Fuzz Testing
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Automatically Bypassing Android Malware Detection System
FUZZIFICATION: Anti-Fuzzing Techniques Jinho Jung, Hong Hu, David Solodukhin, Daniel Pagan, Kyu Hyung Lee*, Taesoo Kim * 1 Fuzzing Discovers Many Vulnerabilities 2 Fuzzing Discovers Many Vulnerabilities 3 Testers Find Bugs with Fuzzing Detected bugs Normal users Compilation Source Released binary Testers Compilation Distribution Fuzzing 4 But Attackers Also Find Bugs Detected bugs Normal users Compilation Attackers Source Released binary Testers Compilation Distribution Fuzzing 5 Our work: Make the Fuzzing Only Effective to the Testers Detected bugs Normal users Fuzzification ? Fortified binary Attackers Source Compilation Binary Testers Compilation Distribution Fuzzing 6 Threat Model Detected bugs Normal users Fuzzification Fortified binary Attackers Source Compilation Binary Testers Compilation Distribution Fuzzing 7 Threat Model Detected bugs Normal users Fuzzification Fortified binary Attackers Source Compilation Binary Testers Compilation Distribution Fuzzing Adversaries try to find vulnerabilities from fuzzing 8 Threat Model Detected bugs Normal users Fuzzification Fortified binary Attackers Source Compilation Binary Testers Compilation Distribution Fuzzing Adversaries only have a copy of fortified binary 9 Threat Model Detected bugs Normal users Fuzzification Fortified binary Attackers Source Compilation Binary Testers Compilation Distribution Fuzzing Adversaries know Fuzzification and try to nullify 10 Research Goals Detected bugs Normal users Fuzzification Fortified binary Attackers Source Compilation Binary Testers Compilation -
Moonshine: Optimizing OS Fuzzer Seed Selection with Trace Distillation
MoonShine: Optimizing OS Fuzzer Seed Selection with Trace Distillation Shankara Pailoor, Andrew Aday, and Suman Jana Columbia University Abstract bug in system call implementations might allow an un- privileged user-level process to completely compromise OS fuzzers primarily test the system-call interface be- the system. tween the OS kernel and user-level applications for secu- OS fuzzers usually start with a set of synthetic seed rity vulnerabilities. The effectiveness of all existing evo- programs , i.e., a sequence of system calls, and itera- lutionary OS fuzzers depends heavily on the quality and tively mutate their arguments/orderings using evolution- diversity of their seed system call sequences. However, ary guidance to maximize the achieved code coverage. generating good seeds for OS fuzzing is a hard problem It is well-known that the performance of evolutionary as the behavior of each system call depends heavily on fuzzers depend critically on the quality and diversity of the OS kernel state created by the previously executed their seeds [31, 39]. Ideally, the synthetic seed programs system calls. Therefore, popular evolutionary OS fuzzers for OS fuzzers should each contain a small number of often rely on hand-coded rules for generating valid seed system calls that exercise diverse functionality in the OS sequences of system calls that can bootstrap the fuzzing kernel. process. Unfortunately, this approach severely restricts However, the behavior of each system call heavily de- the diversity of the seed system call sequences and there- pends on the shared kernel state created by the previous fore limits the effectiveness of the fuzzers. -
Active Fuzzing for Testing and Securing Cyber-Physical Systems
Active Fuzzing for Testing and Securing Cyber-Physical Systems Yuqi Chen Bohan Xuan Christopher M. Poskitt Singapore Management University Zhejiang University Singapore Management University Singapore China Singapore [email protected] [email protected] [email protected] Jun Sun Fan Zhang∗ Singapore Management University Zhejiang University Singapore China [email protected] [email protected] ABSTRACT KEYWORDS Cyber-physical systems (CPSs) in critical infrastructure face a per- Cyber-physical systems; fuzzing; active learning; benchmark gen- vasive threat from attackers, motivating research into a variety of eration; testing defence mechanisms countermeasures for securing them. Assessing the effectiveness of ACM Reference Format: these countermeasures is challenging, however, as realistic bench- Yuqi Chen, Bohan Xuan, Christopher M. Poskitt, Jun Sun, and Fan Zhang. marks of attacks are difficult to manually