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MTFuzz: Fuzzing with a Multi-Task Neural Network
[article]
2020
arXiv
pre-print
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In this paper, we address these issues by using a Multi-Task Neural Network that can learn a compact embedding of the input space based on diverse training samples for multiple related tasks (i.e., predicting ...
Our results show that MTFuzz uncovers 11 previously unseen bugs and achieves an average of 2x more edge coverage compared with 5 state-of-the-art fuzzer on 10 real-world programs. ...
We demonstrate that MTFuzz can transfer a NN learnt on one program to other similar programs.
CONCLUSION This paper presents MTFuzz, a multi-task neural-network fuzzing framework. ...
arXiv:2005.12392v1
fatcat:46luwyy3mja5naldfaz2lkcula
Refined Grey-Box Fuzzing with SIVO
[article]
2021
arXiv
pre-print
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We design and implement from scratch a new fuzzer called SIVO that refines multiple stages of grey-box fuzzing. ...
First, SIVO refines data-flow fuzzing in two ways: (a) it provides a new taint inference engine that requires only logarithmic in the input size number of tests to infer the dependency of all program branches ...
MTFuzz [30] trains a multiple-task neural network to infer the relationship between program inputs and different kinds of edge coverage to guide input mutation. ...
arXiv:2102.02394v2
fatcat:3zwqfzx53jhmjmhyakl3sjqtce
Fine Grained Dataflow Tracking with Proximal Gradients
[article]
2021
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
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2021 pre-print arXiv:1909.03461v5 fatcat:gwv2t2iz5vbijncu2dxcllqwgiOther Versions
Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel information leak detection ...
We introduce proximal gradient analysis (PGA), a novel, theoretically grounded approach that can track more accurate and fine-grained dataflow information. ...
NEUZZ, MTFuzz and Neutaint train neural networks to predict program branch behavior and use the network's gradients to guide the mutation algorithm [43] [44] [45] . ...
arXiv:1909.03461v6
fatcat:2kuubtwdfbadpj7r5j37rz4gp4