arXiv AI June 9, 2026 By Kien Le, Thai Le

Adversarial Robustness of Activation Steering in Large Language Models

arXiv:2606. 07696v1 Announce Type: cross Abstract: Activation steering has become a popular training-free method to control LLM behavior by injecting precomputed direction vectors into the model's residual stream at inference time.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 5

Steering Vectors are an Adversarial Attack Surface

arXiv:2606. 05958v1 Announce Type: new Abstract: Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning.

By Abzal Aidakhmetov, Donato Crisostomi, Tommaso Mencattini, Adrian Robert Minut, Iacopo Masi, Emanuele Rodol\`a

llms fine-tuning multimodal safety

arXiv AI

Aug 3

Efficient LLM Adversarial Training via Low-Rank Defense and Circuit-Guided Surrogates

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).

By Weiyi He, Yuping Lin, Jiliang Tang, Yue Xing

llms fine-tuning safety

arXiv Machine Learning

Jun 26

Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis

arXiv:2406. 10090v3 Announce Type: replace Abstract: Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization.

By Srishti Gupta, Zhang Chen, Luca Demetrio, Fabio Brau, Xiaoyi Feng, Zhaoqiang Xia, Antonio Emanuele Cin\`a, Maura Pintor, Luca Oneto, Ambra Demontis, Battista Biggio, Fabio Roli

safety

arXiv AI

Jul 3

Safety Targeted Embedding Exploit via Refinement

arXiv:2607. 01859v1 Announce Type: new Abstract: Safety training for large language models (LLMs) is conducted predominantly in English, leaving uncertain how well safety mechanisms generalize to low-resource languages and mixed-language code-switching.

By Joshua Adrian Cahyono

llms rag safety

arXiv Machine Learning

Jul 31

Beyond the Bidirectional Promise: Re-evaluating the Robustness of Diffusion Language Models

arXiv:2607. 27386v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) offer a compelling alternative to autoregressive (AR) generation by enabling bidirectional context and iterative refinement.

By Saurabh Yadav, Badri Narayana Patro, Vijay Srinivas Agneeswaran

llms diffusion safety

arXiv Machine Learning

Jul 13

Optimizing Against Safety Representations: Activation-Guided Adversarial Suffixes and the Geometry of Refusal

arXiv:2607. 08883v1 Announce Type: new Abstract: Behavioral alignment in large language models often masks fragile internal safety representations.

By Ege \c{C}akar, Hannah Guan, Kayden Kehe

llms safety

One email a morning, machine-written

Related stories

Steering Vectors are an Adversarial Attack Surface

Efficient LLM Adversarial Training via Low-Rank Defense and Circuit-Guided Surrogates

Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis

Safety Targeted Embedding Exploit via Refinement

Beyond the Bidirectional Promise: Re-evaluating the Robustness of Diffusion Language Models

Optimizing Against Safety Representations: Activation-Guided Adversarial Suffixes and the Geometry of Refusal