construct, blindly testing 2020. Active Fuzzing for Testing and Securing Cyber-Physical Systems. In is ineffective due to the enormous search spaces and resource re- Proceedings of the 29th ACM SIGSOFT International Symposium on Software quirements, and intelligent fuzzing approaches require impractical Testing and Analysis (ISSTA ’20), July 18–22, 2020, Virtual Event, USA. ACM, amounts of data and network access. In this work, we propose active New York, NY, USA, 13 pages. https://doi.org/10.1145/3395363.3397376 fuzzing, an automatic approach for finding test suites of packet- level CPS network attacks, targeting scenarios in which attackers 1 INTRODUCTION can observe sensors and manipulate packets, but have no existing knowledge about the payload encodings. Our approach learns re- Cyber-physical systems (CPSs), characterised by their tight and gression models for predicting sensor values that will result from complex integration of computational and physical processes, are sampled network packets, and uses these predictions to guide a often used in the automation of critical public infrastructure [78]. -
The Art, Science, and Engineering of Fuzzing: a Survey
1 The Art, Science, and Engineering of Fuzzing: A Survey Valentin J.M. Manes,` HyungSeok Han, Choongwoo Han, Sang Kil Cha, Manuel Egele, Edward J. Schwartz, and Maverick Woo Abstract—Among the many software vulnerability discovery techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective. Index Terms—software security, automated software testing, fuzzing. ✦ 1 INTRODUCTION Figure 1 on p. 5) and an increasing number of fuzzing Ever since its introduction in the early 1990s [152], fuzzing studies appear at major security conferences (e.g. [225], has remained one of the most widely-deployed techniques [52], [37], [176], [83], [239]). In addition, the blogosphere is to discover software security vulnerabilities. At a high level, filled with many success stories of fuzzing, some of which fuzzing refers to a process of repeatedly running a program also contain what we consider to be gems that warrant a with generated inputs that may be syntactically or seman- permanent place in the literature. -
Psofuzzer: a Target-Oriented Software Vulnerability Detection Technology Based on Particle Swarm Optimization
applied sciences Article PSOFuzzer: A Target-Oriented Software Vulnerability Detection Technology Based on Particle Swarm Optimization Chen Chen 1,2,*, Han Xu 1 and Baojiang Cui 1 1 School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China; [email protected] (H.X.); [email protected] (B.C.) 2 School of Information and Navigation, Air Force Engineering University, Xi’an 710077, China * Correspondence: [email protected] Abstract: Coverage-oriented and target-oriented fuzzing are widely used in vulnerability detection. Compared with coverage-oriented fuzzing, target-oriented fuzzing concentrates more computing resources on suspected vulnerable points to improve the testing efficiency. However, the sample generation algorithm used in target-oriented vulnerability detection technology has some problems, such as weak guidance, weak sample penetration, and difficult sample generation. This paper proposes a new target-oriented fuzzer, PSOFuzzer, that uses particle swarm optimization to generate samples. PSOFuzzer can quickly learn high-quality features in historical samples and implant them into new samples that can be led to execute the suspected vulnerable point. The experimental results show that PSOFuzzer can generate more samples in the test process to reach the target point and can trigger vulnerabilities with 79% and 423% higher probability than AFLGo and Sidewinder, respectively, on tested software programs. Keywords: fuzzing; model-based fuzzing; vulnerability detection; code coverage; open-source program; directed fuzzing; static instrumentation; source code instrumentation Citation: Chen, C.; Han, X.; Cui, B. PSOFuzzer: A Target-Oriented 1. Introduction Software Vulnerability Detection The appearance of an increasing number of software vulnerabilities has made auto- Technology Based on Particle Swarm matic vulnerability detection technology a widespread concern in industry and academia. -
Configuration Fuzzing Testing Framework for Software Vulnerability Detection
