Adversarial attacks pose a growing threat to the effectiveness of AI systems. These attacks involve altering input data in subtle ways to cause inaccurate predictions. Safeguarding against such attacks necessitates a multi-faceted approach that encompasses {robust{design principles, rigorous testing methodologies, and ongoing monitoring strategies.

The burgeoning domain of artificial intelligence (AI) presents both unprecedented opportunities and substantial risks. As sophisticated AI systems become increasingly integrated into our lives, it is imperative to establish robust safeguards to mitigate potential harms. One essential aspect of this endeavor involves mitigating the risk of AI malici