  Human motion generation driven by deep generative models has enabled
compelling applications, but the ability of text-to-motion (T2M) models to
produce realistic motions from text prompts raises security concerns if
exploited maliciously. Despite growing interest in T2M, few methods focus on
safeguarding these models against adversarial attacks, with existing work on
text-to-image models proving insufficient for the unique motion domain. In the
paper, we propose ALERT-Motion, an autonomous framework leveraging large
language models (LLMs) to craft targeted adversarial attacks against black-box
T2M models. Unlike prior methods modifying prompts through predefined rules,
ALERT-Motion uses LLMs' knowledge of human motion to autonomously generate
subtle yet powerful adversarial text descriptions. It comprises two key
modules: an adaptive dispatching module that constructs an LLM-based agent to
iteratively refine and search for adversarial prompts; and a multimodal
information contrastive module that extracts semantically relevant motion
information to guide the agent's search. Through this LLM-driven approach,
ALERT-Motion crafts adversarial prompts querying victim models to produce
outputs closely matching targeted motions, while avoiding obvious
perturbations. Evaluations across popular T2M models demonstrate ALERT-Motion's
superiority over previous methods, achieving higher attack success rates with
stealthier adversarial prompts. This pioneering work on T2M adversarial attacks
highlights the urgency of developing defensive measures as motion generation
technology advances, urging further research into safe and responsible
deployment.