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Columbia University Academic Commons CONFU: Configuration Fuzzing Testing Framework for Software Vulnerability Detection Huning Dai, Christian Murphy, Gail Kaiser Department of Computer Science Columbia University New York, NY 10027 USA ABSTRACT Many software security vulnerabilities only reveal themselves under certain conditions, i.e., particular configurations and inputs together with a certain runtime environment. One approach to detecting these vulnerabilities is fuzz testing. However, typical fuzz testing makes no guarantees regarding the syntactic and semantic validity of the input, or of how much of the input space will be explored. To address these problems, we present a new testing methodology called Configuration Fuzzing. Configuration Fuzzing is a technique whereby the configuration of the running application is mutated at certain execution points, in order to check for vulnerabilities that only arise in certain conditions. As the application runs in the deployment environment, this testing technique continuously fuzzes the configuration and checks "security invariants'' that, if violated, indicate a vulnerability. We discuss the approach and introduce a prototype framework called ConFu (CONfiguration FUzzing testing framework) for implementation. We also present the results of case studies that demonstrate the approach's feasibility and evaluate its performance. Keywords: Vulnerability; Configuration Fuzzing; Fuzz testing; In Vivo testing; Security invariants INTRODUCTION As the Internet has grown in popularity, security testing is undoubtedly becoming a crucial part of the development process for commercial software, especially for server applications. However, it is impossible in terms of time and cost to test all configurations or to simulate all system environments before releasing the software into the field, not to mention the fact that software distributors may later add more configuration options. -
Fuzzing with Code Fragments
Fuzzing with Code Fragments Christian Holler Kim Herzig Andreas Zeller Mozilla Corporation∗ Saarland University Saarland University [email protected] [email protected] [email protected] Abstract JavaScript interpreter must follow the syntactic rules of JavaScript. Otherwise, the JavaScript interpreter will re- Fuzz testing is an automated technique providing random ject the input as invalid, and effectively restrict the test- data as input to a software system in the hope to expose ing to its lexical and syntactic analysis, never reaching a vulnerability. In order to be effective, the fuzzed input areas like code transformation, in-time compilation, or must be common enough to pass elementary consistency actual execution. To address this issue, fuzzing frame- checks; a JavaScript interpreter, for instance, would only works include strategies to model the structure of the de- accept a semantically valid program. On the other hand, sired input data; for fuzz testing a JavaScript interpreter, the fuzzed input must be uncommon enough to trigger this would require a built-in JavaScript grammar. exceptional behavior, such as a crash of the interpreter. Surprisingly, the number of fuzzing frameworks that The LangFuzz approach resolves this conflict by using generate test inputs on grammar basis is very limited [7, a grammar to randomly generate valid programs; the 17, 22]. For JavaScript, jsfunfuzz [17] is amongst the code fragments, however, partially stem from programs most popular fuzzing tools, having discovered more that known to have caused invalid behavior before. LangFuzz 1,000 defects in the Mozilla JavaScript engine. jsfunfuzz is an effective tool for security testing: Applied on the is effective because it is hardcoded to target a specific Mozilla JavaScript interpreter, it discovered a total of interpreter making use of specific knowledge about past 105 new severe vulnerabilities within three months of and common vulnerabilities. -
Seed Selection for Successful Fuzzing
