Researchers have identified a significant privacy vulnerability in modern text-to-video AI systems, demonstrating that these models can unintentionally reveal whether specific videos were used during training. The findings were presented in a new study titled VidLeaks: Membership Inference Attacks Against Text-to-Video Models, accepted at the USENIX Security Symposium 2026
Text-to-video models are trained on massive collections of internet-scale video and text pairs. While this data scale enables realistic and high-quality video generation, it also raises serious concerns around unauthorized use of copyrighted or private content. The new research shows that attackers can exploit subtle memorization behavior in these models to infer training data membership, even when the models are accessed only through black-box APIs.
Why Text-to-Video Models Are Vulnerable
Unlike text or image models, video generation systems must learn both spatial content and temporal dynamics. To manage computational complexity, these models selectively memorize sparse visual anchors such as keyframes and stabilize motion patterns over time. According to the researchers, this design choice creates a new attack surface.
The study identifies two core challenges that traditional membership inference attacks fail to address. First, video models memorize only sparse but distinctive frames, making naïve frame averaging ineffective. Second, video generation introduces stochastic motion, which masks memorization signals when analyzed at the pixel level. These properties required a fundamentally new attack approach.
The VidLeaks Attack Framework
To overcome these challenges, the researchers propose a new framework called VidLeaks. The attack relies on two complementary technical signals.
The first signal, Sparse Reconstruction Fidelity, measures how closely generated frames match keyframes from a target video. Instead of comparing all frames, VidLeaks uses a Top-K similarity strategy that amplifies weak memorization signals tied to sparsely stored visual anchors.
The second signal, Temporal Generative Stability, evaluates how consistently a model reproduces scene-level motion across repeated generations. Member videos exhibit higher temporal stability than non-members, revealing whether the model has memorized specific temporal patterns.
These signals are combined to infer membership under three increasingly restrictive threat models, ranging from supervised access to a realistic query-only setting with no reference data.
Severe Privacy Leakage in Real Models
The researchers evaluated VidLeaks against multiple representative text-to-video models trained under different paradigms. Even in the most restrictive query-only scenario, the attack achieved high accuracy. In some cases, the framework reached over 97 percent AUC, indicating a strong ability to distinguish between member and non-member videos.
Notably, the attack does not require access to the original training captions. Instead, a publicly available video captioning model is used to generate proxy prompts, making the attack practical for real-world auditing and adversarial scenarios.
Implications for AI Privacy and Copyright
These findings provide the first concrete evidence that text-to-video models leak substantial membership information through their generative behavior. This has serious implications for content creators, organizations, and AI providers facing regulatory scrutiny over training data transparency.
The study also shows that lightweight defenses such as minor API parameter randomization are ineffective. According to the authors, meaningful mitigation will likely require training-time interventions, stronger data deduplication, or privacy-preserving learning techniques.
As generative video systems continue to scale, VidLeaks highlights an urgent need for stronger privacy safeguards to prevent unintended disclosure of sensitive training data.
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