FitM: Binary-Only Coverage-Guided Fuzzing for Stateful Network Protocols

Abstract

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.

On the (in)Efficiency of Fuzzing Network Protocols

“…Maier et al [11] introduced the Fuzzer in the Middle (FitM) for fuzzing network protocols. Instead of using a simulated socket library, FitM intercepts the emulated system-calls inside the QEMU emulator and sends the input messages to the SUT without the network communication overhead.…”

Adaptive scheduling-based fine-grained greybox fuzzing for cloud-native applications

“…For instance, taint analysis [16,20,28], concolic execution [6,29,30], static analysis [31][32][33], deep learning [34,35] and reinforcement learning [11,22,36] are used to boost fuzzer performance. On the other hand, some work has attempted to transform fuzzing to better test specific types of targets, such as JIT compilers [37][38][39][40], OS kernel [23,[41][42][43], protocol [44,45], rounter [46,47], and smart contracts [48,49]. For example, to find JIT compiler vulnerabilities, some fuzzers use an abstract syntax tree to represent and generate JavaScript code as seeds.…”

CrabSandwich: Fuzzing Rust with Rust (Registered Report)

“…Researchers continue to refine graybox fuzzing techniques by developing new methodologies for mutating test cases [3,21,33,39], managing test cases [11,43,48], providing feedback on program behavior [15,23,35,47,49] and more [13,34,41,50].…”