VL-Trojan: Multimodal Instruction Backdoor Attacks against
  Autoregressive Visual Language Models
Jiawei Liang Siyuan Liang Man Luo Aishan Liu Dongchen Han Ee-Chien Chang Xiaochun Cao
Autoregressive Visual Language Models (VLMs) showcase impressive few-shot
learning capabilities in a multimodal context. Recently, multimodal instruction
tuning has been proposed to further enhance instruction-following abilities.
However, we uncover the potential threat posed by backdoor attacks on
autoregressive VLMs during instruction tuning. Adversaries can implant a
backdoor by injecting poisoned samples with triggers embedded in instructions
or images, enabling malicious manipulation of the victim model's predictions
with predefined triggers. Nevertheless, the frozen visual encoder in
autoregressive VLMs imposes constraints on the learning of conventional image
triggers. Additionally, adversaries may encounter restrictions in accessing the
parameters and architectures of the victim model. To address these challenges,
we propose a multimodal instruction backdoor attack, namely VL-Trojan. Our
approach facilitates image trigger learning through an isolating and clustering
strategy and enhance black-box-attack efficacy via an iterative character-level
text trigger generation method. Our attack successfully induces target outputs
during inference, significantly surpassing baselines (+62.52\%) in ASR.
Moreover, it demonstrates robustness across various model scales and few-shot
in-context reasoning scenarios.