做AI安全或内容审核的团队会发现，当前概念擦除方法存在根本漏洞——ConceptAgent无需模型内部信息就能绕过防御。做扩散模型研究的开发者值得看看这篇，它揭示了语义控制动态性的新视角。
扩散模型在文本到图像生成中广泛应用，但概念擦除方法常被用于移除不安全内容。然而，研究表明这些方法往往只是抑制而非彻底消除目标概念，模型仍易受唤醒攻击。现有方法多依赖白盒访问，黑盒场景下的概念唤醒尚未被充分探索。本文提出ConceptAgent，一种无需训练的黑盒多智能体框架，通过从代理引导的噪声状态初始化去噪轨迹，成功唤醒被擦除的概念。实验表明，该方法在黑盒设置下无需模型参数或梯度即可实现准确可控的概念唤醒，揭示了当前概念擦除方法的根本局限性。
Whispers in the Noise: Surrogate-Guided Concept Awakening via a Multi-Agent Framework
Diffusion models (DMs) are widely used for text-to-image generation, but their strong generative capabilities also raise concerns about unsafe or undesirable content. Concept erasure aims to mitigate these risks by removing specific concepts from pretrained models. However, recent studies show that such methods often suppress rather than fully eliminate target concepts, leaving models vulnerable to awakening attacks. Existing approaches primarily rely on white-box access through optimization or inversion, while concept awakening under black-box constraints remains underexplored. In this work, we revisit the denoising process from a trajectory perspective and show that concept erasure mainly disrupts early-stage text-semantic alignment but does not fully prevent semantic information from propagating along the denoising dynamics. As generation proceeds, the model increasingly depends on the evolving noisy state rather than textual conditions, which creates an opportunity to bypass erased mappings. Motivated by this observation, we propose ConceptAgent, a training-free, black-box, multi-agent framework that awakens erased concepts by initializing the denoising trajectory from surrogate-guided noisy states. Extensive experiments demonstrate that ConceptAgent enables accurate and controllable awakening of erased concepts under black-box settings without access to model parameters, gradients, or internal representations. These results highlight fundamental limitations of current concept erasure methods and provide new insights into the dynamic nature of semantic control in DMs.