In well-tested programs, PathAFL found 8 new security bugs with 6 CVEs assigned. Moreover, the error rate of generated test cases is still high. It calculates each paths probability based on the sampling information. Abstract: The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. These algorithms are challenged by a popular class of APIs: higher-order functions that receive callback arguments, which often are invoked asynchronously. However, exploitable states do not always exist in crashing paths. A recent advancement in binary fuzzing performance is Coverage-guided Tracing (CGT), which brings orders-of-magnitude gains in throughput by restricting the expense of coverage tracing to only when new coverage is guaranteed. DirectFuzz uses Directed Graybox Fuzzing to generate test inputs targeted towards a module instance, which enables targeted testing. The benchmark includes a suite of representative open-source network servers for popular protocols, and tools to automate experimentation. BanditFuzz constructs inputs that expose performance issues in a set of target solvers relative to a set of reference solvers, and is the first performance fuzzer that supports the entirety of the theories in the SMT-LIB initiative. Experiment results showed it could generate exploit for most of the userspace test set, and it could also facilitate security mitigation bypassing and exploitability evaluation for kernel test set. Nevertheless, the randomness and blindness of fuzzing hinder it from becoming a reliable security tool. For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Googles fuzzing test suite, which consists of widely used real-world applications. In this paper, we propose Razzer, a tool to find race bugs in kernels. Second, it addresses the performance bottleneck caused by system-mode emulation with a novel technique called augmented process emulation. To that end, we extend TLS-Attacker, an open source framework for analyzing TLS implementations, with support for DTLS tailored to the stateless and unreliable nature of the underlying UDP layer. Existing work has, therefore, focused on guiding the exploration toward program parts that are more likely to contain bugs by using an offline static analysis. Prior work on fuzzing DL libraries can only generate tests for APIs which have been invoked by documentation examples, developer tests, or DL models, leaving a large number of APIs untested. Although there has been much research on finding kernel vulnerabilities from source code, there are relatively few research on kernel fuzzing, which is a practical bug finding technique that does not require any source code. We evaluate StochFuzz on two sets of real-world programs and compare with five other baselines. Our work demonstrates that the performance bugs are a common, severe, yet previously overlooked security problem. This work formulates the DNN backdoor testing and proposes the CatchBackdoor framework. In this paper, we present SAFL, an efficient fuzzing testing tool augmented with qualified seed generation and efficient coverage-directed mutation. You signed in with another tab or window. ROSrosoutloglog ROSloglogdebugloginfologwarnlogerrlogfatal To further demonstrate the capabilities of our annotations, we use AFL combined with IJON to uncover both novel security issues and issues that previously required a custom and comprehensive grammar to be uncovered. Because bug-based benchmarking is expensive and subject to several threats to validity, it might seem reasonable to compare fuzzers in terms of the coverage achieved, and from that derive empirical claims about a fuzzers superiority at finding bugs. All these results confirm the practicality of our system as a kernel fuzzer. We implemented a prototype tool called AFLTeam. We demonstrate the efficacy of PULSAR in two case studies, where it identifies known as well as unknown vulnerabilities. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Then, a fuzzing policy, represented with a suitable architecture of neural networks, is trained on the generated dataset. Our basic hypothesis is that for a DL library under test, there may exist a number of APIs sharing similar input parameters and outputs; in this way, we can easily borrow test inputs from invoked APIs to test other relational APIs. Abstract: Fuzzing is an effective software testing technique to find bugs. Sydr combines DynamoRIO dynamic binary instrumentation tool with Triton symbolic engine. Our insight is that the system call relevancy could be an essential performance indicator for realizing kernel fuzzing. We systematically compare Z3str3, CVC4, Z3str2, and Norn on groups of such instances, and identify those that are particularly challenging for some solvers. It resolves the issues of blind mutations without knowing the effectiveness by Test Coverage Level feedback. We present four case studies covering four real-world programming languages (C, Lua, SQL, and SMT-LIB 2) to show that *Smith is both efficient and effective, while being flexible enough to support programming languages that differ considerably. We implement a black box fuzz testing method for REST APIs. During the experiments, we discovered several previously unknown excessive memory consumption vulnerabilities and received 15 new CVEs. Notably, OpFuzz found 16 critical soundness bugs in CVC4, which has proved to be a very stable SMT solver. Our result is promising: we found an average of 38.6% more bugs than three previous fuzzers over 8 applications using the same amount