  With the significant development of large models in recent years, Large
Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across
a wide range of multimodal understanding and reasoning tasks. Compared to
traditional Large Language Models (LLMs), LVLMs present great potential and
challenges due to its closer proximity to the multi-resource real-world
applications and the complexity of multi-modal processing. However, the
vulnerability of LVLMs is relatively underexplored, posing potential security
risks in daily usage. In this paper, we provide a comprehensive review of the
various forms of existing LVLM attacks. Specifically, we first introduce the
background of attacks targeting LVLMs, including the attack preliminary, attack
challenges, and attack resources. Then, we systematically review the
development of LVLM attack methods, such as adversarial attacks that manipulate
model outputs, jailbreak attacks that exploit model vulnerabilities for
unauthorized actions, prompt injection attacks that engineer the prompt type
and pattern, and data poisoning that affects model training. Finally, we
discuss promising research directions in the future. We believe that our survey
provides insights into the current landscape of LVLM vulnerabilities, inspiring
more researchers to explore and mitigate potential safety issues in LVLM
developments. The latest papers on LVLM attacks are continuously collected in
https://github.com/liudaizong/Awesome-LVLM-Attack.
