Compared to regular whitebox fuzzing, grammar based whitebox fuzzing increased coverage of the code generation module of the ie7 javascript interpreter from 53% to 81% while using three times fewer. Modelbased whitebox fuzzing for program binaries marcel bohme. The second class of fuzzing techniques we call them grammar based fuzzing techniques. Several different types of penetration tests were performed including network protocol and command line fuzzing, session hijacking and credential theft.
Hack, art, and science february 2020 communications. This results in a higher rate of successful fuzzing and the location of. Typically, fuzzers are used to test programs that take structured inputs. Institute for software technology modelbased fuzzing i testing technique, which generates random or semirandom inputs through a fuzz generator. If the input can be modelled by a formal grammar, a smart generationbased fuzzer would instantiate the production. White box fuzzing for attackermemorysafety in os kernel packetfile parsers. July 2006 month of browser bugs simple yet effective. These applications process their inputs in stages, such as lexing, parsing.
The program is then monitored for exceptions such as crashes, or failing builtin code assertions or for finding potential memory leaks. Instrumentation adds runtime overhead, requires that we modify the program being. Blackbox testing based on colorful taint analysis springerlink. Differential seed scheduling works for greybox fuzzers that generate seeds based on runtime code coverage measurement. Automatic and lightweight grammar generation for fuzz testing. Mixed concrete and symbolic execution is an important technique for finding and understanding software bugs, including securityrelevant ones. The automated testing of such programs is nontrivial. Brute force vulnerability discovery, 2007, isbn 0321446119 h. Security research intern, intel coporation jun 2017 sep 2017, hillsboro, or concurrent firmware verification with llvmboogiebased software verification tools.
The problem with spending all this effort on coverage tracing is that. Compared to regular whitebox fuzzing, grammarbased whitebox fuzzing increased coverage of the code generation module of the ie7 javascript interpreter from 53% to 81% while using three times fewer. Joint meeting of the european software engineering conference. It simply consists in ran domly modifying wellformed inputs and. We have implemented grammarbased whitebox fuzzing and evaluated it on a large application, the javascript interpreter of the internet explorer 7 webbrowser. The program is then monitored for exceptions such as crashes, failing builtin code assertions, or potential memory leaks. Blackbox and whitebox fuzzing are fully automatic, and have historically proved to be very effective at. Grammarbased whitebox fuzzing proceedings of the 29th. Blackbox random fuzzing, grammarbased fuzzing and whitebox fuzzing are. Existing grammarbased fuzzers are surprisingly inefficient, though. Grammarbased fuzzing eases the difficulties in fuzzing and digs out deepseated vulnerabilities to some degree.
Demott, charles miller, fuzzing for software security testing and quality assurance, 2008, isbn 9781596932142. Results of experiments show that grammarbased whitebox. May 30, 2011 software vulnerability detection is one of the most important methods for guaranteeing software security. In the chapter on mutationbased fuzzing, we have seen how to use extra hints such as sample input files to speed up test generation. However, the effectiveness of whitebox fuzzing is limited when testing applications with highlystructured inputs, such as compilers and interpreters.
Results of experiments show that grammar based whitebox fuzzing outperforms whitebox fuzzing, blackbox fuzzing and grammar based blackbox fuzzing in overall code coverage, while us. Cloud penetration testing think research expose think. The random fuzzing we employed in our study can be improved by taking into account specific properties of the object being studied. Software vulnerability detection is one of the most important methods for guaranteeing software security. The generation fuzzing engine must have a template or other form of input vectors, which acts as a provider of input data for the generator. Brute force vulnerability discovery, 2007, isbn 0321446119. A whitebox approach for automated security testing of. White box fuzzing takes advantage of its access to the source code and design speci.
Citeseerx citation query grammarbased whitebox fuzzing. Compared to regular whitebox fuzzing, grammarbased whitebox fuzzing increased coverage of the code generation module of the ie7 javascript interpreter from 53% to 81% while using three times. We then discuss how to check grammarbased constraints for contextfree grammars section 2. A whitebox approach for automated security testing of android. The experiment results showed that the ymir system was capable of generating fuzzing grammars that can raise branch coverage for activex control using highlystructured input string by 1550%. Now these tools are often easy to use, because the fuzzing tool itself is able to look at the code and decide what inputs to generate to go to different parts of that target programs code. Unlike blackbox fuzzing, white box fuzzing uses program analysis to understand the impact of the input and increase code coverage of the sut. Grammarbasedwhiteboxfuzzing in this section, we recall the basic notions behind whitebox fuzzing section 2.