of fuzzing time. Our proposed Structure-aware CAN Fuzzing system takes less time to run than existing solutions, meaning that problematic CAN messages that may have originated from SW implementation errors or CAN DBC (database CAN) design errors can be found quickly and, subsequently, appropriate action can be taken. This has forced protocol designers to make choices that affect the complexity of DTLS, and to incorporate features that need not be addressed in the numerous TLS analyses. Our experimental results show that Ankou is $1.94\times$ and $8.0\times$ more effective in finding bugs than AFL and Angora, respectively. Abstract: Parallel coverage-guided greybox fuzzing is the most common setup for vulnerability discovery at scale. Abstract: Device drivers remain a main source of runtime failures in operating systems. RestCT is systematic in that it covers and tests not only the interactions of a certain number of operations in RESTful APIs, but also the interactions of particular input-parameters in every single operation. One such tool is fuzzing, a testing process which subjects a system or program to a stream of input data, where the goal of the test is to check for exploitable edge cases. ESPIKE will facilitate testing implementations of Nesting API calls enables a test to use values produced by an API that are available only once a callback has been invoked, and is often necessary to ensure that methods are invoked in a specific order. For this purpose, we utilize static analysis to evaluate the complexity of each basic block and employ the hardware trace mechanism to dynamically extract the execution path for calculating the seed inputs weights. In this paper, we proposed a novel context-aware adaptive mutation scheme, namely CMFuzz, which utilizes a contextual bandit algorithm LinUCB to effectively choose optimal mutation operators for various seed files. In this paper, we study how to intelligently generate data payloads embedded in REST API requests in order to find data-processing bugs in cloud services. We also evaluated Snipuzz on 20 popular real-world IoT devices. This paper introduces AutoInter-fuzzing, a fuzzer controlling thread interleavings elaborately and providing an interleaving-aware power schedule to detect vulnerabilities in a multi-threaded program. We analyze the root cause for performance degradation in naive combination, and discover optimizations conflict in coverage criteria and optimization granularity. Then, we optimize the power schedule on AST nodes based on a probabilistic model. As an auxiliary post-processing step, T-Fuzz leverages a symbolic execution-based approach to filter out false positives and reproduce true bugs in the original program. In this paper, we present a test generation technique specifically directed at input parsers. Just as obfuscation techniques increase the amount of human labor needed to find a vulnerability, our techniques render automated fuzzing-based approaches futile. Abstract: Fuzzing is an effective method to find software bugs and vulnerabilities. We developed a framework called kernel-AFL (kAFL) to assess the security of Linux, macOS, and Windows kernel components. We select 9 open-sourced programs with different highly-structured inputs to demonstrate the effectiveness of RapidFuzz. The results show that almost half of the fuzzer-found bugs propagate to the generated binaries for at least one package, in which case only a very small part of the binary is typically affected, yet causing two failures when running the test suites of all the impacted packages. In practice, multithreading-relevant bugs are usually buried in the sophisticated program flows. Abstract: Directed grey-box fuzzing (DGF) is a security testing technique that aims to steer the fuzzer towards predefined target sites in the program. With these optimizations, Intriguer can efficiently perform symbolic emulation for more relevant instructions and invoke a solver for complicated constraints only. Abstract: We describe a largely automated and systematic analysis of TLS implementations by what we call protocol state fuzzing: we use state machine learning to infer state machines from protocol implementations, using only blackbox testing, and then inspect the inferred state machines to look for spurious behaviour which might be an indication of flaws in the program logic. Consequently, we encourage seed selection to be foremost in mind when evaluating/deploying fuzzers, and recommend that (a) seed choice be carefully considered and explicitly documented, and (b) never to evaluate fuzzers with only a single seed. Such proposals are assessed empirically: Can they be shown to perform better than the status quo? As the optimizations reside in different stages such as input seed selection and mutation, it is tempting to combine the optimizations in different stages. We have implemented our algorithm on top of CUTE and applied it to obtain better branch coverage for an editor implementation (VIM 5.7, 150K lines of code) as well as a data structure implementation in C. Our experiments suggest that hybrid concolic testing can handle large programs and provide, for the same testing budget, almost 4 the branch coverage than random testing and almost 2 that of concolic testing. In total, HYPER-CUBE found 54 novel bugs, and so far we obtained 37 CVEs. FreeDom also exhibits up to 3.74x higher throughput through browser self-termination. It is this limitation which prohibits nearly all of todays state-of-the-art fuzzers from attaining the performance benefits of CGT. Abstract: With the rise of smartphones and their importance in everyday life, they have also become an increasingly larger target of cyberattacks. In this paper, we generalize this idea by collecting and applying empirical data from single, isolated fuzzer runs to automatically identify a set of fuzzers that complement each other when executed collaboratively. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Abstract: The statefulness property of network protocol implementations poses a unique challenge for testing and verification techniques, including Fuzzing. As a result, RATEL outperformed other fuzzers in terms of coverage and bugs. Moreover, PANGOLIN found 400 more bugs in LAVA-M and discovered 41 unseen bugs with 8 of them assigned with the CVE IDs. Then this paper systematically study the machine learning based fuzzing models from selection of machine learning algorithm, pre-processing methods, datasets, evaluation metrics, and hyperparameters setting. The markers are 4.5 cm (although when printed and measured, came out to 4.4 cm for me). Fuzzing is an efficient technique to test programs and find bugs. In particular, the deep neural network is used to learn the hidden vulnerability pattern from a large number of vulnerable and clean program paths to train a prediction model to classify whether paths are vulnerable. To build meaningful operation call sequences, researchers have proposed techniques to learn and utilize the API dependencies based on OpenAPI specifications. To demonstrate the effectiveness of the extracted constraints, DocTer uses the constraints to enable the automatic generation of valid and invalid inputs to test DL API functions. Moreover, we are able to uncover substantially more bugs: in total, we uncovered 44 new bugs with 22 CVEs requested. To protect sensitive data exchange in these scenarios, the DTLS protocol has been developed as a cryptographic variation of TLS. Using BanditFuzz, we discovered 1700 syntactically unique inputs resulting in inconsistent answers across state-of-the-art SMT solvers Z3, CVC4, Colibri, MathSAT, and Z3str3 over the floating-point and string SMT theories. Therefore, evaluating the distance between the execution trace of a seed and the target sites (aka, the seed distance) is important for DGF. Abstract: Applying modern fuzzers to novel targets is often a very lucrative venture. We propose three Fuzzification techniques: 1) SpeedBump, which amplifies the slowdown in normal executions by hundreds of times to the fuzzed execution, 2) BranchTrap, interfering with feedback logic by hiding paths and polluting coverage maps, and 3) AntiHybrid, hindering taint-analysis and symbolic execution. A quantitative notion of security allows for the relaxation of noninterference and tolerates small (unavoidable) leaks. Solved creating a package from scratch using: Please start posting anonymously - your entry will be published after you log in or create a new account. Fuzzing takes on one of two forms: compiler-based or binary-only, depending on the availability of source code. Unfortunately, grammar-based fuzzing is often unable to discover subtle bugs associated with the parsing and handling of the language syntax. To conduct directed fuzzing, WindRanger uses the deviation basic blocks and their related data flow information for seed distance calculation, mutation, seed prioritization as well as explore-exploit scheduling. We had identified 158 unique JS engine bugs, of which 129 have been verified, and 115 have already been fixed by the developers. The discovery of vulnerabilities in protocol implementations, however, is a challenging task: While for standard protocols this process can be conducted with regular techniques for auditing, the situation becomes difficult for proprietary protocols if neither the program code nor the specification of the protocol are easily accessible. To gain directness, DGF prioritizes the seeds whose execution traces are closer to the target sites. We further execute Havoc with three parallel threads and result in 9% higher average edge coverage over QSYM upon all the benchmark projects. AFLs binary-only fuzzing mode, QEMU-AFL, is typically 2-5X slower than its source-available fuzzing mode. PATA does so using the following steps. However, data-flow coverage does show promise in targets where control flow is decoupled from semantics (e.g., parsers). To further investigate neural program-smoothing-based fuzzing, we first construct a large-scale benchmark with a total of 28 influential open-source projects. The byte-wise relationship determination mitigates the problem of loading extra bytes when fuzzers infer the byte-constraint relation. Abstract: Unlike traditional software, smart contracts have the unique organization in which a sequence of transactions shares persistent states. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. This model treats fuzzing as a random sampling process. Alongside the wrapper itself and the Rviz display, a few examples are provided to interface the ZED with other ROS packages : RTAB-Map: See zed_rtabmap_example; ROS Nodelet, depthimage_to_laserscan: See zed_nodelet_example; AR Track Alvar: See zed_ar_track_alvar_example; Tutorials Additionally, in most cases, we were able to do so using multiple orders of magnitude less time than comparable fuzzers. First, IntelliGen determines a set of entry functions and evaluates their respective chance of exhibiting a vulnerability. Alongside the wrapper itself and the Rviz display, a few examples are provided to interface the ZED with other ROS packages : RTAB-Map: See zed_rtabmap_example; ROS Nodelet, depthimage_to_laserscan: See zed_nodelet_example; AR Track Alvar: See zed_ar_track_alvar_example; Tutorials Abstract: A growing awareness is brought that the safety and security of industrial control systems cannot be dealt with in isolation, and the safety and security of industrial control protocols (ICPs) should be considered jointly. Despite numerous attempts over the last decades to automate the stimuli generation based on coverage feedback, Coverage Directed Test Generation (CDG) has not found the widespread adoption that one would expect. Abstract: Taint analysis assists fuzzers in solving complex fuzzing constraints by inferring the influencing input bytes. In addition, the primitives improve AFLs throughput up to 7.7x with 30 cores, which is a more common setting in data centers. First, PATA identifies variables used in constraints and constructs the Representative Variable Sequence (RVS), consisting of occurrences of all representative constraint variables and their values. It allowed us to find unusual padding oracle vulnerabilities and overflows/overreads in widely used TLS libraries, including OpenSSL, Botan, and MatrixSSL. Abstract: Robotic vehicles (RVs) are being adopted in a variety of application domains. We therefore propose an alternative representation of code coverage, which retains the same path sensitivity as the original AFL algorithm, but can directly work on the raw ETM data without matching them with disassembled instructions. Whitebox guidance (e.g., taint tracking or concolic execution) is sometimes integrated with coverage-guided fuzzing to help cover tricky-to-reach branches that are guarded by complex conditions (so-called magic values). As such, fuzzers often get stuck in a local optimum during their search. We find that on these programs FairFuzz achieves high branch coverage at a faster rate that state-of-the-art versions of AFL. FuzzingDriver does not add any overhead to the fuzzing job as it is run beforehand. We also explore combining different coverage metrics through cross-seeding, and the result is very encouraging: this pure fuzzing based approach can crash at least the same numbers of binaries in the CGC dataset as a previous approach (Driller) that combines fuzzing and concolic execution. The slowdown is largely caused by the heavy dynamic instrumentation. Instead, we propose to fuzz all commits simultaneously, but code present in more (recent) commits with higher priority. However, these techniques either lack the overall awareness of how all the APIs are connected or the flexibility of adaptively fixing the learned knowledge. Popular fuzzers (e.g., AFL) often use coarse coverage information, e.g., edge hit counts stored in a compact bitmap, to achieve highly efficient greybox testing. During the fuzzing phase, the testers goal is to find flaws in software by sending unexpected input to the target application and monitoring its behavior. astra_camera . It also gains final increase with 8.35%-36.84% more paths within 24 hours, and has exposed 9 previously unknown vulnerabilities. Abstract: Directed greybox fuzzing is an augmented fuzzing technique intended for the targeted usages such as crash reproduction and proof-of-concept generation, which gives directedness to fuzzing by driving the seeds toward the designated program locations called target sites. By transforming the program as well as mutating the input, T-Fuzz covers more code and finds more true bugs than any existing technique. This paper presents a summary of the recent advances, analyzes how they improve the fuzzing process, and sheds light on future work in fuzzing. We argue that the reason for this are twofold. RestCT is also automatic in that its application relies on only a given Swagger specification of RESTful APIs. The key insight behind NEZHAs design is that current tools generate inputs by simply borrowing techniques designed for finding crash or memory corruption bugs in individual programs (e.g., maximizing code coverage). The generated testcases make structural features easier to be identified, which makes the whole process faster. FreeDom guided by coverage is more effective in revealing new code blocks (2.62%) and finds three complex bugs that its generative approach fails to find. 33 of these bugs are security vulnerabilities. While manual or automated large scale analysis of those systems regularly uncover new vulnerabilities, the way those systems are analyzed follows often the same approaches used on desktop systems. Performing security checks of industrial communication processes help detect unknown risks and vulnerabilities. Our empirical results show that they are critical to the evaluation of a fuzzer. In this paper, we present an automatic program generation technique to test such programming language implementations. Abstract: We investigate the use of coverage-guided fuzzing as a means of proving satisfiability of SMT formulas over finite variable domains, with specific application to floating-point constraints.We showhow an SMT formula can be encoded as a program containing a location that is reachable if and only if the programs input corresponds to a satisfying assignment to the formula. Therefore, ensuring the robustness and correctness of SMT solvers is critical. In this paper, we evaluate 3 such general-purpose fuzzing tools namely libFuzzer, American Fuzzy Lop(AFL) and honggfuzz on 2 