2026· International Journal of Scientific Research and Management· 0 citations
TL;DR
A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out of a trained system.
Abstract
As artificial intelligence becomes woven into critical applications such as healthcare, finance, autonomous systems, and cybersecurity, adversarial threats to machine learning models have grown into one of the most pressing concerns in the field. Adversarial machine learning studies how attackers exploit weaknesses in model architectures and data pipelines, manipulating inputs to trigger misclassification, extract sensitive information, or quietly degrade system performance. This article offers a detailed overview of the security risks associated with adversarial attacks, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out of a trained system. In response to these threats, the discussion evaluates a wide range of defense strategies designed to strengthen the robustness and reliability of AI systems, including adversarial training, robust optimization, defensive distillation, anomaly detection, and privacy-preserving techniques such as differential privacy and federated learning. Particular emphasis is placed on weaving these defenses into every stage of the AI development lifecycle and on cultivating a threat-aware mindset before models are ever deployed into real-world environments. By drawing together current research, mathematical foundations, and practical implementation experience, this article traces the evolving landscape of adversarial machine learning and offers actionable guidance for developers, researchers, and policymakers who are working to secure AI-driven applications against increasingly sophisticated attacks.
The rapid adoption of artificial intelligence and machine learning across critical sectors has introduced cybersecurity challenges that traditional security frameworks were never designed to address. As machine learning models increasingly shape decision-making in finance, healthcare, autonomous systems, and national security, they have become attractive targets for sophisticated adversaries. This paper examines the evolving threat landscape surrounding AI systems, with particular attention to vulnerabilities that arise at each stage of the machine learning lifecycle, including data poisoning, adversarial manipulation, model extraction, and inference-based privacy attacks. It explores how attackers exploit weaknesses in training data, model architectures, and deployment pipelines to manipulate model behavior or exfiltrate sensitive information, and it reviews the defense strategies that have emerged in response, from adversarial training and robust model design to secure deployment practices and privacy-preserving techniques such as differential privacy, federated learning, and homomorphic encryption. The discussion also situates these technical measures within the broader governance frameworks and secure-by-design principles that organizations are beginning to adopt across the AI development lifecycle. By synthesizing current research and practical defense approaches, this paper provides a comprehensive overview of how machine learning systems can be safeguarded against a threat landscape that continues to evolve as quickly as the technology itself. The overarching conclusion is that securing AI systems is not a task any single discipline can accomplish alone; it requires sustained collaboration between machine learning researchers, cybersecurity professionals, and policymakers if AI technologies are to remain reliable, trustworthy, and resilient in adversarial environments.
Harsh Verma· International Journal of Sci...· 0 citations
The review explores the key adversarial attack classes: poisoning, evasion, model extraction, model extraction, model inversion, and membership inference and also white-box, black-box, and grey-box threat models.
Ujjwal Deshmukh· International Journal of Inn...· 0 citations
A novel framework for adversarial machine unlearning is introduced to enable privacy-preserving threat intelligence sharing and lays the foundation for secure and compliant knowledge transfer in federated security operations and collaborative defence ecosystems.
R. Polishetty, Arfi Siddik Mollashaik, Naveen Jagadam et al.· Journal of Intelligent Decis...· 0 citations
Adversarial attacks pose a significant threat to the reliability and security of Artificial Intelligence (AI) systems, especially as AI integrates into critical fields like healthcare and finance. This work specifically addresses adversarial evasion attacks conducted during the testing phase. In this study, attacks are designed to be untargeted (misleading the model to any wrong class) and white-box (attacker has access to the model), using small perturbations to cause incorrect classifications. To counter these threats, this research investigates adversarial training, a defense mechanism where models are trained using data generated by attacks. The study systematically compares two distinct adversarial training strategies: ‘pre-train’, where adversarial examples are generated beforehand, and ‘in-train’, where perturbations are introduced dynamically during the training process. This comparison aims to understand the advantages and limitations of each approach in enhancing model robustness. Using a Fully Connected Neural Network (FCNN) and the MNIST dataset, experiments were conducted with attacks like FGSM and BIM at varying perturbation strengths $\epsilon $ . The results provide a controlled comparison of adversarial training strategies, highlighting key trade-offs between robustness and generalization. We observe that the ‘in-train’ strategy provides superior robustness in the evaluated setting against white-box, untargeted attacks compared to the ‘pre-train’ method. However, the findings are derived from experiments on the MNIST dataset using FCNNs, and therefore should be interpreted as foundational insights. Extending the analysis to more complex datasets and architectures remains an important direction for future work.
José María Jorquera Valero, Ibon Bengoechea Cazorla, Manuel Gil Pérez· IEEE Access· 0 citations
The growing deployment of deep learning models in safety-critical domains has exposed the artificial intelligence
landscape to a widening array of adversarial threats, where imperceptible input perturbations reliably induce severe
misclassifications. This survey provides a comprehensive synthesis of adversarial attacks and defense mechanisms in modern
AI security. It introduces a structured taxonomy categorizing attacks into evasion, poisoning, and model inversion strategies,
evaluated across varying levels of attacker knowledge. Correspondingly, current defense techniques—including adversarial
training, anomaly detection, and gradient masking—are critically reviewed for their resilience against adaptive, real-world
adversaries. The survey further examines robustness benchmarking and success rate analysis frameworks, emphasizing the
gap between theoretical guarantees and practical deployment. By consolidating recent advances and persistent limitations,
this work identifies open research challenges and outlines emerging directions toward provably secure and trustworthy AI
systems for real-world applications.
P. Mahalakshmi, D. Jayalakshmi· International Journal of Inn...· 0 citations
A systematic analysis of 207 studies selected from 4447 records following the PRISMA 2020 guidelines, covering work published between 2020 and 2026 across cybersecurity and computer vision finds systems that are robust against adaptive adversaries, interpretable under operational constraints, and auditable in environments where AI accountability is a legal requirement.