The state of the art richard mcnally, ken yiu, duncan grove and damien gerhardy command, control, communications and intelligence division defence science and technology organisation dstotn1043 abstract fuzzing is an approach to software testing where the system being tested is bombarded with test cases generated by another program. Results of experiments show that grammarbased whitebox fuzzing explores deeper program paths and avoids deadends due to nonparsable inputs. In contrast, grammarbased fuzzing requires an input grammar specifying the input format of the application under test, which is typically written by hand. In particular, a grammarbased fuzzer will be able to get past the well formedness checks that the target program probably is implementing on its input, and therefore will be able to cover more parts of the program. Whitebox fuzzing is a form of automatic dynamic test generation, based. Results of our experiments show that grammar based whitebox fuzzing explores deeper program paths and avoids deadends due to nonparsable inputs. Grammarbased whitebox fuzzing 2008 by patrice godefroid, adam kiezun, michael y levin venue. As these techniques operate with system inputs, any failure reported is a true failurethere are no false alarms. Fuzzing grammars explicitly differentiate fields that affect paths from those that do not. Automatic generation of syntax valid c programs for. First, we automatically generate fuzzing grammars to improve the code coverage of blackbox fuzz testing. However, white box fuzzing requires considerable time for heavyweight application analysis and constraint solving, so it cannot scale to large, realworld applications 6. Rt2007 page 2 november 2007 acknowledgments most of this talk presents recent results of joint work with michael y. Undue influence 12 200968 symposium on software testing and.
A sentence generator for testing parsers 1972 citeseerx. Levin and david molnar, extending prior joint work with nils klarlundand. The main problem with whitebox fuzzers is the requirement. A fuzzer usually starts with generating test inputs by mutating a set of given inputs 6. Make sure to limit the depth of derivation trees to avoid nontermination of the input generation algorithm and exceedingly large. We present a new automated white box fuzzing technique and a tool, buzzfuzz, that implements this technique. White box fuzzing presented the input as symbols and explored different paths by solving path constraints, so that it greatly improved the coverage. We adapted a grammarbased white box fuzzing method from 7. We would like to improve grammarbased blackbox fuzzing techniques instead of focusing on a white box approach.
We can approximate the benefits of white box fuzzing without needing source code claim. The underlined text in algorithm 1 contains the three necessary changes. However, existing symbolic execution techniques are limited to examining one. Whitebox fuzzing is a form of automatic dynamic test generation, based on symbolic execution and constraint solving, designed for security testing of large applications. First, the new algorithm requires a grammar g that describes valid program inputs. Compared to regular whitebox fuzzing, grammar based whitebox fuzzing increased coverage of the code generation module of the ie7 javascript interpreter from 53% to 81% while using three times fewer tests. Fuzzing has become the most interesting software testing technique. A fuzzing technique relies on its specific fuzz generator, which itself can use various fuzzing strategies. Fuzzing or fuzz testing is an automated software testing technique that involves providing invalid, unexpected, or random data as inputs to a computer program. Grammar based fuzzing tools have been shown effectiveness in finding bugs and generating good fuzzing files. Taintbased directed whitebox fuzzing proceedings of the.
Grammarbased white box fuzzing godefroid2008 combines grammarbased fuzzing with symbolic testing and is now available as a service from microsoft. In this case the tool generates new inputs at least partially informed by the code of the target program itself. It has developed from the original blackbox fuzzing to white box fuzzing and greybox fuzzing, from mutational fuzzing to generational fuzzing, and from nofeedback fuzzing to feedback fuzzing. Results of experiments show that grammar based whitebox fuzzing explores deeper program paths and avoids deadends due to nonparsable inputs. So there, in addition, you have a grammar that represents the input format of the the, the, the, the, the inputs.
Whitebox fuzzing executes the program under test with an. Compared to regular whitebox fuzzing, grammarbased whitebox fuzzing increased coverage of the code generation module of the ie7 javascript interpreter from 53 % to 81% while using three times fewer tests. This paper presents the results of a series of penetration tests performed on the openstack essex cloud management software. A checksumaware fuzzing assistant tool for coverage. Greybox fuzzing is a technique of incorporating information about the target to the fuzzer. Grammar based fuzzing security testing andreas zeller, saarland university. Microsoft uses white box fuzzing as part of their quality assurance process. Thus, we run these test cases on a target program, and then observe the corresponding program behavior to determine whether there is any bug or vulnerability in the target program. Fuzzing or fuzz testing is an automated software testing technique that involves providing. Symbolic execution is highly associated with white box fuzzing and is a way of determining how.
Automated penetration testing with white box fuzzing. Modelbased whitebox fuzzing for program binaries request pdf. And in the case of dart or white box fuzzing the goal was to find bugs in data driven applications. Grammarbased whitebox fuzzing marks tokens returned by tokenization functions as symbolic variables, extracts as constraints the effects such tokens have on program paths, and generates new input. To solve the blindness problem of the original fuzzing, white box fuzzing such as sage 2, bap 3, and klee 4 based on symbolic execution 5 was then proposed. Fuzzing on the operating system kernel level starts late, but also makes great advance. We have implemented grammar based whitebox fuzzing and evaluated it on a large application, the javascript interpreter of the internet explorer 7 webbrowser. A mutationbased fuzzer leverages an existing corpus of seed inputs during fuzzing. In contrast, grammarbased fuzzing requires an input grammar.
