CalvinBackup/www-project-ai-testing-guide
1
0
Fork 0
mirror of https://github.com/OWASP/www-project-ai-testing-guide.git synced 2026-07-15 23:47:19 +02:00
Code Issues Packages Projects Releases Wiki Activity

Update AITG-APP-09_Testing_for_Model_Extraction.md

Browse Source
This commit is contained in:
Matteo Meucci
2025-11-23 13:38:38 +01:00
committed by GitHub
parent a64b7a1c04
commit f5f1c06034
1 changed files with 4 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Document/content/tests/AITG-APP-09_Testing_for_Model_Extraction.md
+4 -4
View File
@@ -156,10 +156,10 @@ else:
- Utilize differential privacy and noise injection techniques in model outputs to reduce the utility of extracted data.
- Deploy robust model monitoring and anomaly detection systems to flag and respond to extraction attempts.
### Suggested Tools for this Specific Test
- **ML Privacy Meter:** Tool specifically designed to quantify risks of model extraction and related privacy vulnerabilities ([ML Privacy Meter GitHub](https://github.com/privacytrustlab/ml_privacy_meter)).
- **PrivacyRaven:** A tool for testing extraction vulnerabilities and defending machine learning models through detection and mitigation strategies ([PrivacyRaven GitHub](https://github.com/trailofbits/PrivacyRaven)).
- **ART (Adversarial Robustness Toolbox):** Includes modules for detecting and mitigating model extraction vulnerabilities ([ART GitHub](https://github.com/Trusted-AI/adversarial-robustness-toolbox)).
### Suggested Tools
- **ML Privacy Meter:** Tool specifically designed to quantify risks of model extraction and related privacy vulnerabilities - [ML Privacy Meter GitHub](https://github.com/privacytrustlab/ml_privacy_meter)
- **PrivacyRaven:** A tool for testing extraction vulnerabilities and defending machine learning models through detection and mitigation strategies - [PrivacyRaven GitHub](https://github.com/trailofbits/PrivacyRaven)
- **ART (Adversarial Robustness Toolbox):** Includes modules for detecting and mitigating model extraction vulnerabilities - [ART GitHub](https://github.com/Trusted-AI/adversarial-robustness-toolbox)
### References
- OWASP Top 10 for LLM Applications 2025 - LLM02:2025 Sensitive Information Disclosure ([OWASP LLM 2025](https://genai.owasp.org/))