metrics, i.e. Abstract: Modbus/TCP is a network protocol for industrial communications encapsulated in TCP/IP network packets. Error trying to catkin_package an existing ros package that uses eigen, pcl error by trying to get the ti_mmwave_rospkg driver, Invalid roslaunch XML syntax: not well-formed, Launch python extension build with catkin_make. The correctness of the source code of these applications is crucial to prevent attackers from exploiting its vulnerabilities, leading to severe consequences like the disclosure of sensitive information or the degradation of the availability of the application. We evaluated Token-Level Fuzzing by modifying AFL and fuzzing four popular JavaScript engines, finding 29 previously unknown bugs, several of which could not be found with state-of-the-art byte-level and grammar-based fuzzers. However, a software program oftentimes has only a fairly small portion that contains vulnerabilities, leading coverage-based fuzzers to work poorly most of the time. DifFuzz automatically detects these vulnerabilities by analyzing two versions of the program and using resource-guided heuristics to find inputs that maximize the difference in resource consumption between secret-dependent paths. The experimental results show that ESRFuzzer outperforms state-of-the-art solutions in terms of types and number of vulnerabilities found. Existing test generators cannot effectively test such APIs because they only sequence API calls, but do not nest one call into the callback function of another. Different from coverage-based fuzzing whose goal is to increase code coverage for triggering more bugs, DGF is designed to test only the potential buggy code whose location is known (e.g., testing the high-risk code such as string operations). For instance, modern coverage-guided kernel fuzzers, such as Syzkaller, estimate code coverage by comparing the entire set of executed basic blocks (or edges) regardless of the system call relevancy. To automatically create large sets of test programs, random program generators, or fuzzers, have emerged. We also enhance the mutation strategies by reducing the testing complexity for REST APIs, generating more appropriate test cases to cover possible paths. We have further conducted an in-depth study on the bugs found by OpFuzz. We evaluated Squirrel on four popular DBMSs: SQLite, MySQL, PostgreSQL and MariaDB. However, as statistics shows, a large portion of the explored code is often invulnerable. We run our fuzzer on seven mature solvers and find 29 previously unknown critical bugs. Fuzz testing (fuzzing) for the ICP is a common way to discover whether the ICP itself is designed and implemented with flaws and network security vulnerability. In this work, we propose active fuzzing, an automatic approach for finding test suites of packet-level CPS network attacks, targeting scenarios in which attackers can observe sensors and manipulate packets, but have no existing knowledge about the payload encodings. In this paper we describe Badger - a new hybrid approach for complexity analysis, with the goal of discovering vulnerabilities which occur when the worst-case time or space complexity of an application is significantly higher than the average case. Our framework employs (i) a vulnerability-aware selection of coverage metrics for enhancing the effectiveness of code exploration, (ii) crash deduplication information for early feedback, and (iii) a configurable input culling strategy that interleaves multiple strategies to achieve comprehensiveness. It employs a specialized memory allocator and code instrumentation that is simple enough to be implemented in any compiler, binary translation system, or even in hardware. On average, our coverage-preserving CGT attains near-identical speed to the present block-coverageonly CGT, UnTracer; and outperforms leading binary- and sourcelevel coverage tracers QEMU, Dyninst, RetroWrite, and AFL-Clang by 224, finding more bugs in less time. The experimental results show that Truzz can generate up to 33% more inputs flowing into functional code, in addition to roughly 83% more edge coverage identified than the vanilla fuzzers. In this work, we propose a novel framework based on rev.ng and libFuzzer, 40 of these bugs have been confirmed by related developers. This paper presents HyDiff, the first hybrid approach for differential software analysis. Inefficient implementations of these steps significantly reduce the fuzzers overall throughput. In this paper, we propose IntelliGen, a framework that constructs valid fuzz drivers automatically. Finally, MaxAFL can also find more bugs than the other fuzzers. The resulting approach can leverage more sophisticated information about the execution of the target program, enhancing the effectiveness of the evolutionary fuzzing. In this work, we propose to learn an effective and fast fuzzer from symbolic execution, by phrasing the learning task in the framework of imitation learning. Since most implementations and firmwares are closed-source, fuzzing remains one of the main methods to uncover Remote Code Execution (RCE) vulnerabilities in deployed systems. This paper examines the properties required to achieve compiler-quality binary-only fuzzing instrumentation. It then tests the harness, instead of the original complicated program, using an efficient implementation of fork on Windows. As the emulated USB device works at the device level, porting Second, they believe all bytes are equal and mutate them sequentially, wasting lots of time testing some