Grammarbased whitebox fuzzers gwf can generate files that are valid w. Synthesizing program input grammars osbert bastani. Demott, charles miller, fuzzing for software security testing and quality assurance, 2008, isbn 9781596932142 michael sutton, adam greene, and pedram amini. Interview with patrice godefroid pen testing coursera. Jan 01, 2018 white box fuzzing presented the input as symbols and explored different paths by solving path constraints, so that it greatly improved the coverage. To this end, we propose a grammaraware coveragebased grey box fuzzing approach to fuzz programs that process structured in puts. Pohl, costeffective identification of zeroday vulnerabilities with the aid of threat modeling and fuzzing, 2011. With lightweight instrumentation afl, we get empirically bettermore results than either white or blackbox fuzzers cons.
In this chapter, we take this idea one step further, by providing a specification of the legal inputs to a program. A white box fuzzer 30 25 leverages program analysis fuzzz systematically increase code coverage or to reach certain critical program locations. Sign up gramfuzz is a grammar based fuzzer that lets one define complex grammars to generate text and binary data formats. Fuzzing is a software testing technique which can automatically generate test cases. Blackbox fuzzing miller et al, 1990, peachtec, 2017, fitblip, 2016, helin, 2017 does not consider the internal logic of the program but continuously provides input data and observes the output results. Blackbox fuzzing lies at one extreme in terms of the level of program understanding. Kinds of fuzzing black box the tool knows nothing about the program or its input easy to useand get started, but will explore only shallow statesunless it gets lucky grammar based the tool generates input informed by a grammar more work to use, to produce the grammar, but can go deeperin the state space white box.
To overcome this problem, whitebox fuzzing can be performed using grammarbased specifications for valid input values godefroid et al. The former needs to construct and solve path constraints to detect vulnerabilities. Based on how the structural knowledge of the put is utilized, fuzzers can be classified as white box, blackbox or greybox. Automatic and lightweight grammar generation for fuzz. Since then, fuzzing has been developed as a generalpurpose technique for discovering bugs in software programs. Black box and white box fuzzing are fully automatic, and have historically been proven to be effective in finding security vulnerabilities in binaryformat file parsers. Blackbox fuzzing is a form of testing, heavily used for finding security vulnerabilities in software. The bene t of the blackbox approach is that we are neither bound to a certain language used for implementing the target program nor do we need the source code which is helpful when testing closedsource software. It generates inputs by modifying or rather mutating the provided seeds. In addition, the ymir system discovered two new vulnerabilities revealed only when input values are wellformed.
Fuzzing is the third main approach for hunting software security vulnerabilities. If the input can be modelled by a formal grammar, a smart generation based fuzzer would instantiate the production. Two main classes of methods can detect vulnerabilities in binary files. 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. I the goal of a sqli fuzzer is to modify a part of the structure of a sql statement as a new input without violating its. For simplicity, we call a tool based on fuzzing techniques a fuzzer. Today, fuzzing is widely recognized as a valid computer security test method, and is being used by many commercial software development companies. Whitebox approaches assume that the program code is avail able for analysis. Results of experiments show that grammarbased whitebox fuzzing outperforms whitebox fuzzing, blackbox fuzzing and grammarbased blackbox fuzzing in overall code coverage, while us. Finally, we conclude this paper with an outlook on future work. In the case of fuzzing interpreters, this can be done by providing keywords or whole code snippets which the fuzzer incorporates into the generated test cases. Patrice godefroid of microsoft defines white box fuzzing as a new approach to fuzzing pioneered at. Boosting fuzzing performance with differential seed scheduling. Results of our experiments show that grammarbased whitebox fuzzing explores deeper program paths and avoids deadends due to nonparsable inputs.
Unfortunately, the current effectiveness of whitebox fuzzing is limited when testing applications with highlystructured inputs, such as compilers and interpreters. Grammarbased white box fuzzing, takes into account the input languages grammar to fuzz the input in ways that are syntactically correct. It attempts to evaluate the value of fuzzing seeds and selectively pick the best one. Godefroid of microsoft defines white box fuzzing as a new approach to fuzzing pioneered at microsoft in the sage tool and based on symbolic execution and constraint solving techniques. A fuzzer can be white, grey, or blackbox, depending on whether it is aware of program structure. Grammarbased fuzzing tools have been shown effectiveness in finding bugs and generating good fuzzing files. The state of the art executive summary fuzzing is an approach to software testing where the system being tested is bombarded with test cases generated by another program. Inputs from hell generating uncommon inputs from common. Specifying inputs via a grammar allows for very systematic and efficient test generation, in particular for. But on the other hand, it will often go deeper in the programs state space. Grammar based fuzzers can use fuzzing grammars to derive concrete test cases by replacing fields that do not affect execution paths with long strings. Kernelaware memory checker and symbolic pointer reasoning. Dynamic taint analysis for automatic detection, analysis, and signature generation of exploits on comnodity software. If the programs specification is available, a whitebox fuzzer might leverage techniques from modelbased testing.
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