P-Fuzz: A Parallel Grey-Box Fuzzing Framework
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Abstract:
Fuzzing is an effective technology in software testing and security vulnerability detection. Unfortunately, fuzzing is an extremely compute-intensive job, which may cause thousands of computing hours to find a bug. Current novel works generally improve fuzzing efficiency by developing delicate algorithms. In this paper, we propose another direction of improvement in this field, i.e., leveraging parallel computing to improve fuzzing efficiency. In this way, we develop P-fuzz, a parallel fuzzing framework that can utilize massive, distributed computing resources to fuzz. P-fuzz uses a database to share the fuzzing status such as seeds, the coverage information, etc. All fuzzing nodes get tasks from the database and update their fuzzing status to the database. Also, P-fuzz handles some data races and exceptions in parallel fuzzing. We compare P-fuzz with AFL and a parallel fuzzing framework Roving in our experiment. The result shows that P-fuzz can easily speed up AFL about 2.59× and Roving about 1.66× on average by using 4 nodes.Keywords:
Fuzz testing
Vulnerability
Fuzzing has now developed into an efficient method of vulnerability mining. Symbolic execution is also a popular software vulnerability mining technology. Both are research hotspots in the field of network and information security. Hybrid fuzzing is the addition of symbolic execution technology on the basis of traditional fuzzing, and has now developed into a new branch of fuzzing. This article studies the existing hybrid fuzzing methods, reviews the development and evolution process and technical core of hybrid fuzzing, and compares the performance of currently well-known hybrid fuzzing through an experimental method based on symbolic execution. Finally, it discusses the existing problems in the field of hybrid fuzzing testing, and tries to look forward to its future development trend.
Fuzz testing
Vulnerability
Software bug
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As an improvement on traditional random fuzzing, directed fuzzing utilizes dynamic taint analysis to locate regions of seed inputs which can influence security-sensitive program points, and focuses on mutating these identified regions to generate error-revealing test cases. The seed inputs are of great importance to directed fuzzing, because they essentially determine the number of security-sensitive program points we can test. In this paper, we present a seed selection method complementing with a seed generation method for directed fuzzing. Using static analysis, dynamic monitoring and symbolic execution, our approach can provide directed fuzzing with seeds that can cover more security-sensitive program points in a cost-effective way. We implemented a prototype called Seeded-Fuzz, and applied it to five real-world applications. Experimental results show that starting directed fuzzing with our carefully selected and generated seeds, Seeded-Fuzz can test more critical program sites and detect more bugs.
Fuzz testing
Taint checking
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Білім берy қоғaмның экономикaлық дaмyының негізі, әлеyметтік тұрaқтылықтың фaкторлaрының бірі, хaлықтың рyхaни-aдaмгершілік әлеyетінің және интеллектyaлдық өсyінің қaйнaр көзі ретінде бaрлық yaқыттaрдa тaптырмaс құндылық болып есептеліп келеді. Aл қaзіргідей aдaм кaпитaлын қaлыптaстырy мен дaмытy мәселесін шешy негізгі міндет ретінде қaрaстырылaтын зaмaндa хaлықтың білімдік қaжеттіліктері өсіп, жоғaры, ортa aрнayлы, кәсіби қосымшa білім aлyғa үміткерлер сaны aртa түсyде. Бұғaн жayaп ретінде білім берy ұйымдaрының сaлaлaнyы aртып, әртүрлі типтегі оқy орындaрының сaны aртyдa, білім берyдің инфрaқұрылымы, бaсқaрy формaлaры, әдістемелік, ғылыми қызмет түрлері дaмyдa. Олaрды білім aлyшылaрдың жеке сұрaныстaры мен мүмкіндіктеріне бaғыттay күшейтілyде. Осығaн орaй білімнің сaпaсынa қойылaтын тaлaптaр aртып, бұл сaлaның әлеyметпен өзaрa әрекеттестігіне негізделген құрылымдық – қызметтік дaмyының көкейтестілігі aртyдa. Мaқaлaдa «серіктестік», «әлеyметтік серіктестік», «білімдегі әлеyметтік серіктестік» ұғым- дaрының мәні aшылып, олaрдың қaлыптaсy және дaмy үрдісіне шолy жaсaлaды, жоғaры оқy орындaрындa педaгогтaрды дaярлayдa әлеyметтік серіктестердің әлеyетін пaйдaлaнyдa бaсшылыққa aлынaтын ұстaнымдaр мен тиімді жолдaры сипaттaлaды. Түйін сөздер: серіктестік, әлеyметтік серіктестік, білімдегі әлеyметтік серіктестік, бірлескен әрекет ұстaнымдaры, әлеуметтік серіктестік әлеуеті. Обрaзовaние является основой экономического рaзвития обществa, одним из фaкторов социaль- ной стaбильности, источником дyховно-нрaвственного потенциaлa и интеллектyaльного ростa людей и во все временa считaлось незaменимой ценностью. И в нaстоящее время, когдa решение проблемы формировaния и рaзвития человеческого кaпитaлa рaссмaтривaется кaк основнaя зaдaчa, рaстyт обрaзовaтельные потребности людей, yвеличивaется количество желaющих полyчить высшее, среднее, специaльное, профессионaльное дополнительное обрaзовaние. В ответ нa это yсиливaется рaзветвленность обрaзовaтельных оргaнизaций, yвеличивaется количество обрaзовaтельных оргaни- зaций рaзличного типa, рaзвивaются инфрaстрyктyрa обрaзовaния, формы yпрaвления, методическaя и нayчнaя деятельность. Yсиливaется их ориентaция нa индивидyaльные потребности и возможности обyчaющихся. В связи с этим повышaются требовaния к кaчествy обрaзовaния, возрaстaет знaчение стрyктyрно-фyнкционaльного рaзвития этой сферы нa основе взaимодействия с обществом. В стaтье рaскрывaется знaчение понятий «пaртнерство», «социaльное пaртнерство», «социaльное пaртнерство в обрaзовaнии», рaссмaтривaется процесс их стaновления и рaзвития, описывaются рyко- водящие принципы и эффективные способы использовaния потенциaлa социaльных пaртнеров в подготовке педaгогических кaдров в высших yчебных зaведениях. Ключевые словa: партнерство, социaльное пaртнерство, социaльное пaртнерство в обрaзовaнии, принципы совместного действия, поненциал социального партнерство. Education is the basis of the economic development of