arXiv Computer Vision
Oct 9

Boosting the Local Invariance for Better Adversarial Transferability

The paper introduces LI-Boost, a technique that enhances the local invariance of adversarial perturbations to improve their transferability across models. By demonstrating a positive correlation between local invariance and transferability, the authors show that LI-Boost significantly boosts five categories of transfer-based attacks on ImageNet, including CNNs, ViTs, defense mechanisms, commercial APIs, and vision-language models. Extensive experiments confirm the method’s effectiveness across diverse architectures and attack types.

By Bohan Liu, Xiaosen Wang
arXiv Machine Learning
Oct 9

Fast Adversarial Attacks with Gradient Prediction

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.

By Kamil Ciosek, Aleksandr V. Petrov, Nicol\`o Felicioni, Konstantina Palla
arXiv Machine Learning
Oct 8

Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks

The paper examines the vulnerability of open‑weight large language model safeguards that aim to block harmful usage by assuming new harmful behavior arises only through fine‑tuning. It demonstrates that simple, low‑cost attacks—abliteration and prefilling—can significantly increase attack success rates across multiple benchmarks, and proposes an abliteration‑resistant tuning (ART) method that mitigates these attacks. The study shows that safeguarding defenses must be evaluated against a broader range of attack strategies beyond fine‑tuning.

By Kevin Kuo, Virginia Smith, Chhavi Yadav