## 2 Related Works  
The vulnerability of DL models to cybersecurity threats has been a critical area of research, particularly in high-stakes domains such as healthcare. [PERSON] et al. [17] demonstrated the weakness of DL models against adversarial attacks, where imperceptible perturbations lead to incorrect predictions. [PERSON] et al. [18] introduced the FGSM. They provided an efficient approach for generating adversarial examples and highlighting the fragility of DL models to gradient-based attacks. [PERSON] et al. [13] demonstrated how adversarial attacks misclassify malignant tumors as benign. Such incidents can delay life-saving treatments. [PERSON] et al. [19] identified the risks of adversarial attacks in radiology and emphasized the need for domain-specific defenses in healthcare applications. [PERSON] and [PERSON] [20] highlighted the vulnerability of Convolutional Long Short-Term Memory (ConvLSTM) models to adversarial attacks. [PERSON] et al. [21] explored adversarial attacks on Large Language Models (LLMs) in medical applications. [PERSON] et al. [22] demonstrated how attacks could disrupt system functionality and lead to erroneous medical decisions. Data poisoning represents another significant threat. Malicious samples introduced into the training dataset can compromise the model's integrity through this attack. [PERSON] et al. [23] highlighted the potential for data poisoning to inject biases, thus reducing the model's reliability and jeopardizing patient outcomes. [PERSON] et al. [24] showed that even minimal poisoning levels can destabilize machine learning models. They stressed advanced anomaly detection methods to safeguard critical systems. Evasion attacks that can exploit model decision thresholds to bypass detection have also gained attention. [PERSON] et al. [10] investigated the vulnerabilities of DL models to such attacks and proposed adaptive defense mechanisms that leverage real-time model monitoring. These findings demonstrate the dynamic nature of evasion attacks and their challenges to static defense strategies. Several defense mechanisms have been developed to mitigate these threats. Adversarial training has emerged as a prominent technique to enhance robustness by incorporating adversarial examples into the training process [18]. Noise-filtering and feature-smoothing approaches have also been explored to counter adversarial and evasion attacks [25]. [PERSON] et al. [26] proposed applying differentially private learning to adjust model training to dynamically reduce poisoned data's impact. However, these approaches often face trade-offs between computational efficiency and model resilience that limit their practical applicability in resource-constrained healthcare settings. Despite the emerging research and developments, there are critical gaps in this area of research. To the best of our knowledge, and based on extensive research in AI security, there is a significant lack of studies focused on vulnerability assessments in DL models for healthcare using post-attack analysis. While studies such as [PERSON] and [PERSON] [27] and [PERSON] et al. [28] have explored adversarial threats and vulnerabilities in AI systems, these works do not provide a quantifiable approach to evaluating the vulnerabilities of DL models. Consequently, the existing literature highlights a gap our research seeks to address. By introducing a novel framework for post-attack analysis and quantifying vulnerabilities through the PAVI, the HAVA offers a unique and systematic approach to this critical area of research. The current research lacks a unified framework for evaluating multiple attack vectors and their collective impact on DL models. Additionally, traditional evaluation metrics, such as accuracy and confusion matrices, fail to contextualize the relative severity of attacks. Due to this, it has become difficult to prioritize defenses effectively. To address these gaps, we designed and implemented the HAVA.