DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models
The paper introduces DarkLLM, a novel framework for generating adversarial attacks using large language models. This approach allows for the translation of natural-language attack instructions into effective visual perturbations across various models. The authors demonstrate that DarkLLM can produce highly effective attacks with only 1B parameters, highlighting vulnerabilities in modern foundation models.
- ▪DarkLLM unifies various types of adversarial attacks within a single framework.
- ▪The framework leverages natural-language instruction tuning for flexible adversarial generation.
- ▪Extensive experiments show DarkLLM's effectiveness against multiple models and tasks.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18868 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
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| Cluster | KRXSb44d_9L8 |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Cryptography and Security arXiv:2605.18868 (cs) [Submitted on 15 May 2026] Title:DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models Authors:Ye Sun, Xin Wang, Jiaming Zhang, Yifeng Gao, Yixu Wang, Yifan Ding, Qixian Zhang, Henghui Ding, Xingjun Ma, Yu-Gang Jiang View a PDF of the paper titled DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models, by Ye Sun and 9 other authors View PDF HTML (experimental) Abstract:While vision and multimodal foundation models underpin critical tasks from perception to complex reasoning, they remain highly vulnerable to adversarial attacks.
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