society, one of the factors of social stability, a source of spiritual and moral potential and intellectual growth of people and has always been considered an irreplaceable value. And at the present time, when the solution of the problem of the formation and development of human capital is considered as the main task, the educational needs of people are growing, the number of people wishing to receive higher, secondary, special, professional additional education is increasing. In response to this, the branching of educational organizations is increasing, the number of educational organizations of various types is increasing, the infrastructure of education, forms of management, methodological and scientific activities are developing. Their focus on the individual needs and capabilities of students is increasing. In this regard, the requirements for the quality of education are increasing, the importance of the structural and functional development of this sphere on the basis of interaction with society is increasing. The article reveals the meaning of the concepts of "partnership", "social partnership", "social partnership in education", examines the process of their formation and development, describes the guidelines and effective ways to use the potential of social partners in the training of teachers in higher educational institutions. Keywords: partnership, social partnership, social partnership in education, principles of joint action, the potential of social partnership.
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ZigBee defines several security services on the MAC layer, including sequential freshness, frame integrity, data encryption and access control. Unfortunately, there are still security vulnerabilities that could result in network meltdown. Therefore, it is necessary to detect these defects by using a fuzzing test. However, fuzzing tests have usually been inefficient because test cases are either too numerous or invalid. In this paper, a novel comprehensive fuzzing test algorithm, CG-Fuzzing (comprehensive genetic-based-fuzzing) is proposed. The CG-Fuzzing algorithm contains three parts: structure-based, boundary-based and genetic algorithms. This paper establishes an evolutionary model that helps achieve high rates of passing filtering rules and vulnerability triggering. Compared with the traditional fuzzing methods, the number of test cases is reduced and they are more efficient. Experimental results prove that the synthesised performance of CG-Fuzzing is outstanding. The fuzzing test with the algorithm takes only 4 min to exploit a previously known vulnerability of ZigBee.
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Vulnerability
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Fuzzing is an effective technology in software testing and security vulnerability detection. Unfortunately, fuzzing is an extremely compute-intensive job, which may cause thousands of computing hours to find a bug. Current novel works generally improve fuzzing efficiency by developing delicate algorithms. In this paper, we propose another direction of improvement in this field, i.e., leveraging parallel computing to improve fuzzing efficiency. In this way, we develop P-fuzz, a parallel fuzzing framework that can utilize massive, distributed computing resources to fuzz. P-fuzz uses a database to share the fuzzing status such as seeds, the coverage information, etc. All fuzzing nodes get tasks from the database and update their fuzzing status to the database. Also, P-fuzz handles some data races and exceptions in parallel fuzzing. We compare P-fuzz with AFL and a parallel fuzzing framework Roving in our experiment. The result shows that P-fuzz can easily speed up AFL about 2.59× and Roving about 1.66× on average by using 4 nodes.
Fuzz testing
Vulnerability
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В статье приводятся определение фаззинг тестирования, основные этапы фаззинга, рассматривается классификация фаззеров. The article describes the definition of fuzzing testing, the main stages of fuzzing, and discusses the classification of fuzzers.
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While fuzzing can be very costly, it has proven to be a fundamental technique in uncovering bugs (often security related) in many applications. A recent study on bug reports from OSS-Fuzz observed that recent code changes are responsible for 77% of all reported bugs, stressing the importance of continuous testing. With the increased adoption of CI/CD practices in software development, it is only natural to look for effective ways of incorporating fuzzing into continuous security testing. In this paper, we study the effectiveness of fuzz testing in CI/CD pipelines with a focus on security related bugs and seek optimization opportunities to triage commits that do not require fuzzing. Through experimental analysis, we found that the fuzzing effort can be reduced by 63% in three of the nine libraries we analyzed (55% on average). Additionally, we investigate the correlation between fuzzing campaign duration and the effectiveness of fuzzers in vulnerability discovery: a significantly shorter fuzzing campaign facilitates a faster pipeline for developers, while it can still uncover important bugs. Our findings suggest that continuous fuzzing is indeed beneficial for secure software development processes, and that there are many opportunities to improve its effectiveness.
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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The nationally-recognized Susquehanna
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Fuzzing is a software testing technique that finds bugs by repeatedly injecting mutated inputs to a target program. Known to be a highly practical approach, fuzzing is gaining more popularity than ever before. Current research on fuzzing has focused on producing an input that is more likely to trigger a vulnerability.
Fuzz testing
Software testing
Software bug
Popularity
Vulnerability
Taint checking
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