📜 要約
Summary of Topic and Objective
The focus of this research is on LibAFL, a sophisticated and highly customizable fuzzing library written in Rust. The primary objective is to explore and summarize the features and use cases of LibAFL, highlighting its capabilities, performance, and adaptability across various platforms and environments. The investigation aims to provide a comprehensive understanding of how LibAFL can be utilized for effective fuzzing, particularly in identifying bugs and vulnerabilities in software through advanced techniques and instrumentation.
Key Features and Findings
-
High Performance and Scalability:
- LibAFL is optimized for high performance, achieving up to 120k executions per second in Frida-mode on a phone.
- The Low-Level Message Passing (LLMP) system allows for near-linear scalability across multiple cores and machines, enhancing its efficiency in large-scale fuzzing campaigns.
-
Multi-Platform Support:
- The library supports various platforms, including Windows, macOS, Linux, and Android, on both x86_64 and aarch64 architectures.
- It can be built in no_std mode, making it suitable for constrained environments like embedded devices and hypervisors.
-
Modularity and Customization:
- LibAFL is highly modular, allowing users to replace any component of the library to meet specific needs. This includes support for structured input types like abstract syntax trees.
- It integrates with popular instrumentation frameworks such as SanitizerCoverage, Frida, QEMU, and TinyInst, providing flexibility in how fuzzing is conducted.
-
Stateful Fuzzing Capabilities:
- The library addresses the challenges of stateful fuzzing, which involves testing systems that rely on sessions and state models.
- Techniques include mutating the order of messages (the 'trace') and handling statefulness by sending entire traces, which is crucial for achieving full coverage in stateful systems.
-
Monitoring and Analyzing Fuzzing Campaigns:
- Real-time monitoring tools are emphasized for detecting anomalies, crashes, and other significant events during fuzzing.
- Metrics such as code coverage, execution speed, and the number of bugs detected are used to measure the performance of fuzzing campaigns.
- Visualization tools and dashboards provide comprehensive overviews, aiding in the optimization of fuzzing strategies.
Summary of Results and Conclusions
The investigation into LibAFL reveals that it is a powerful and versatile fuzzing library capable of addressing a wide range of fuzzing needs. Its high performance, scalability, and adaptability make it suitable for both large-scale and constrained environments. The modular design allows for extensive customization, enabling users to tailor the library to specific requirements. The integration with various instrumentation frameworks and the ability to handle stateful fuzzing further enhance its utility. Real-time monitoring and analytical tools are crucial for optimizing fuzzing campaigns, ensuring that LibAFL remains an effective tool for identifying software vulnerabilities. Overall, LibAFL stands out as a robust solution for advanced fuzzing applications.
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🏷Introduction to LibAFL
Introduction to LibAFL
LibAFL is a powerful and highly customizable fuzzing library written in Rust, designed to provide the benefits of off-the-shelf fuzzers with a high degree of flexibility and adaptability. It supports multiple platforms, including Windows, macOS, Linux, and Android, and can be built in no_std mode for use in constrained environments like embedded devices. Key features include fast performance, scalability across multiple cores and machines through its Low-Level Message Passing (LLMP) system, and support for various instrumentation backends such as SanitizerCoverage, Frida, QEMU, and TinyInst. LibAFL is highly customizable, allowing users to replace components to suit specific needs, including support for structured input types like abstract syntax trees. The library's documentation, example fuzzers, and related research papers are available on its .
github.com
Fast Performance
LibAFL is optimized for performance, with users reporting up to 120,000 executions per second in Frida-mode on a mobile device, utilizing all available cores. This makes it one of the fastest fuzzing libraries available.
Scalability
LibAFL's Low-Level Message Passing (LLMP) system allows it to scale almost linearly across multiple cores and machines. This feature is particularly useful for large-scale fuzzing operations that require significant computational resources.
Adaptability
LibAFL is highly customizable, allowing users to replace each component to suit their specific needs. This includes adding support for structured input types, such as abstract syntax trees. This adaptability makes it a versatile tool for various fuzzing scenarios.
Multi-Platform Support
LibAFL has been confirmed to work on Windows, macOS, Linux, and Android, on both x86_64 and aarch64 architectures. It can also be built in no_std mode for use in embedded devices and hypervisors, making it a versatile tool for security researchers and developers.
Flexible Instrumentation
LibAFL supports various instrumentation backends, including SanitizerCoverage, Frida, QEMU, and TinyInst, making it easy to integrate with different target environments. This flexibility allows for more precise and effective fuzzing.
Portability
LibAFL can be built in no_std mode, which means it does not require a specific OS-dependent runtime to function. This allows it to be used in more constrained environments, such as embedded devices, hypervisors, or even WebAssembly.
Customizability
LibAFL supports a wide range of instrumentation backends, including binary-only modes like QEMU-Mode and Frida-Mode with AddressSanitizer and CmpLog, as well as custom instrumentation options. Developers can tailor the fuzzer to their specific needs, making it a highly customizable tool.
Research and Development
LibAFL has integrated techniques from more than 20 previous works and conducted extensive experiments to demonstrate the benefits of their framework in combining and evaluating different approaches. This makes it a solid base for comparative and extensible research in the future.
