From 2d3d23b800e92f3e39c7588fc8ddb056aac22312 Mon Sep 17 00:00:00 2001 From: Matteo Meucci Date: Fri, 14 Nov 2025 10:58:33 +0100 Subject: [PATCH] Update AITG-INF-05_Testing_for_Fine-tuning_Poisoning.md --- .../tests/AITG-INF-05_Testing_for_Fine-tuning_Poisoning.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Document/content/tests/AITG-INF-05_Testing_for_Fine-tuning_Poisoning.md b/Document/content/tests/AITG-INF-05_Testing_for_Fine-tuning_Poisoning.md index 97e1aaf..f3127c6 100644 --- a/Document/content/tests/AITG-INF-05_Testing_for_Fine-tuning_Poisoning.md +++ b/Document/content/tests/AITG-INF-05_Testing_for_Fine-tuning_Poisoning.md @@ -42,7 +42,7 @@ This test identifies vulnerabilities arising from poisoning during fine-tuning, - Tool Link: [Cleanlab on GitHub](https://github.com/cleanlab/cleanlab) ### References -- OWASP Top 10 for LLM Applications 2025. "LLM04: Data and Model Poisoning." OWASP, 2025. [Link](https://genai.owasp.org/) +- OWASP Top 10 for LLM Applications 2025. "LLM04: Data and Model Poisoning." OWASP, 2025. [Link](https://genai.owasp.org/llmrisk/llm042025-data-and-model-poisoning/) - NIST AI 100-2e2025, "Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations," Section 2.3 "Poisoning Attacks and Mitigations." NIST, March 2025. [Link](https://doi.org/10.6028/NIST.AI.100-2e2025) - Wallace, Eric, et al. "Universal Adversarial Triggers for Attacking and Analyzing NLP." EMNLP-IJCNLP 2019. [Link](https://arxiv.org/abs/1908.07125) - BadLlama: "Tailoring Backdoor Attacks to Large Language Models." [Link](https://arxiv.org/abs/2401.06333)