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Computer Science > Computation and Language

arXiv:2402.11753 (cs)
[Submitted on 19 Feb 2024 (v1), last revised 22 Feb 2024 (this
version, v2)]

Title:ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned
LLMs

Authors:Fengqing Jiang, Zhangchen Xu, Luyao Niu, Zhen Xiang, Bhaskar
Ramasubramanian, Bo Li, Radha Poovendran
Download a PDF of the paper titled ArtPrompt: ASCII Art-based
Jailbreak Attacks against Aligned LLMs, by Fengqing Jiang and 6 other
authors
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    Abstract:Safety is critical to the usage of large language models
    (LLMs). Multiple techniques such as data filtering and supervised
    fine-tuning have been developed to strengthen LLM safety.
    However, currently known techniques presume that corpora used for
    safety alignment of LLMs are solely interpreted by semantics.
    This assumption, however, does not hold in real-world
    applications, which leads to severe vulnerabilities in LLMs. For
    example, users of forums often use ASCII art, a form of
    text-based art, to convey image information. In this paper, we
    propose a novel ASCII art-based jailbreak attack and introduce a
    comprehensive benchmark Vision-in-Text Challenge (ViTC) to
    evaluate the capabilities of LLMs in recognizing prompts that
    cannot be solely interpreted by semantics. We show that five SOTA
    LLMs (GPT-3.5, GPT-4, Gemini, Claude, and Llama2) struggle to
    recognize prompts provided in the form of ASCII art. Based on
    this observation, we develop the jailbreak attack ArtPrompt,
    which leverages the poor performance of LLMs in recognizing ASCII
    art to bypass safety measures and elicit undesired behaviors from
    LLMs. ArtPrompt only requires black-box access to the victim
    LLMs, making it a practical attack. We evaluate ArtPrompt on five
    SOTA LLMs, and show that ArtPrompt can effectively and
    efficiently induce undesired behaviors from all five LLMs.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2402.11753 [cs.CL]
          (or arXiv:2402.11753v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2402.11753
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Luyao Niu [view email]
[v1] Mon, 19 Feb 2024 00:43:31 UTC (968 KB)
[v2] Thu, 22 Feb 2024 18:40:03 UTC (968 KB)
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