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Text of the page (random words):
generation based or mutation based depending on whether inputs are generated from scratch or by modifying existing inputs a fuzzer can be dumb unstructured or smart structured depending on whether it is aware of input structure a fuzzer can be white grey or black box depending on whether it is aware of program structure reuse of existing input seeds edit a mutation based fuzzer leverages an existing corpus of seed inputs during fuzzing it generates inputs by modifying or rather mutating the provided seeds 31 for example when fuzzing the image library libpng the user would provide a set of valid png image files as seeds while a mutation based fuzzer would modify these seeds to produce semi valid variants of each seed the corpus of seed files may contain thousands of potentially similar inputs automated seed selection or test suite reduction allows users to pick the best seeds in order to maximize the total number of bugs found during a fuzz campaign 32 a generation based fuzzer generates inputs from scratch for instance a smart generation based fuzzer 33 takes the input model that was provided by the user to generate new inputs unlike mutation based fuzzers a generation based fuzzer does not depend on the existence or quality of a corpus of seed inputs some fuzzers have the capability to do both to generate inputs from scratch and to generate inputs by mutation of existing seeds 34 aware of input structure edit typically fuzzers are used to generate inputs for programs that take structured inputs such as a file a sequence of keyboard or mouse events or a sequence of messages this structure distinguishes valid input that is accepted and processed by the program from invalid input that is quickly rejected by the program what constitutes a valid input may be explicitly specified in an input model examples of input models are formal grammars file formats gui models and network protocols even items not normally considered as input can be fuzzed such as the contents of databases shared memory environment variables or the precise interleaving of threads an effective fuzzer generates semi valid inputs that are valid enough so that they are not directly rejected from the parser and invalid enough so that they might stress corner cases and exercise interesting program behaviours a smart model based 34 grammar based 33 35 or protocol based 36 fuzzer leverages the input model to generate a greater proportion of valid inputs for instance if the input can be modelled as an abstract syntax tree then a smart mutation based fuzzer 35 would employ random transformations to move complete subtrees from one node to another if the input can be modelled by a formal grammar a smart generation based fuzzer 33 would instantiate the production rules to generate inputs that are valid with respect to the grammar however generally the input model must be explicitly provided which is difficult to do when the model is proprietary unknown or very complex if a large corpus of valid and invalid inputs is available a grammar induction technique such as angluin s l algorithm would be able to generate an input model 37 38 a dumb fuzzer 39 40 does not require the input model and can thus be employed to fuzz a wider variety of programs for instance afl is a dumb mutation based fuzzer that modifies a seed file by flipping random bits by substituting random bytes with interesting values and by moving or deleting blocks of data however a dumb fuzzer might generate a lower proportion of valid inputs and stress the parser code rather than the main components of a program the disadvantage of dumb fuzzers can be illustrated by means of the construction of a valid checksum for a cyclic redundancy check crc a crc is an error detecting code that ensures that the integrity of the data contained in the input file is preserved during transmission a checksum is computed over the input data and recorded in the file when the program processes the received file and the recorded checksum does not match the re computed checksum then the file is rejected as invalid now a fuzzer that is unaware of the crc is unlikely to generate the correct checksum however there are attempts to identify and re compute a potential checksum in the mutated input once a dumb mutation based fuzzer has modified the protected data 41 aware of program structure edit typically a fuzzer is considered more effective if it achieves a higher degree of code coverage the rationale is if a fuzzer does not exercise certain structural elements in the program then it is also not able to reveal bugs that are hiding in these elements some program elements are considered more critical than others for instance a division operator might cause a division by zero error or a system call may crash the program a black box fuzzer 39 35 treats the program as a black box and is unaware of internal program structure for instance a random testing tool that generates inputs at random is considered a blackbox fuzzer hence a blackbox fuzzer can execute several hundred inputs per second can be easily parallelized and can scale to programs of arbitrary size however blackbox fuzzers may only scratch the surface and expose shallow bugs hence there are attempts to develop blackbox fuzzers that can incrementally learn about the internal structure and behavior of a program during fuzzing by observing the program s output given an input for instance learnlib employs active learning to generate an automaton that represents the behavior of a web application a white box fuzzer 40 34 leverages program analysis to systematically increase code coverage or to reach certain critical program locations for instance sage 42 leverages symbolic execution to systematically explore different paths in the program if the program s specification is available a whitebox fuzzer might leverage techniques from model based testing to generate inputs and check the program outputs against the program specification a whitebox fuzzer can be very effective at exposing bugs that hide deep in the program however the time used for analysis of the program or its specification can become prohibitive if the whitebox fuzzer takes relatively too long to generate an input a blackbox fuzzer will be more efficient 43 hence there are attempts to combine the efficiency of blackbox fuzzers and the effectiveness of whitebox fuzzers 44 a gray box fuzzer leverages instrumentation rather than program analysis to glean information about the program for instance afl and libfuzzer utilize lightweight instrumentation to trace basic block transitions exercised by an input this leads to a reasonable performance overhead but informs the fuzzer about the increase in code coverage during fuzzing which makes gray box fuzzers extremely efficient vulnerability detection tools 45 uses edit fuzzing is used mostly as an automated technique to expose vulnerabilities in security critical programs that might be exploited