The rapid development of generative artificial intelligence has made text to
video models essential for building future multimodal world simulators.
Jedoch, these models remain vulnerable to jailbreak attacks, where specially
crafted prompts bypass safety mechanisms and lead to the generation of harmful
or unsafe content. Such vulnerabilities undermine the reliability and security
of simulation based applications. In this paper, we propose T2VShield, a
comprehensive and model agnostic defense framework designed to protect text to
video models from jailbreak threats. Our method systematically analyzes the
input, model, and output stages to identify the limitations of existing
defenses, including semantic ambiguities in prompts, difficulties in detecting
malicious content in dynamic video outputs, and inflexible model centric
mitigation strategies. T2VShield introduces a prompt rewriting mechanism based
on reasoning and multimodal retrieval to sanitize malicious inputs, along with
a multi scope detection module that captures local and global inconsistencies
across time and modalities. The framework does not require access to internal
model parameters and works with both open and closed source systems. Extensive
experiments on five platforms show that T2VShield can reduce jailbreak success
rates by up to 35 percent compared to strong baselines. We further develop a
human centered audiovisual evaluation protocol to assess perceptual safety,
emphasizing the importance of visual level defense in enhancing the
trustworthiness of next generation multimodal simulators.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

PDF herunterladen: