Pengtao Xie

Modern language models remain vulnerable to backdoor attacks via poisoned data, where training inputs containing a trigger are paired with a target output, causing the model to reproduce that behavior whenever the trigger appears at inference time. Recent work has emphasized stealthy attacks that stress-test data-curation defenses using stylized artifacts or token-level perturbations as triggers, but this focus leaves a more practically relevant threat model underexplored: backdoors tied to naturally occurring semantic concepts. We introduce SteganoBackdoor, an optimization-based framework that constructs SteganoPoisons, steganographic poisoned training examples in which a backdoor payload is distributed across a fluent sentence while exhibiting no representational overlap with the inference-time semantic trigger. Across diverse model architectures, SteganoBackdoor achieves high attack success under constrained poisoning budgets and remains effective under conservative data-level filtering, highlighting a blind spot in existing data-curation defenses.

Generative AI models pose a significant challenge to intellectual property (IP), as they can replicate unique artistic styles and concepts without attribution. While watermarking offers a potential solution, existing methods often fail in complex scenarios where multiple concepts (e.g., an object and an artistic style) are composed within a single image. These methods struggle to disentangle and attribute each concept individually. In this work, we introduce TokenTrace, a novel proactive watermarking framework for robust, multi-concept attribution. Our method embeds secret signatures into the semantic domain by simultaneously perturbing the text prompt embedding and the initial latent noise that guide the diffusion model's generation process. For retrieval, we propose a query-based TokenTrace module that takes the generated image and a textual query specifying which concepts need to be retrieved (e.g., a specific object or style) as inputs. This query-based mechanism allows the module to disentangle and independently verify the presence of multiple concepts from a single generated image. Extensive experiments show that our method achieves state-of-the-art performance on both single-concept (object and style) and multi-concept attribution tasks, significantly outperforming existing baselines while maintaining high visual quality and robustness to common transformations.