uninteresting bytes. We demonstrate the efficacy of smart fuzzing by implementing it for two real-world CPS testbedsa water purification plant and a water distribution systemfinding attacks that drive them into 27 different unsafe states involving water flow, pressure, and tank levels, including six that were not covered by an established attack benchmark. We evaluate EMS against state-of-the-art fuzzers including AFL, QSYM, MOPT, MOPT-dict, EcoFuzz, and AFL++ on 9 real world programs. Respect to that answer, just the control_loop.cpp has the main entry, the hw_interface.cpp is called by an include file. Further, we find 10 new bugs in the semantic analysis stages of these benchmarks. However, most inputs fail to pass the semantic checking (e.g., violating semantic rules), which restricts their capability of discovering deep bugs. this work, we first conduct a comprehensive study of known The selection of effective test vector sequences is critical and a natural domain for the employment of artificial intelligence and machine learning techniques that incorporate successful selection criteria. Abstract: Fuzz testing operating system kernels remains a daunting task to date. We evaluate our framework on a real-world vulnerability and we show we manage to reproduce the bug successfully. Unfortunately, the state-of-the-art concolic testing tools do not scale well for testing COTS OSes because of state explosion. Using 15 real-world kernel UAF vulnerabilities on Linux systems, we then demonstrate FUZE could not only escalate kernel UAF exploitability and but also diversify working exploits. Gramatron uses grammar automatons in conjunction with aggressive mutation operators to synthesize complex bug triggers faster. Furthermore, our experiments also corroborate that Singularity can discover previously unknown performance bugs and availability vulnerabilities in real-world applications such as Google Guava and JGraphT. To scientifically understand the effectiveness and limitations of the two approaches, we propose FreeDom, a full-fledged cluster-friendly DOM fuzzer that works with both generative and coverage-guided modes. Our approach learns regression models for predicting sensor values that will result from sampled network packets, and uses these predictions to guide a search for payload manipulations (i.e. Abstract: JavaScript (JS) engine vulnerabilities pose significant security threats affecting billions of web browsers. We discuss how to leverage REST API specifications, which, by definition, contain data schemas for API request bodies. Our evaluation on popular fuzzers and real-world applications shows that Fuzzification effectively reduces the number of discovered paths by 70.3% and decreases the number of identified crashes by 93.0% from real-world binaries, and decreases the number of detected bugs by 67.5% from LAVA-M dataset while under user-specified overheads for common workloads. We found many unknown bugs in five widely used real-world applications and reported them to the relevant vendors. This paper tackles this challenge by introducing classming, a novel, effective approach to performing deep, differential JVM testing. It can also become a security-critical vulnerability when attackers can control the input to consume a large amount of memory to launch a Denial-of-Service attack. DocTer detects 94 bugs from 174 API functions, including one previously unknown security vulnerability that is now documented in the CVE database, while a baseline technique without input constraints detects only 59 bugs. In this work, we present ContractFuzzer, a novel fuzzer to test Ethereum smart contracts for security vulnerabilities. In fact, we discover that test cases in fuzzing history contain rich knowledge of the key mutation strategies that lead to the discovery of unique paths and crashes. Features and limitations. ROSrosoutloglog ROSloglogdebugloginfologwarnlogerrlogfatal Furthermore, if a process fuzzes its own kernel, a kernel crash highly impacts the performance of the fuzzer as the OS needs to reboot. Abstract: Fuzzing is a promising method for discovering vulnerabilities. The idea behind fuzzing is to check the behavior of software against a large number of randomly generated inputs, trying to cover all interesting parts of the input space, while observing the tested software for anomalous behaviour. First, drivers receive complex and untrusted inputs from not only the user space but also the hardware. It allows filtering a lot of inputs that dont give new results. Abstract: Existing directed fuzzers are not efficient enough. A number of our reported issues have already been confirmed as JVM defects, and some even match recent clarifications and changes to the JVM specification, Java SE 8 Edition. In this paper, we present DeepFuzzer, an enhanced greybox fuzzer with qualified seed generation, balanced seed selection, and hybrid seed mutation. Moreover, existing works for device fuzzing are more likely to detect memory corruption vulnerabilities.In this paper, we propose a solution ESRFuzzer to address these issues. JavaScript engine requires highly structured input JavaScript programs that are syntactically and semantically correct. However, current exploit generation tools have difficulty to solve hard constraints in execution paths and cannot simulate the blockchain behaviors very well. All the newly discovered vulnerabilities have been confirmed by their vendors. More specifically, traditional testing approaches relies on observable crashes of a