The paper surveys transferable attacks on AI systems, noting that such attacks can be crafted in one setting and then applied to others, thereby lowering costs and increasing security risks. It introduces a unified taxonomy across six axes—cross-instance, cross-domain, cross-modality, cross-model, cross-task, and cross-deployment—while distinguishing source-target changes from attacker resources. The survey also links transfer mechanisms to enhancement methods, highlights inconsistencies and gaps in current research, and proposes a research agenda for better benchmarking and defense strategies.
arXiv:2509.20411v3 Announce Type: replace-cross Abstract: Machine learning-based cybersecurity systems are highly vulnerable to adversarial attacks, while Generative Adversarial Networks (GANs) act a...
arXiv:2505. 19840v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) have achieved widespread success yet remain prone to adversarial attacks.
arXiv:2509. 23689v2 Announce Type: replace Abstract: Model Merging (MM) has proven to be an effective alternative to multi-task learning, where several fine-tuned models are merged, without access to the tasks' training data, into one model that retains performance across different tasks.
arXiv:2607. 14006v1 Announce Type: cross Abstract: Penetration testing traditionally evaluates whether adversaries can exploit weaknesses in software, infrastructure, configurations, or operational controls to achieve security-relevant compromise.
arXiv:2609.23894v1 Announce Type: cross Abstract: Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other ag...
arXiv:2606. 03381v1 Announce Type: cross Abstract: Ensuring the protection of Artificial Intelligence (AI) models deployed in military Command and Control (C2) systems and critical infrastructure is essential for maintaining information superiority.
arXiv:2512. 14751v3 Announce Type: replace-cross Abstract: Finetuning pretrained large language models (LLMs) has become the standard paradigm for developing downstream applications.
arXiv:2510. 10982v2 Announce Type: replace-cross Abstract: Recent AI regulations increasingly emphasize the need for mechanisms that preserve the utility of data for AI innovation while preventing misuse, particularly by enforcing purpose limitation in downstream AI applications.
arXiv:2606. 13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross.
arXiv:2608. 07274v1 Announce Type: cross Abstract: Split Federated Learning (SFL) facilitates privacy-preserving collaborative training with reduced client-side overhead.
The paper reviews one‑pixel attacks (OPAs), highlighting their extreme adversarial fragility across domains such as medical diagnosis, autonomous driving, biometrics, and quantum communication. It presents a PRISMA‑guided synthesis of studies from 2017 to 2026, offering a unified taxonomy that covers algorithmic foundations, black‑box evolutionary optimization, hybrid and program‑synthesis attacks, defence mechanisms, interpretability tools, and domain‑specific vulnerabilities. The review identifies dominant Differential Evolution strategies, emerging efficiency‑optimized and saliency‑guided methods, and gaps in dataset diversity, transferability, and evaluation standards, while proposing future research directions and a regulatory framework for robustness testing and AI security governance.
The paper introduces a new framework for creating frequency‑based adversarial attacks that are grounded in an explicit optimization problem. By defining a perturbation constraint set linked to structured, non‑orthogonal transforms, the authors show that attacks can be generated as weighted σ‒projections onto this set, providing a clear geometric characterization. Experiments on standard datasets demonstrate that these attacks are highly effective across both pretrained and robust models, even on unseen architectures.