2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) 2021
DOI: 10.1109/ase51524.2021.9678671
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InstruGuard: Find and Fix Instrumentation Errors for Coverage-based Greybox Fuzzing

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Cited by 7 publications
(4 citation statements)
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“…Compared with AFL, Instrim can provide a 1.04-1.78x speed up during fuzz testing. Liu et al [37] implemented InstruGuard to detect instrumentation errors by static analysis on target binaries and fix them with a general solution based on binary rewriting. Experiments show that InstruGuard can correct the instrumentation errors of different fuzzers and help to find more bugs.…”
Section: Coverage-guided Grey Box Fuzz Testingmentioning
confidence: 99%
“…Compared with AFL, Instrim can provide a 1.04-1.78x speed up during fuzz testing. Liu et al [37] implemented InstruGuard to detect instrumentation errors by static analysis on target binaries and fix them with a general solution based on binary rewriting. Experiments show that InstruGuard can correct the instrumentation errors of different fuzzers and help to find more bugs.…”
Section: Coverage-guided Grey Box Fuzz Testingmentioning
confidence: 99%
“…Fuzzing ranks as one of the most popular software testing techniques used to evaluate the SUT concerning security and robustness properties. [23][24][25] Typically, fuzzing detects anomalies by providing malformed input to the SUT and monitoring the execution state, which can be identified by information such as memory and security oracles. According to the dependence degree of program analysis on the program source code, fuzzers can be divided into white-box, gray-box, and black-box.…”
Section: Fuzzingmentioning
confidence: 99%
“…Scheduling [14,[24][25][26] Instrumentation [27][28][29][30] Hybrid Fuzzing [31][32][33][34] Sanitizer [35][36][37][38] Directed Fuzzing [17,[39][40][41] Others [16,[42][43][44][45][46][47] Fuzzing Evaluation Fuzzing Benchmarks [48][49][50][51] Empirical Study [52][53][54][55][56] Targeting Specific Types of Bugs Di erential Fuzzing [57][58][59] Performance Fuzzing [20,[60][61][62] Others [21,63,64] Targeting Specific Kinds of Targets Kernel Fuzzing [65][66]…”
Section: Sub-categories and Referencesmentioning
confidence: 99%
“…The genetic algorithm used in APICRAFT (Algorithm 4) is based on NSGA-II, which has a similar basic workflow to the classic genetic algorithm [162] (line [25][26][27][28][29][30] with the exception of the chromosome ranking strategy, which handles multiple objectives (v line [11][12][13][14][15][16]. In NSGA-II, an objective is a metric that has a score formula to measure a chromosome from an independent dimension, and each chromosome has more than one objective (i.e., it is multi-objective).…”
Section: Nsga-iimentioning
confidence: 99%