Et Tu, MacBook? Unprivileged Keystroke Inference and Context Profiling via the Built-in IMU Side Channel
Unprivileged access to MacBook motion sensors can infer keystrokes with up to 97.5 percent accuracy.
The paper shows that inertial sensors in recent Apple MacBooks leak typing vibrations through an unprivileged IOKit interface, supplemented by HIDIdleTime and CGEventSource metadata. Their attack, BRUTUS, infers which key was pressed, the desk surface, and who is typing. Character recovery reaches 89.1% to 97.5%, and language-model assistance reconstructed some sentences at 100% accuracy. The authors say access to built-in IMU sensors should be strictly regulated.
- Unprivileged IOKit access exposes MacBook IMU vibration data.
- BRUTUS recovers characters with 89.1% to 97.5% accuracy.
- Language models reconstructed some sentences at 100% accuracy.
- The same signals profile the typist and desk surface.
- Authors urge stricter controls on built-in IMU access.
Full article203 words · extracted from arxiv.org · click to collapse
Recent generations of Apple MacBooks embed an inertial measurement unit (IMU) within their unibody chassis for device orientation and motion sensing. However, this IMU inadvertently captures not only intended device-level information but also subtle physical vibrations from user interactions and the surrounding environment. These signals establish a novel, previously unexplored side channel. We uncover a vulnerability allowing non-root access to IMU data via an IOKit driver, alongside two content-free system metadata interfaces (HIDIdleTime and CGEventSource) that further enrich the side-channel leakage. Through rigorous characterization of the IMU data, we reveal that the leakage spans three core dimensions: (1) keystroke identity (which key is typed), (2) desk surface (where the laptop is placed), and (3) user behavior (who is typing). Leveraging these findings, we introduce BRUTUS, the first comprehensive unprivileged side-channel attack targeting built-in IMU sensors on Apple MacBooks. BRUTUS achieves a character-level accuracy of 89.1% to 97.5% in key recovery. Furthermore, aided by language models, it can successfully reconstruct certain sentences with 100% accuracy. For user identification and environment profiling, BRUTUS correctly discovers user and environment profiles without labels and correctly assigns subsequent segments to their corresponding profiles. Ultimately, this work highlights the urgent necessity of strictly regulating access to built-in IMU sensors.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.21569