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We present Prognosis, a framework offering automated closed-box learning andanalysis of models of network protocol implementations. Prognosis can learnmodels that vary in abstraction level from simple deterministic automata tomodels containing data operations, such as register updates, and can be used tounlock a variety of analysis techniques -- model checking temporal properties,computing differences between models of two implementations of the sameprotocol, or improving testing via model-based test generation. Prognosis ismodular and easily adaptable to different protocols (e.g., TCP and QUIC) andtheir implementations. We use Prognosis to learn models of (parts of) threeQUIC implementations -- Quiche (Cloudflare), Google QUIC, and Facebook mvfst --and use these models to analyze the differences between the variousimplementations. Our analysis provides insights into different design choicesand uncovers potential bugs. Concretely, we have found critical bugs inmultiple QUIC implementations, which have been acknowledged by the developers.

Prognosis: Closed-Box Analysis of Network Protocol Implementations