Large language models (LLMs) are increasingly deployed over knowledge bases for efficient knowledge retrieval and question answering. However, LLMs can inadvertently answer beyond a user’s permission scope, leaking sensitive content and complicating deployment under fine-grained access-control requirements. We identify a geometric regularity in intermediate activations: representations associated with different permission scopes exhibit consistent and distinguishable structure in activation space. Building on this observation, we propose Activation-Space Anchored Access Control (AAAC), a training-free framework for multi-class permission control. AAAC constructs a compact bank of permission-specific activation anchors from a small offline calibration set and requires no model fine-tuning. At inference time, the trusted permission metadata is used by the controller to steer query activations toward authorized anchors and away from unauthorized ones, thereby mitigating the risk of over-privileged generation. Across three LLM families, AAAC achieves permission violation rates of 0.005–0.009 and prompt-based attack success rates of 0.084–0.101, while maintaining competitive utility with 10.3%–25.2% inference overhead.