Traffic Analysis

TL;DR for operators Encrypted LLM connections can conceal prompts and responses while exposing recognizable patterns of AI activity. In a controlled benchmark, classifiers using only encrypted response metadata identify broad prompt categories with 67.37% mean accuracy, against a 16.67% chance baseline. Traffic from collaborative agents also reveals task categories, including when only part of the team’s communication is visible. ...

TL;DR for operators A federated-learning client repeatedly downloads a model, computes locally, and uploads an update while the mobile network assigns radio resources to each step. Encryption hides the transmitted contents, but not the timing, direction, allocation size, and recurring cadence created by this training cycle. FLINT1 reconstructs those scheduling traces and uses them to classify CNN, RNN, and Transformer families. With a 300-second observation window, it reaches a macro F1 of 0.930 ± 0.021 in the evaluated closed-world testbed. ...