program, and binary instrumentation techniques are used to improve the detection of those faulty states. A key ingredient of many of these tools is Satisfiability Modulo Theories (SMT) solvers, which are used to reason over symbolic expressions collected during the analysis. We find that many code variants labeled ``safe with respect to side-channel vulnerabilities are in fact vulnerable to JIT-induced timing side channels. Abstract: Android apps are GUI-based event-driven software and have become ubiquitous in recent years. Less understood, however, are the implications of other bug-related information (e.g., bug descriptions in CVE), particularly whether utilization of such information can facilitate exploit generation, even on other vulnerability types that have never been automatically attacked. Even compared with the best cases of AFL, AFLFast, AFLGo and FairFuzz, EnFuzz discovers 26.8%, 117%, 38.8% and 39.5% more unique crashes, executes 9.16%, 39.2%, 19.9% and 20.0% more paths and covers 5.96%, 12.0%, 21.4% and 11.1% more branches respectively. It first collects all interfaces in target services and uncovers deep nested multi-level interfaces to test. We evaluate SRIs effectiveness by comparing the performance of micro-fuzzing with SRI, measured by the number of AC vulnerabilities detected, to simply using empty values as seed inputs. Abstract: We present the design of an algorithm to maximize the number of bugs found for black-box mutational fuzzing given a program and a seed input. Current state-of-the-art fuzzers for finding JavaScript engine vulnerabilities focus mainly on generating syntactically correct test cases based on either a predefined context-free grammar or a trained probabilistic language model. Abstract: Hybrid fuzzing, which combines fuzzing and concolic execution, is promising in light of the recent performance improvements in concolic engines. Special tools such as Valgrind, Google TSAN and others are used to find such bugs. Such flaws can be found by using fuzzing, a popular testing technique among security researchers.This paper presents Chizpurfle, a novel gray-box fuzzing tool for vendor-specific Android services. Abstract: Fuzzing is a commonly used technique designed to test software by automatically crafting program inputs. Security specialists recommend fuzz testing to identify vulnerabilities within vehicle software systems. We observed, however, that existing fuzzing approaches, either generative or mutational, fall short in fully harvesting high-quality input corpora such as known proof of concept (PoC) exploits or unit tests. Unfortunately, such a characteristic makes it difficult for existing fuzzers to find out critical transaction sequences. With an additional data structure to store the guiding information, the synchronization ensures the information is shared and updated among different fuzzer instances timely. However, due to the complexity of two sides, the binding code is prone to inconsistent semantics and security holes, which lead to severe vulnerabilities. Fuzzing has detected thousands of bugs and vulnerabilities in various applications. This helps us to identify the root cause and facilitate the development of real exploits. After intensive static analysis of the boot loader, emmutaler allows us to create a normal Linux ELF containing it. We tested spotInstr and spotFuzzer in multiple dimensions to show their superior performance and stability. IPC-related security bugs in trusted code could facilitate jailbreaks of sandboxing, and thus are becoming high-value targets. Evaluation of end-to-end coverage-guided testing also shows that our JIGSAW-powered hybrid fuzzer can outperform state-of-the-art testing tools. Since such probing is transparently piggybacked to the regular fuzzing, no prior knowledge of the input specification is needed. Our tool finds out-of-bounds accesses to heap, stack, and global objects, as well as use-after-free bugs. Interestingly, we further observe that even simply executing Havoc itself without appending it to any fuzzer can lead to strong edge coverage performance and outperform most of our studied fuzzers. We present the design and implementation of Fuzzware, a software-only system to fuzz test unmodified monolithic firmware in a scalable way. This paper revisits the fuzzing technique that generates JavaScript environment API calls using random walks on so-called prototype graphs to uncover potentially security-related failures. ros-melodic-desktop is a trimmed-down version of the desktop-full package, containing most official ROS tutorial packages but without perception (PCL, OpenCV, and image related packages) and simulators packages (gazebo, rqt, stage).. Most fuzzing effortsespecially feedback fuzzingare limited to user space components of an operating system (OS), although bugs in kernel components are more severe, because they allow an attacker to gain access to a system with full privileges. First, we identify all the control flow-dependent conditional statements of the target conditional statement. We present the design and implementation of emmutaler, as well as multiple fuzzing experiments ran using it, such as fuzzing the USB and firmware parsing stack. Abstract: Greybox fuzzing is one of the most effective approaches for detecting software vulnerabilities. This paper tackles this challenge by introducing classfuzz, a coverage-directed fuzzing approach that focuses on representative classfiles for differential JVM testing. As a result, developers only need to focus on