Download AFLNet and compile it. We have tested AFLNet on Ubuntu 18.04 and Ubuntu 16.04 64-bit and it would also work on all environments that support the vanilla AFL and graphviz.

Server fuzzing is difficult. Unlike simple command-line tools, servers feature a massive state space that can be traversed effectively only with well-defined sequences of input messages. Valid sequences are specified in a protocol. In this paper, we present AFLNET, the first greybox fuzzer for protocol implementations. Unlike existing protocol fuzzers, AFLNET takes a mutational approach and ...

Sequence Mutator 使用具有协议感知能力的变异算子增强了 AFL 的 fuzz_one方法。 AFLNET 是一种基于突变的模糊测试方法，即从语料库中选择种子消息序列并进行突变以生成新序列。与从头开始生成新消息序列的现有基于生成的方法相比，有几个优点。

paper, we present AFLNET, the first greybox fuzzer for protocol implementations. Unlike existing protocol fuzzers, AFLNET takes a mutational approach and uses state-feedback to guide the fuzzing process. AFLNET is seeded with a corpus of recorded message exchanges between the server and an actual client.

AFLNetLegion is greybox fuzzer for protocol implementations based on AFLNet, a state-of-the-art fuzzer for network servers.It extends Legion's algorithms to selecting server states and request sequence.

Dec 29, 2024 · Protocol implementations are stateful which makes them difficult to test: Sending the same test input message twice might yield a different response every time. Our proposal to consider a sequence of messages as a seed for coverage-directed greybox fuzzing, to associate each message with the corresponding protocol state, and to maximize the coverage of both the state space and the code was ...

TY - GEN. T1 - AFLNET. T2 - International Conference on Software Testing, Verification and Validation 2020. AU - Pham, Van-Thuan. AU - Bohme, Marcel

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Dec 10, 2023 · This paper builds upon a previous study (AVR-Net) and introduces a novel multi-modal speaker diarization system, AFL-Net. The proposed AFL-Net incorporates dynamic lip movement as an additional modality to enhance the identity distinction.

AFLNET integrates automated state model inference with coverage-guided fuzzing, allowing both to work in tandem: fuzzing generates new message sequences to cover