## 1 Introduction  
Artificial intelligence (AI) has revolutionized various industries, and healthcare has emerged as one of the most transformative fields for its application. AI-powered systems are widely used for disease diagnosis, process automation, and supporting clinical decision-making [1, 2, 3, 4]. Deep learning (DL), which is considered a subset of AI, has demonstrated exceptional efficacy in medical imaging tasks by enabling the precise classification and diagnosis of diseases such as breast cancer. DL models have been proven instrumental in analyzing digitized images of Fine-Needle Aspiration (FNA) biopsies and can be applied to perceive valuable insights that enhance clinical decision-making and patient outcomes [5, 6]. Despite their potential, DL models in healthcare are not resilient against cybersecurity threats. Adversarial, data poisoning, and adversarial evasion attacks pose severe risksto the reliability of AI systems and patient safety. Adversarial attacks introduce subtle imperceptible perturbations in the input data to mislead the models into making incorrect predictions [7, 8]. A variation of the adversarial attack, called the evasion attack, targets the model during inference by introducing perturbations that bypass detection thresholds without altering the model's structure or training data. Data poisoning attacks alter training datasets by injecting malicious samples to compromise a model's learning process and degrade its performance [9, 10]. In healthcare, such attacks pose severe risks because misclassifications or tampering with diagnoses can result in delayed treatments, unnecessary procedures, or even harm to the patient [11, 12, 13]. For example, an adversarial attack on breast cancer detection systems can misclassify malignant cells as benign, delaying life-saving treatments. Recent efforts have focused on developing novel frameworks to enhance the resilience of DL models. Studies have highlighted using federated learning to mitigate data poisoning by decentralizing model training to reduce the risk of malicious data corruption [14]. Integrating adversarially robust training algorithms has been proposed to improve the model's reliability against gradient-based attacks such as the Fast Gradient Sign Method (FGSM) [15]. Explainable AI techniques have also gained attention in identifying threats by elucidating decision pathways in complex DL models, enabling researchers to pinpoint areas vulnerable to exploitation [16]. In this research, we propose the Healthcare AI Vulnerability Assessment Algorithm (HAVA), which integrates multiple features, simulates multiple attacks and computes performance metrics and the Post-Attack Vulnerability Index (PAVI) for attack analysis and mitigation assistance. By contextualizing the severity of performance degradation, HAVA provides a link between identifying theoretical vulnerabilities and implementing real-world defenses. Through this research, we have produced the following key contributions:  
1. Comprehensive analysis of FGSM, data poisoning and evasion attacks to offer a holistic view of the cybersecurity threats faced by DL models in healthcare. 2. Introduction of the PAVI as a core feature of the HAVA to standardize quantification and comparison of DL model vulnerabilities across different attack scenarios. 3. Empirical evaluation of the impact of these attacks on a DL model trained with the Wisconsin Diagnostic Breast Cancer (WDBC) dataset to provide actionable insights into the model vulnerabilities. 4. Provision of actionable recommendations and customized defense strategies based on visualizations and comparative analyses generated by the HAVA, focusing on improving the reliability of AI applications in healthcare. This paper is organized as follows. Section 2 reviews related works and identifies gaps in the existing research. Section 3 outlines the research materials and methods containing the dataset description, preprocessing, and mathematical representation of the classification model and attacks. Section 4 provides and explains the HAVA.