building the core logic for finding bugs of their own interests. In this paper, we propose a program-state based binary fuzzing approach, named Steelix, which improves the penetration power of a fuzzer at the cost of an acceptable slow down of the execution speed. However, its effectiveness is limited due to the lack of input specification, lack of routers internal running states, and lack of testing environment recovery mechanisms. Abstract: Recently, hybrid fuzzing has been proposed to address the limitations of fuzzing and concolic execution by combining both approaches. This paper proposes R2Z2, an automated tool to find rendering regressions. We propose augmenting evolutionary fuzzing by additionally leveraging information about memory accesses performed by the target program. Existing hybrid fuzzers schedule seeds based on fixed heuristics that aim to predict input utilities. We realized Semantic Fusion as YinYang, a practical SMT solver testing tool. The results show that StochFuzz outperforms state-of-the-art binary-only fuzzers (e.g., e9patch, ddisasm, and RetroWrite) in terms of soundness and cost-effectiveness and achieves performance comparable to source-based fuzzers. one that distinguishes between multiple occurrences of the same variable based on the execution path information. Abstract: Although current fuzz testing (fuzzing) methods are highly effective, there are still many situations such as complex state machines where fully automated approaches fail. We believe that FIFUZZ can effectively augment existing fuzzing approaches to find many real bugs that have been otherwise missed. AFLNET acts as a client and replays variations of the original sequence of messages sent to the server and retains those variations that were effective at increasing the coverage of the code or state space. One known challenge is that much of the kernel code is locked under specific kernel states and current kernel fuzzers are not effective in exploring such an enormous state space. We note that existing directed greybox fuzzing approaches are limited to witnessing reaching a location or witnessing simple event orderings like use-after-free. Coverage-guided tracing leverages two observations: (1) only a fraction of generated test cases increase coverage, and thus require tracing; and (2) coverage-increasing test cases become less frequent over time. As a consequence, using only a small (usually one line) annotation, a user can help the fuzzer to solve previously unsolvable challenges. We propose an approach for addressing this problem. Additionally, if the grammar provided to the fuzzer is incomplete, or does not match the implementation completely, the fuzzer will fail to exercise important parts of the available functionality. To this end, we first propose to model UaF vulnerabilities as typestate properties, Such inaccuracy and incompleteness in coverage introduce serious limitations to fuzzers. It has found over 300 previously unknown bugs in the Chromium browser and many bugs in other software. Abstract: Expressing class specifications via executable constraints is important for various software engineering tasks such as test generation, bug finding and automated debugging, but developers rarely write them. This enables mutations like adding a car driving on the street to an existing image while providing an oracle that accounts for that semantic change. It also slows down the detection of real vulnerabilities by over 43%. We have implemented Steelix and evaluated it on three datasets: LAVA-M dataset, DARPA CGC sample binaries and five real-life programs. Abstract: Directed fuzzing is a practical technique, which concentrates its testing energy on the process toward the target code areas, while costing little on other unconcerned components. However, our initial attempts demonstrate that naive combination actually worsens the performance, which explains that most optimizations are still isolated by stages and metrics. However, there is a problem that the existing gradient-based fuzzers require heavyweight analysis or sufficient learning time. Abstract: A rendering regression is a bug introduced by a web browser where a web page no longer functions as users expect. We make Sydr evaluation artifacts publicly available to facilitate results reproducibility. Fudge automatically generates fuzz driver candidates for libraries based on existing client code. The found inconsistencies expose possible security vulnerabilities and violations of the MQTT specification. Lastly, we discuss some innovative mitigations in use by iPhone boot loaders, that we found during the development of emmutaler. In this paper we introduce the AutoFuzz [1] - extendable, open source framework used for testing network protocol implementations. Additionally, we show that our techniques outperform various state-of-the-art tools on a wide variety of targets across different privilege levels (kernel-space and userland) with no platform-specific code. While fuzzing may have a reputation of being able to explore deep into a programs state space efficiently, nave fuzzers usually have limited code coverage for typical programs since unconstrained random inputs are unlikely to drive the execution down many different paths. We introduce and implement a suite of enhancements that expand CGTs introspection to fuzzings most common code coverage metrics, while maintaining its orders-of-magnitude speedup over conventional always-on coverage tracing. 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