Seed Selection for Successful Fuzzing Adrian Herrera Hendra Gunadi Shane Magrath ANU & DST ANU DST Australia Australia Australia Michael Norrish Mathias Payer Antony L. Hosking CSIRO’s Data61 & ANU EPFL ANU & CSIRO’s Data61 Australia Switzerland Australia ABSTRACT ACM Reference Format: Mutation-based greybox fuzzing—unquestionably the most widely- Adrian Herrera, Hendra Gunadi, Shane Magrath, Michael Norrish, Mathias Payer, and Antony L. Hosking. 2021. Seed Selection for Successful Fuzzing. used fuzzing technique—relies on a set of non-crashing seed inputs In Proceedings of the 30th ACM SIGSOFT International Symposium on Software (a corpus) to bootstrap the bug-finding process. When evaluating a Testing and Analysis (ISSTA ’21), July 11–17, 2021, Virtual, Denmark. ACM, fuzzer, common approaches for constructing this corpus include: New York, NY, USA, 14 pages. https://doi.org/10.1145/3460319.3464795 (i) using an empty file; (ii) using a single seed representative of the target’s input format; or (iii) collecting a large number of seeds (e.g., 1 INTRODUCTION by crawling the Internet). Little thought is given to how this seed Fuzzing is a dynamic analysis technique for finding bugs and vul- choice affects the fuzzing process, and there is no consensus on nerabilities in software, triggering crashes in a target program by which approach is best (or even if a best approach exists). subjecting it to a large number of (possibly malformed) inputs. To address this gap in knowledge, we systematically investigate Mutation-based fuzzing typically uses an initial set of valid seed and evaluate how seed selection affects a fuzzer’s ability to find bugs inputs from which to generate new seeds by random mutation. -
Software Testing: Essential Phase of SDLC and a Comparative Study Of
International Journal of System and Software Engineering Volume 5 Issue 2, December 2017 ISSN.: 2321-6107 Software Testing: Essential Phase of SDLC and a Comparative Study of Software Testing Techniques Sushma Malik Assistant Professor, Institute of Innovation in Technology and Management, Janak Puri, New Delhi, India. Email: [email protected] Abstract: Software Development Life-Cycle (SDLC) follows In the software development process, the problem (Software) the different activities that are used in the development of a can be dividing in the following activities [3]: software product. SDLC is also called the software process ∑ Understanding the problem and it is the lifeline of any Software Development Model. ∑ Decide a plan for the solution Software Processes decide the survival of a particular software development model in the market as well as in ∑ Coding for the designed solution software organization and Software testing is a process of ∑ Testing the definite program finding software bugs while executing a program so that we get the zero defect software. The main objective of software These activities may be very complex for large systems. So, testing is to evaluating the competence and usability of a each of the activity has to be broken into smaller sub-activities software. Software testing is an important part of the SDLC or steps. These steps are then handled effectively to produce a because through software testing getting the quality of the software project or system. The basic steps involved in software software. Lots of advancements have been done through project development are: various verification techniques, but still we need software to 1) Requirement Analysis and Specification: The goal of be fully tested before handed to the customer. -
Manipulating I/Os and Repurposing Binary Code to Enable Instrumented Fuzzing in ICS Control Applications
ICSFuzz: Manipulating I/Os and Repurposing Binary Code to Enable Instrumented Fuzzing in ICS Control Applications Dimitrios Tychalas1, Hadjer Benkraouda2 and Michail Maniatakos2 1NYU Tandon School of Engineering, Brooklyn, NY, USA 2New York University Abu Dhabi, Abu Dhabi, UAE Abstract critical infrastructures such as power grids, oil and gas indus- tries, transportation and water treatment facilities. Possible Industrial Control Systems (ICS) have seen a rapid prolif- suspension of their operation may cause severe complications eration in the last decade amplified by the advent of the 4th which translate to significant loss of revenue, environmental Industrial Revolution. At the same time, several notable cyber- disasters or even human casualties [22, 47]. security incidents in industrial environments have underlined Since safety and security are tightly correlated in ICS en- the lack of depth in security evaluation of industrial devices vironments, the recent cyberattacks that targeted industrial such as Programmable Logic Controllers (PLC). Modern settings have showcased tangible threats to contemporary in- PLCs are based on widely used microprocessors and deploy dustrial