diff --git a/Document/content/tests/AITG-MOD-06_Testing_for_Robustness_to_New_Data.md b/Document/content/tests/AITG-MOD-06_Testing_for_Robustness_to_New_Data.md
index fde28bb..bcd5f45 100644
--- a/Document/content/tests/AITG-MOD-06_Testing_for_Robustness_to_New_Data.md
+++ b/Document/content/tests/AITG-MOD-06_Testing_for_Robustness_to_New_Data.md
@@ -40,5 +40,5 @@ This test identifies vulnerabilities associated with the lack of robustness to n
 
 ### References
 - "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift." Rabanser, Stephan, et al. NeurIPS 2019. [Link](https://arxiv.org/abs/1810.11953)
-- OWASP Top 10 for LLM Applications 2025. "LLM05: Improper Output Handling." OWASP, 2025. [Link](https://genai.owasp.org/)
+- OWASP Top 10 for LLM Applications 2025. "LLM05: Improper Output Handling." OWASP, 2025. [Link](https://genai.owasp.org/llmrisk/llm052025-improper-output-handling/)
 - NIST AI 100-2e2025, "Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations," Section 4.2 "Evaluation – Robustness and Resilience to Distribution Shifts." NIST, March 2025. [Link](https://doi.org/10.6028/NIST.AI.100-2e2025)