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Update AITG-INF-05_Testing_for_Fine-tuning_Poisoning.md

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Matteo Meucci
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@@ -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)