Dominik Maier, Otto Bittner, Marc Munier, Julian Beier (TU Berlin)

Common network protocol fuzzers use complex grammars for fuzzing clients and servers with a (semi-)correct input for the server. In contrast, feedback-guided fuzzers learn their way through the target and discover valid input on their own. However, their random mutations frequently destroy all stateful progress when they clobber necessary early communication packets. Deeper into the communication, it gets increasingly unlikely for a coverage-guided fuzzer like AFL++ to explore later stages in client-server communications. Even combinations of both approaches require considerable manual effort for seed and grammar generation, even though sound input sources for servers already exist: their respective clients. In this paper, we present FitM, the Fuzzer in the Middle, a coverage-guided fuzzer for complex client-server interactions. To overcome issues of the State-of-the-Art, FitM emulates the network layer between client and host, fuzzing both server and client at the same time. Once FitM reaches a new step in a protocol, it uses CRIU’s userspace snapshots to checkpoint client and server to continue fuzzing this step in the protocol directly. The combination of domain knowledge gathered from the proper peer, with coverage guided snapshot fuzzing, allows FitM to explore the target extensively. At the same time, FitM reruns earlier snapshots in a probabilistic manner, effectively fuzzing the state space. We show that FitM can reach greater depth than previous tools by comparing found basic blocks, the number of client-server interactions, and execution speed. Based on AFL++’s qemuafl, FitM is an effective and low-effort binary only fuzzer for network protocols, that uncovered overflows in the GNU Inetutils FTP client with minimum effort.

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Tommaso Frassetto (Technical University of Darmstadt), Patrick Jauernig (Technical University of Darmstadt), David Koisser (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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Peng Wang (Indiana University Bloomington), Zilong Lin (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), XiaoFeng Wang (Indiana University Bloomington)

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Bernard Nongpoh (Université Paris Saclay), Marwan Nour (Université Paris Saclay), Michaël Marcozzi (Université Paris Saclay), Sébastien Bardin (Université Paris Saclay)

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