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Honggfuzz €? Simple Command Line Software Fuzzing Tool

It provides an easy way to instrument a target (unfortunately it did not work for this target but we will see how I was able to overcome those issues), it has some very powerful commands such as mutate only X amount of bytes (using the -F parameter), an easy to use command line and it uses AddressSanitzer instrumentation for software coverage saving all the unique crashes as well as the coverage files which hit new block codes.

Honggfuzz – Simple Command Line Software Fuzzing Tool

CI Fuzz CLI is an easy-to-use fuzzing tool, that enables you to integrate and run fuzz tests directly from your command line. I chose this tool for this tutorial, as it is particularly user-friendly and allows developers to set up and run a fuzz test with only three commands.

The term "fuzz" originates from a fall 1988 class project[2] in the graduate Advanced Operating Systems class (CS736), taught by Prof. Barton Miller at the University of Wisconsin, whose results were subsequently published in 1990.[3] To fuzz test a UNIX utility meant to automatically generate random input and command-line parameters for the utility. The project was designed to test the reliability of UNIX command line programs by executing a large number of random inputs in quick succession until they crashed. Miller's team was able to crash 25 to 33 percent of the utilities that they tested. They then debugged each of the crashes to determine the cause and categorized each detected failure. To allow other researchers to conduct similar experiments with other software, the source code of the tools, the test procedures, and the raw result data were made publicly available.[4] This early fuzzing would now be called black box, generational, unstructured (dumb) fuzzing.

Abstract:Evolutionary fuzzers generally work well with typical software programs because of their simple algorithm. However, there is a limitation that some paths with complex constraints cannot be tested even after long execution. Fuzzers based on concolic execution have emerged to address this issue. The concolic execution fuzzers also have limitations in scalability. Recently, the gradient-based fuzzers that use a gradient to mutate inputs have been introduced. Gradient-based fuzzers can be applied to real-world programs and achieve high code coverage. However, there is a problem that the existing gradient-based fuzzers require heavyweight analysis or sufficient learning time. In this paper, we propose a new type of gradient-based fuzzer, MaxAFL, to overcome the limitations of existing gradient-based fuzzers. Our approach constructs an objective function through fine-grained static analysis. After constructing a well-made objective function, we can apply the gradient-based optimization algorithm. We use a modified gradient-descent algorithm to minimize our objective function and propose some probabilistic techniques to escape local optimum. We introduce an adaptive objective function which aims to explore various paths in the program. We implemented MaxAFL based on the original AFL. MaxAFL achieved increase of code coverage per time compared with three other fuzzers in six open-source Linux binaries. We also measured cumulative code coverage per total execution, and MaxAFL outperformed the other fuzzers in this metric. Finally, MaxAFL can also find more bugs than the other fuzzers.Keywords: fuzzing; optimization; gradient descent; test automation

Since fuzzing with pure random input has a small chance of reaching large parts of the code, sophisticated fuzzing tools make use of additional information, such as input structure or code coverage, to generate inputs. A simple but effective approach is to gather code coverage information during input processing of the SUT and collect inputs that trigger previously unreached code parts. This growing collection of inputs, called corpus, is used continuously to generate further inputs.

Fuzzing user (software) applications is perhaps the best-established use of fuzzing, and there are several consolidated techniques for gathering feedback from a target process. For example, the OSS-Fuzz (Serebryany 2017) project revealed over 30,000 bugs in 500 open source projects by using coverage-guided fuzzers such as libFuzzer (LLVM 2021), AFL++ (Fioraldi et al. 2020), and honggfuzz (Swiecki 2021).

Oh et al. (2015) present a simple Dynamic Binary Instrumentation (DBI) method for embedded systems without any dependency on the operating system. They connect the target device with a debugger and insert software breakpoints at manually chosen locations. When a breakpoint is reached, the instrumentation framework is notified, and the breakpoint is removed for further execution. This method enables observation of manually selected, executed code parts in \(O_t_i\) and could be used for coverage-guided fuzzing of any embedded system that provides a suitable debugger. According to the measurements of the authors, the overhead of this method is only around 1%. However, the measurements have only been performed on one device.

The other disadvantage of emulators is the setup and configuration effort required to imitate the whole execution environment. However, with the actual hardware, there is an environment already present in which the embedded software runs as expected. Therefore, we see more research potential in performing fuzzing on the actual hardware and extracting feedback from existing functionalities e.g. debug interfaces. Common embedded debugging tools from Lauterbach (Lauterbach 2021) or SEGGER (Segger 2021) provide real-time tracing mechanisms for a wide variety of microcontrollers, which may be used for fuzzing feedback.

The first command prevents gdb from breaking long lines, e.g., when printing backtraces. The second command executes the fuzzing target, feeding it either the path to the sample or the actual sample content. The third command generates the backtrace. If the execution of the binary finishes without a crash, or times out, the evaluation is stopped and no backtrace is generated. 076b4e4f54


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