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New ARMs agent achieves over 90% attack success on Claude 4 Sonnet

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ARMs, an adaptive red-teaming agent designed to systematically assess the safety vulnerabilities of vision-language models (VLMs). ARMs employs diverse, multi-step strategies to elicit harmful outputs, achieving state-of-the-art attack success rates, notably exceeding 90% on Claude 4 Sonnet. This agent has been used to construct ARMs-Bench, a large dataset of over 30,000 red-teaming instances, which significantly improves VLM robustness when used for safety fine-tuning. AI

IMPACT This research introduces a novel method for identifying and mitigating safety risks in multimodal AI models, potentially leading to more robust and secure AI systems.

RANK_REASON The cluster contains a research paper detailing a new method for red-teaming AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ARMs agent achieves over 90% attack success on Claude 4 Sonnet

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13 / 100
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The cluster contains a research paper detailing a new method for red-teaming AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, safety
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhaorun Chen, Xun Liu, Mintong Kang, Jiawei Zhang, Minzhou Pan, Shuang Yang, Bo Li ·

    ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks

    arXiv:2510.02677v2 Announce Type: replace Abstract: As vision-language models (VLMs) gain prominence, their multimodal interfaces also introduce new safety vulnerabilities, making the safety evaluation challenging and critical. Existing red-teaming efforts are either restricted t…

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