HiddenSplat: Injecting View-Specific Content into 3D Gaussian Splatting via “Invisible” Gaussians

3D Gaussian Splatting (3DGS) has rapidly emerged as a leading representation for photorealistic novel-view synthesis and is increasingly adopted in AR/VR, robotics, and digital content creation. As 3DGS scenes become widely distributed and reused as shareable assets, they introduce an underexplored security risk: an adversary can inject a set of structurally optimized Gaussians into a pretrained 3DGS scene such that a chosen target content (e.g., a sensitive image, logo, watermark, or hidden message) appears clearly when the scene is rendered from a specific target viewpoint while remaining visually imperceptible from other viewpoints. We term this attack HiddenSplat. Unlike prior approaches, HiddenSplat efficiently operates in both white-box and gray-box settings, enables the attacker to arbitrarily select the target viewpoint, and requires no auxiliary encoders or decoders. Extensive experiments across diverse scenes and target patterns demonstrate that HiddenSplat achieves high-fidelity target reconstruction at the attack viewpoint while keeping the injected content effectively “invisible” from non-target views. Our findings reveal a new, practical attack surface to 3DGS assets and highlight the need for efficient defenses against such vulnerabilities.