with malicious intent 9 19 20 more generally fuzzing is used to demonstrate the presence of bugs rather than their absence running a fuzzing campaign for several weeks without finding a bug does not prove the program correct 46 after all the program may still fail for an input that has not been executed yet executing a program for all inputs is prohibitively expensive if the objective is to prove a program correct for all inputs a formal specification must exist and techniques from formal methods must be used exposing bugs edit in order to expose bugs a fuzzer must be able to distinguish expected normal from unexpected buggy program behavior however a machine cannot always distinguish a bug from a feature in automated software testing this is also called the test oracle problem 47 48 typically a fuzzer distinguishes between crashing and non crashing inputs in the absence of specifications and to use a simple and objective measure crashes can be easily identified and might indicate potential vulnerabilities e g denial of service or arbitrary code execution however the absence of a crash does not indicate the absence of a vulnerability for instance a program written in c may or may not crash when an input causes a buffer overflow rather the program s behavior is undefined to make a fuzzer more sensitive to failures other than crashes sanitizers can be used to inject assertions that crash the program when a failure is detected 49 50 there are different sanitizers for different kinds of bugs to detect memory related errors such as buffer overflows and use after free using memory debuggers such as addresssanitizer to detect race conditions and deadlocks threadsanitizer to detect undefined behavior undefinedbehaviorsanitizer to detect memory leaks leaksanitizer or to check control flow integrity cfisanitizer fuzzing can also be used to detect differential bugs if a reference implementation is available for automated regression testing 51 the generated inputs are executed on two versions of the same program for automated differential testing 52 the generated inputs are executed on two implementations of the same program e g lighttpd and httpd are both implementations of a web server if the two variants produce different output for the same input then one may be buggy and should be examined more closely validating static analysis reports edit static program analysis analyzes a program without actually executing it this might lead to false positives where the tool reports problems with the program that do not actually exist fuzzing in combination with dynamic program analysis can be used to try to generate an input that actually witnesses the reported problem 53 browser security edit modern web browsers undergo extensive fuzzing the chromium code of google chrome is continuously fuzzed by the chrome security team with 15 000 cores 54 for microsoft edge and internet explorer microsoft performed fuzzed testing with 670 machine years during product development generating more than 400 billion dom manipulations from 1 billion html files 55 54 toolchain edit a fuzzer produces a large number of inputs in a relatively short time for instance in 2016 the google oss fuzz project produced around 4 trillion inputs a week 20 hence many fuzzers provide a toolchain that automates otherwise manual and tedious tasks which follow the automated generation of failure inducing inputs automated bug triage edit main article bug triage automated bug triage is used to group a large number of failure inducing inputs by root cause and to prioritize each individual bug by severity a fuzzer produces a large number of inputs and many of the failure inducing ones may effectively expose the same software bug only some of these bugs are security critical and should be patched with higher priority for instance the cert coordination center provides the linux triage tools which group crashing inputs by the produced stack trace and lists each group according to their probability to be exploitable 56 the microsoft security research centre msec developed the exploitable tool which first creates a hash for a crashing input to determine its uniqueness and then assigns an exploitability rating 57 exploitable probably exploitable probably not exploitable or unknown previously unreported triaged bugs might be automatically reported to a bug tracking system for instance oss fuzz runs large scale long running fuzzing campaigns for several security critical software projects where each previously unreported distinct bug is reported directly to a bug tracker 20 the oss fuzz bug tracker automatically informs the maintainer of the vulnerable software and checks in regular intervals whether the bug has been fixed in the most recent revision using the uploaded minimized failure inducing input automated input minimization edit automated input minimization or test case reduction is an automated debugging technique to isolate that part of the failure inducing input that is actually inducing the failure 58 59 if the failure inducing input is large and mostly malformed it might be difficult for a developer to understand what exactly is causing the bug given the failure inducing input an automated minimization tool would remove as many input bytes as possible while still reproducing the original bug for instance delta debugging is an automated input minimization technique that employs an extended binary search algorithm to find such a minimal input 60 see also edit american fuzzy lop fuzzer concolic testing glitch glitching monkey testing random testing responsible disclosure runtime error detection security testing smoke testing software symbolic execution system testing test automation references edit a b john neystadt february 2008 automated penetration testing with white box fuzzing microsoft retrieved 2009 05 14 barton p miller september 1988 fall 1988 cs736 project list pdf computer sciences department university of wisconsin madison retrieved 2020 12 30 barton p miller lars fredriksen bryan so december 1990 an empirical study of the reliability of unix utilities pdf communications of the acm fuzz testing of application reliability university of wisconsin madison retrieved 2020 12 30 barton p miller david koski cjin p lee vivekananda maganty ravbi murthy ajitkumar natarajan jeff steidl april 1995 fuzz revisited a re examination of the reliability of unix utilities and services pdf computer sciences technical report 1268 university of wisconsin madison justin forrester barton p miller september 2000 an empirical study of the robustness of windows nt applications using random testing pdf 4th usenix windows systems symposium barton p miller gregory cooksey fredrick moore july 2006 an empirical study of the robustness of macos applications using random testing pdf first international workshop on random testing barton p miller mengxiao zhang elisa heymann 2021 the relevance of classic fuzz testing have we solved this one pdf ieee 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