Alex Ciechonski

Alex Ciechonski, Fabio Palmese, Alessandro E. C. Redondi et al.

The rapid proliferation of Internet of Things (IoT) devices introduces significant security challenges due to limited visibility and weak device-level guarantees. Accurate and timely identification of devices is essential for enforcing network policies and detecting unauthorised hardware, yet existing approaches often rely on long-term traffic observation, payload inspection, or infrastructure-dependent features. In this paper, we investigate whether IoT devices can be reliably identified during the early stages of network attachment using only passive traffic analysis. We propose a lightweight approach based on flow-level features extracted from metadata, avoiding payload inspection and active probing. Through systematic evaluation across multiple observation windows, we show that device-specific signatures emerge within the first few seconds of communication, enabling high-accuracy identification (up to 99%) across 37 IoT devices. Notably, extending the observation window does not consistently improve performance and may slightly degrade accuracy, indicating that the most discriminative behaviour occurs during initial device startup. These findings demonstrate the feasibility of fast, privacy-preserving IoT device identification at the network edge, supporting real-time enforcement, device inventory, and anomaly detection in practical deployments.

Yash Vekaria, Aurelio Loris Canino, Jonathan Levitsky et al.

Generative AI (GenAI) browser assistants integrate powerful capabilities of GenAI in web browsers to provide rich experiences such as question answering, content summarization, and agentic navigation. These assistants, available today as browser extensions, can not only track detailed browsing activity such as search and click data, but can also autonomously perform tasks such as filling forms, raising significant privacy concerns. It is crucial to understand the design and operation of GenAI browser extensions, including how they collect, store, process, and share user data. To this end, we study their ability to profile users and personalize their responses based on explicit or inferred demographic attributes and interests of users. We perform network traffic analysis and use a novel prompting framework to audit tracking, profiling, and personalization by the ten most popular GenAI browser assistant extensions. We find that instead of relying on local in-browser models, these assistants largely depend on server-side APIs, which can be auto-invoked without explicit user interaction. When invoked, they collect and share webpage content, often the full HTML DOM and sometimes even the user's form inputs, with their first-party servers. Some assistants also share identifiers and user prompts with third-party trackers such as Google Analytics. The collection and sharing continues even if a webpage contains sensitive information such as health or personal information such as name or SSN entered in a web form. We find that several GenAI browser assistants infer demographic attributes such as age, gender, income, and interests and use this profile--which carries across browsing contexts--to personalize responses. In summary, our work shows that GenAI browser assistants can and do collect personal and sensitive information for profiling and personalization with little to no safeguards.