A Systematic Review of Adversarial Machine Learning Attacks, Defensive Controls and Technologies

Advances in Neural Information Processing Systems 19

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Trojaning Attack on Neural Networks

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Certified Adversarial Robustness via Randomized SmoothingPreprint

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Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

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Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning

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Formal Security Analysis of Neural Networks using Symbolic IntervalsPreprint

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Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks

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Spectral Signatures in Backdoor AttacksPreprint

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Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

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Certified Defenses for Data Poisoning Attacks

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Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization

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Adversarial Attacks and Defences Competition

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A Survey of Privacy Attacks in Machine Learning

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Defensive Distillation is Not Robust to Adversarial ExamplesPreprint

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BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements

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Byzantine Stochastic Gradient DescentPreprint

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Label Sanitization Against Label Flipping Poisoning Attacks

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Adversarial Examples in Modern Machine Learning: A ReviewPreprint

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Hidden Backdoors in Human-Centric Language Models

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Concealed Data Poisoning Attacks on NLP Models

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Explanation-Guided Backdoor Poisoning Attacks Against Malware ClassifiersPreprint

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Evading Classifiers by Morphing in the Dark

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Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems

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Deep k-NN Defense Against Clean-Label Data Poisoning Attacks

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A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

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Reconstructing Training Data with Informed Adversaries

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“Real Attackers Don't Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice

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Auditing Differentially Private Machine Learning: How Private is Private SGD?Preprint

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Adversarial Unlearning of Backdoors via Implicit Hypergradient

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Witches' Brew: Industrial Scale Data Poisoning via Gradient MatchingPreprint

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NeuronInspect: Detecting Backdoors in Neural Networks via Output ExplanationsPreprint

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Certified Robustness to Label-Flipping Attacks via Randomized SmoothingPreprint

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SPECTRE: Defending Against Backdoor Attacks Using Robust StatisticsPreprint

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Adversarial Machine Learning

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Property Inference from Poisoning

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FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client PerspectivePreprint

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Audio-domain position-independent backdoor attack via unnoticeable triggers

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Dynamic Backdoor Attacks Against Machine Learning ModelsPreprint

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On Adversarial Bias and the Robustness of Fair Machine LearningPreprint

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Fast Geometric Projections for Local Robustness CertificationPreprint

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A Black-Box Attack Method against Machine-Learning-Based Anomaly Network Flow Detection Models

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Poisoning Attacks and Data Sanitization Mitigations for Machine Learning Models in Network Intrusion Detection Systems

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Countering Physical Eavesdropper Evasion with Adversarial Training

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Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

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Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation

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Detecting Adversarial Examples Is (Nearly) As Hard As Classifying ThemPreprint

Tramèr, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11630 · Cited by 22 · see citing works

Securing Malware Cognitive Systems against Adversarial Attacks

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SNAP: Efficient Extraction of Private Properties with Poisoning

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Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack ScenariosPreprint

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A STUDY ON MACHINE LEARNING ALGORITHM IN MEDICAL DIAGNOSIS

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Deep Partition Aggregation: Provable Defenses against General Poisoning AttacksPreprint

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“I Never Thought About Securing My Machine Learning Systems”: A Study of Security and Privacy Awareness of Machine Learning Practitioners

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Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset ReleasePreprint

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VERICA - Verification of Combined Attacks

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Adversarial Perturbations Against Real-Time Video Classification SystemsPreprint

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Trigger Hunting with a Topological Prior for Trojan DetectionPreprint

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GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

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The Design and Implementation of a Scalable Deep Learning Benchmarking Platform

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Privacy‐enhancing machine learning framework with private aggregation of teacher ensembles

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Feedback Learning for Improving the Robustness of Neural Networks

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Towards Training Robust Private Aggregation of Teacher Ensembles Under Noisy Labels

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Property Inference From PoisoningPreprint

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Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy Preserving Quantum Machine Learning

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Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware LearnersPreprint

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Certifying machine learning models against evasion attacks by program analysis

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Mental Models of Adversarial Machine LearningPreprint

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Analytics Using Machine Learning-Guided Simulations with Application to Healthcare Scenarios

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Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning).Preprint

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The Hidden Vulnerability of Distributed Learning in Byzantium

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Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

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A comprehensive survey on intrusion detection systems with advances in machine learning, deep learning and emerging cybersecurity challenges

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Robust AI for Financial Fraud Detection in the GCC: A Hybrid Framework for Imbalance, Drift, and Adversarial Threats

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Adversarial Threats to AI-Driven Systems: Exploring the Attack Surface of Machine Learning Models and Countermeasures

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A Comparative Study of Privacy-Preserving Techniques in Federated Learning: A Performance and Security Analysis

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Adversarial Threats to Cloud IDS: Robust Defense With Adversarial Training and Feature Selection

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The Ethical and Legal Implications of Shadow AI in Sensitive Industries: A Focus on Healthcare, Finance and Education

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When AI Is Fooled: Hidden Risks in LLM-Assisted Grading

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Black-box adversarial attack defense approach: An empirical analysis from cybersecurity perceptive

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Quantum Computing Implications in Generative AI Cybersecurity

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Cyber Security in Industrial Automation Using AI

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Securing (vision-based) autonomous systems: taxonomy, challenges, and defense mechanisms against adversarial threats

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Detection of Physiological Data Tampering Attacks with Quantum Machine Learning

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Enhancing Security Against Adversarial Attacks Using Robust Machine Learning

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Indirect prompt injection in large language models

