production. Log only metadata necessary for debugging. +- **Implement Generic Error Handling**: Ensure that user-facing error messages are always generic and never include stack traces, internal variable states, or raw data from the request or system. +- **Use Data Loss Prevention (DLP) Solutions**: Deploy automated DLP tools that can inspect both API traffic and logs in real-time to detect and block sensitive data exfiltration. +- **Enforce Strong Multi-Tenancy Controls**: In multi-tenant systems, ensure that data from one tenant is cryptographically and logically isolated from all others at all stages (inference, logging, caching). ### Suggested Tools