environments. The most prominent of these incidents, commodity operating systems (e.g., ARM on Linux). Thus, Stuxnet [30], drew widespread attention when discovered, threats from the information technology domain can be read- as the first potential case of cyberwarfare. In the following ily ported to industrial environments. PLC application bi- years more incidents including the 2015/2016 cyberattacks naries in particular have never been considered as regular on the Ukrainian power grid [33, 62] and the 2017 attack programs able to introduce traditional security threats, such on petrochemical facilities in Saudi Arabia [42] have helped as buffer overflows. -
Fuzzy Attacks on Web Logs
Institut für Technische Informatik und Kommunikationsnetze Laura Peer Fuzzy Attacks on Web Logs Group Thesis (GA)-2011-09 August 2011 to September 2011 Tutor: David Gugelmann Co-Tutor: Dr. Stephan Neuhaus Supervisor: Prof. Dr. Bernhard Plattner 2 Abstract Oftentimes, weaknesses in web applications arise because input is not properly validated. Web programmers can make mistakes or may simply not conceive of every possible input scenario provided to their application. In this thesis we use the method of fuzzing, the automated process of feeding pseudo random input data to a program, to test web applications for bugs. Our target applications are web logs. With the help of a small program which was written for our specific task, we have discovered a number of problems and vulnerabilities. 3 4 Contents 1 Introduction 7 1.1 Motivation........................................7 1.2 The Task........................................7 1.3 Related Work......................................8 1.4 Overview........................................9 2 Background 11 2.1 Fuzzing......................................... 11 2.1.1 The Fuzzing Process............................. 11 2.1.2 Random Data Generation........................... 11 2.1.3 Vulnerabilities.................................. 12 2.2 Fuzzing the HTTP Protocol.............................. 16 2.2.1 Client to Server Transmission Analysis.................... 16 2.2.2 Server Response and Detection Methods.................. 18 2.3 Web Application Input................................. 19 3 Design 21 3.1 Analyzing the Web Log with a Web Crawler..................... 23 3.2 The HTTP Communication Interface......................... 23 3.3 Fuzzed Data Generation................................ 24 3.4 The Main Program Loop................................ 24 3.4.1 Working with the Web Sniffer Output..................... 24 3.4.2 The FuzzField Class.............................. 25 3.5 Error Detection Mechanisms............................ -
Stress Testing Embedded Software Applications
Embedded Systems Conference / Boston, MA September 20, 2007 ESC-302 Stress Testing Embedded Software Applications T. Adrian Hill Johns Hopkins University Applied Physics Laboratory [email protected] ABSTRACT This paper describes techniques to design stress tests, classifies the types of problems found during these types of tests, and analyzes why these problems are not discovered with traditional unit testing or acceptance testing. The findings are supported by citing examples from three recent embedded software development programs performed by the Johns Hopkins University Applied Physics Laboratory where formal stress testing was employed. It also defines what is meant by the robustness and elasticity of a software system. These findings will encourage software professionals to incorporate stress testing into their formal software development process. OUTLINE 1. INTRODUCTON 1.1 What can be learned by “breaking” the software 2. DESIGNING A STRESS TEST 2.1 What is a reasonable target CPU load? 2.2 Other ways to stress the system 2.3 Characteristics of a Stress Test 3. REAL WORLD RESULTS 3.1 Case #1: Software Missed Receiving Some Commands When CPU Was Heavily Loaded 3.2 Case #2: Processor Reset when Available Memory Buffers Were Exhausted 3.3 Case #3: Unexpected Command Rejection When CPU Was Heavily Loaded 3.4 Case #4: Processor Reset When RAM Disk Was Nearly Full 3.5 Synopsis of All Problems Found During Stress Testing 4. SUMMARY 1. INTRODUCTON Traditional Software Acceptance Testing is a standard phase in nearly every software development methodology. Test engineers develop and execute tests that are defined to validate software requirements.