As artificial intelligence (AI) technologies and particularly Generative AI (Gen AI) continue to evolve, so do the threats they face. Adversarial attacks, which pose significant risks to AI models, can result in data leakage, model theft, and manipulation. This whitepaper explores the necessity of protecting both traditional and generative AI models from various AI adversarial threats including model inference, extraction, evasion and injection, data poisoning, prompt injections and personal identifiable information (PII) leakage. We outline the emerging risks, the methodologies of these attacks, and propose a robust framework for mitigating these threats to maintain the security and integrity of AI systems.

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