The paper introduces a new class of adversarial attacks that eliminate the need for a backward pass by predicting input gradients from forward-pass hidden states using lightweight linear regression. The authors theoretically derive exact affine conditional gradient means, proving optimality in the idealized NTK regime, and empirically demonstrate that their methods recover most of FGSM’s performance while achieving a 532% increase in throughput on practical finite-width models. This approach offers a simple and general way to generate adversarial examples much faster under realistic wall‑clock constraints.
arXiv:2607. 28959v1 Announce Type: cross Abstract: Adversarial training is one of the most effective defenses against adversarial attacks, yet the computational cost remains prohibitive at modern scales, especially for large language models (LLMs).
MIG‑COW is a new adversarial attack framework that uses momentum‑integrated gradients and a consensus‑orthogonal decomposition to create highly generalizable, visually imperceptible perturbations against deepfake detectors. It achieves a 99.96% white‑box attack success rate on the AADD‑2025 Challenge benchmarks while maintaining high perceptual similarity (SSIM). In black‑box settings, its success rate drops to 7.16%, and adding low‑performing but diverse models to the ensemble can reduce attack effectiveness, highlighting the importance of careful surrogate model selection.
arXiv:2606. 07970v1 Announce Type: cross Abstract: Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with only a few steps of supervised finetuning (SFT) on poisoned datasets.
arXiv:2605. 25194v2 Announce Type: replace Abstract: Adversarial images pose a severe security threat to multimodal large language models through prompt injection.
arXiv:2608. 12535v1 Announce Type: new Abstract: Deep learning models are widely used for time series analysis in domains such as healthcare, finance, energy systems, and environmental monitoring.
arXiv:2507. 01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation.
arXiv:2607. 09532v1 Announce Type: new Abstract: We show how an adversarial model trainer can plant backdoors in a large class of deep, feedforward neural networks.
The paper introduces DLR-Lock, a defense that replaces each pretrained MLP in a language model with a deep low‑rank residual network (DLR‑Net). This substitution increases activation memory linearly with depth during backpropagation and creates architectural mismatches that hinder fine‑tuning. Experiments on large language models show that DLR‑Lock can withstand adaptive attackers while preserving the model’s original performance.
The paper introduces Fed-ADR, a coordinated attack framework where a malicious orchestrator server directs heterogeneous adversarial clients to adapt their gradient updates in real time, thereby evading existing federated learning defenses and drastically reducing global model accuracy. It also presents a lightweight detection mechanism that estimates true client gradients from historical data to spot coordinated attacks, and an in-situ recovery method that restores model performance without restarting training. Experiments on MNIST, Fashion‑MNIST, and CIFAR‑10 show the attack can drop accuracy from over 90% to below 10%, while the defense can recover accuracy to above 90% within a few rounds at a computational cost at least 20× lower than retraining from scratch.
arXiv:2610.27533v1 Announce Type: cross Abstract: Machine learning benchmarks act as guideposts for model development and spur the creation of training datasets intended to improve benchmark scores....
arXiv:2510. 02999v5 Announce Type: replace-cross Abstract: Existing gradient-based jailbreak attacks on Large Language Models (LLMs) typically optimize adversarial suffixes to align the LLM output with predefined target responses.
arXiv:2606. 11409v1 Announce Type: cross Abstract: Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly treating all attacks as equally costly.