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Cross-Domain Adversarial Attacks and Robust Defense Mechanisms for Multimodal Neural Networks

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Generative AI for Cybersecurity Applications in Threat Simulation and Defense

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From vulnerability to resilience: Adversarial training and real-time detection for AI security

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Multi-Agent AI Framework for Threat Mitigation and Resilience in Machine Learning Systems

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Trustworthy AI for medical decisions: Adversarially robust and fair machine learning prediction for Parkinson’s disease

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Self-evolving cyber defense: an analytical review of AI-driven autonomous and adversarial systems

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A comprehensive overview of machine learning for intrusion detection in software-defined networking

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Exploiting Adversarial Learning and Topology Augmentation for Open-Set Visual Recognition

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Defending Digital Transactions: Federated Learning for E-Wallet Fraud Detection

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OntoSecAI: Ontology-driven security automation for AI-enabled systems

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Exploring the role of Artificial Intelligence in life-cycle-related studies

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DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehicles

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Hidden adversarial attack on facial biometrics - a comprehensive survey

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Contextualized AI for Cyber Defense: An Automated Survey Using LLMs

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An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction

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Securing Practical Federated Learning-Based Spectrum Sensing for 6G Networks

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Expanding ML-Documentation Standards For Better Security

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Security Risks of AI/ML With a Focus on Reinforcement Learning: A Review and Perspectives From Grid Applications

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Adversarial Attacks on FinTech AI Models: Threats and Mitigation Techniques

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KANPoolConvNeXt: an innovative malware detection method

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Adversarial Shadows in Digital Forensics: New Insights Into File Fragment Classification Vulnerabilities and Defenses

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DLFANet: a Deep-Learned Fusion Attention Network for Robust Text Detection in Complex Scenes

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Machine learning intervention on cyber-hate in code-switch texts: a systematic review with open challenges and solutions

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A review of fairness challenges in natural language processing

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Emerging roles of large language models (LLMs) in smart farming and precision agriculture: security challenges, threat taxonomy, and mitigation strategies

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Explainability-guided counterfactual cyberattacks through surrogate modeling in power systems

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Optimizing Solar Energy Use in Electric Vehicle Charging for Sustainable Transport

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Adversarial Attacks on Faster R-CNN Model for Object Detection in Autonomous Vehicles

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Exploring the nature of generative artificial intelligence in evolving cyber threats

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Future Trends in AI Security

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Understanding and Deobfuscating Textual Data: Managerial Insights and Computational Challenges

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Towards Adversarial-Prevention-Capable Deep Learning Models: Divergent Approach

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Agentic AI for Cybersecurity: Explainable Graph Neural Networks with Federated and Reinforcement Learning for Threat Prediction and Mitigation

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Analyzing and Defending Against Adversarial Attacks on Generative AI in the Cloud (Vulnerabilities)

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Robust Detection of Adversarial Attacks Using Fine-Tuned Transfer Learning based Perceptive Neural Network (PNN) ModelPreprint

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Quantitative Risk Assessment Attack Success Rate, Availability Degradation, and Model Integrity in Federated Learning

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Adversarial Vulnerabilities in Machine Learning: Differential Analysis of Gradient and XGBoost-Based Attack Mechanisms

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Secgenai: A Security-Enhanced Generative AI Framework for Healthcare Chatbots

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Optimized Machine Learning and Ensemble-Based Intrusion Detection for Binary and Multi-Class Cyber Attack Classification

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Phishdet: an Online Phishing Detection System Based on Cnn Model

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Unified Protocols and Pipelines: Rethinking Model Context Serving in the Era of MaaS

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Deep Learning in Cybersecurity: A Review of Attacks, Defenses, and Interpretability Approaches

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ARMOR-Net: Adaptive ML-based Reconnaissance and Object Recognition Network for Smart Defence Surveillance Systems

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The evolving landscape of malware detection: Insights through topic modeling

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TEE-assisted computation over ciphertext: A review

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RandomForestNN Classification for Adversarial AI Black-Box Techniques on MITRE ATT&CK Labeled Data

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Harnessing the Lemurs optimizer for adversarial attacks on deep neural networks

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A Comprehensive Survey on Online AutoML and Adversarial Robustness for IoT and EV Charging Network Security

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Security and privacy frameworks for artificial intelligence-enhanced metaverse applications in smart healthcare

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A Comprehensive Review on Adversarial Attacks and Detection Techniques in Deep Learning Models for Image Analysis

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Adaptive Multi-Layer Defense Framework for Emerging Adversarial Attacks

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Sudhakaran, P., Prakash, P. G. O., Bapu, B. R. T., & Khandagale, H. P. (2026) · Journal of Electronic Testing · doi:10.1007/s10836-026-06261-2

Ethical AI and Responsible Data Use in Education

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You, S., Kuehlkamp, A., Joshi, A., & Nabrzyski, J. (2026) · Blockchains · doi:10.3390/blockchains4040021

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Sarode, D. J., & Surwade, P. D. P. (2025) · International Journal of Innovations in Engineering and Science · doi:10.46335/ijies.2025.10.8.2

Adversarial Machine Learning: Attacks, Defences, and the Road Ahead

Ghritlahare, A. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15354151

ADVERSARIAL MACHINE LEARNING SECURITY: PROTECTING AI SYSTEMS FROM COMPROMISE AND MANIPULATION

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Rattan, P., & Kumar, A. (2026) · EDPACS · doi:10.1080/07366981.2026.2668703

Taxonomy of Adversarial Attacks and Defenses

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