Forge: A Robust Multi-tab Website Fingerprinting Attack via Blind Source Separation

While Tor's strong anonymity shields users' privacy, it also enables malicious activities, motivating attacks that bypass its protections. Website Fingerprinting (WF) has emerged as a primary threat in this domain. However, existing WF methods struggle with realistic multi-tab browsing scenarios, often relying on prior knowledge of the number of open tabs and lacking robustness against network noise and defenses. To address these challenges, we propose Forge, a robust WF attack framework inspired by the classic cocktail party problem. Specifically, Forge reframes multi-tab WF as a task of Blind Source Separation(BSS), decomposing mixed traffic into individual signals without requiring a predefined number of concurrent tabs. A robust website identifier then classifies separated components using a dual-domain attention mechanism across time and frequency, allowing Forge to effectively resist WF defenses and network noise. We evaluate our model on a comprehensive collection of datasets covering open-world, defense-enabled, and dynamic scenarios. The results demonstrate that Forge can improve Mean Average Precision by 78.6% over the state-of-the-art average in the challenging multi-tab open-world scenario.