For more detailed information, you can refer to the and the .
github.com
aflplus.plus
References
- github.com
- LibAFL: A Framework to Build Modular and Reusable Fuzzers - Eurecom
- springeropen.com
- acm.org
🏷Key Features of LibAFL
Key Features of LibAFL
LibAFL is an advanced fuzzing library written in Rust that allows developers to build highly customizable and scalable fuzzers. Some key features of LibAFL include its high performance, scalability, adaptability, and multi-platform support. It is optimized for performance, with users reaching up to 120k executions per second in Frida-mode on a phone. Its Low Level Message Passing (LLMP) system allows it to scale almost linearly across cores and multiple machines. LibAFL is highly modular, enabling users to replace any part of the library, such as the input format or instrumentation backend. It works on Windows, macOS, Linux, and Android on both x86_64 and aarch64 architectures, and can also be built in no_std mode for embedded devices and hypervisors. Additionally, LibAFL offers integrations with popular instrumentation frameworks like SanitizerCoverage and Frida. The library's documentation and example fuzzers are available in the repository, providing a great starting point for new users. LibAFL is actively developed and maintained by Andrea Fioraldi and Dominik Maier, with contributions and bug reports welcomed.
Fuzzing Xpdf with LibAFL
The context provided describes the process of setting up a Rust-based fuzzer using the LibAFL library to find a vulnerability (CVE-2019-13288) in the Xpdf PDF viewer. The key points are:
- LibAFL is an "Advanced Fuzzing Library" written in Rust that is fast, multi-platform, and scalable.
- The fuzzer is set up as a Rust workspace with a project for Exercise 1, which involves fuzzing Xpdf 3.02 to find a crash/PoC for CVE-2019-13288.
- The setup includes building Xpdf with AFL++'s afl-clang-fast compiler, creating input and output corpora, configuring various LibAFL components (Observers, Feedbacks, State, Monitor, EventManager, Scheduler, Executor, Mutator, Stage), and running the fuzzer.
- After about 10 minutes of fuzzing, the fuzzer found a timeout that also produced new coverage, indicating a potential vulnerability.
- The resulting PDF file in the "timeouts" corpus can be used to reproduce the issue, where Xpdf crashes with a segmentation fault.
The companion code for this exercise can be found at the .
github.com
Summary of LibAFL - Advanced Fuzzing Library
LibAFL is an advanced fuzzing library written in Rust that allows developers to build highly customizable and scalable fuzzers. Some key features of LibAFL include:
- Fast: LibAFL is optimized for performance, with users reaching up to 120k executions per second in Frida-mode on a phone.
- Scalable: LibAFL's Low Level Message Passing (LLMP) system allows it to scale almost linearly across cores and multiple machines.
- Adaptable: LibAFL is highly modular, allowing users to replace any part of the library, such as the input format or instrumentation backend.
- Multi-platform: LibAFL has been confirmed to work on Windows, macOS, Linux, and Android on both x86_64 and aarch64 architectures. It can also be built in no_std mode for embedded devices and hypervisors.
- Integrations: LibAFL offers integrations with popular instrumentation frameworks like SanitizerCoverage and Frida.
To get started with LibAFL, users need to install the Rust development language, clone the repository, and build the library using Cargo. The library's documentation and example fuzzers are available in the repository, providing a great starting point for new users.
LibAFL is actively developed and maintained by Andrea Fioraldi and Dominik Maier. Contributions and bug reports are welcome, and users can refer to the TODO.md file for planned features.
github.com
LibAFL: A Framework to Build Modular and Reusable Fuzzers
LibAFL is a framework that provides a set of utilities for building custom fuzzers. It allows for the creation of modular and reusable fuzzers, making it easier to adapt fuzzing techniques to specific targets. LibAFL is designed to be highly customizable, enabling researchers and security practitioners to incorporate their own mutation strategies, feedback mechanisms, and other components into the fuzzing process.
Fuzzing Firmware
The paper discusses the challenges of fuzzing firmware, particularly the complex interactions between firmware and hardware. State-of-the-art methods re-host firmware in emulators and facilitate interactions with hardware by provisioning for inputs from a diversity of methods (such as interrupts) from a plethora of devices (such as modems). However, the authors recognize a significant disconnect between how a fuzzer generates inputs (as a monolithic file) and how the inputs are consumed during re-hosted execution (as a stream, in slices, per peripheral).
The authors demonstrate that this disconnect can significantly impact a fuzzer's effectiveness at discovering inputs that explore deeper code and bugs. To address this, they propose a new approach called "multi-stream input generation and representation", where inputs are now a collection of independent streams, one for each peripheral.
MULTIFUZZ: A New Fuzzer for Monolithic Firmware
The authors design and build a new fuzzer, MULTIFUZZ, for testing monolithic firmware. MULTIFUZZ implements the multi-stream input generation and representation approach, including:
- Stream-specific mutation strategies
- Efficient methods for generating useful values for peripherals
- Enhancing the use of information learned during fuzzing
- Improving a fuzzer's ability to handle roadblocks
MULTIFUZZ is evaluated on synthetic and real-world firmware targets. It passes all 66 unit tests from a benchmark consisting of 46 synthetic binaries targeting a diverse set of microcontrollers. On an evaluation with 23 real-world firmware targets, MULTIFUZZ outperforms the state-of-the-art fuzzers Fuzzware and Ember-IO, reaching significantly more code on 14 out of the 23 firmware targets and discovering 18 new bugs.
Comprehensive Summary of the Context
The context provided is a brief description of a Rust-based fuzzing framework called LibAFL. Some key points from the summary:
- LibAFL is a fuzzing framework written from scratch in Rust.
- It allows users to create almost any type of fuzzer.
- LibAFL already outperforms many well-established fuzzing tools like libfuzzer and AFL.
- The context was cut short due to a network security block, preventing the full description from being provided.
Since the original context was limited, a more comprehensive summary could not be generated. The key information available has been summarized above.
Summary of the Context
libAFL is a fuzzing library that can help identify vulnerabilities in software by generating inputs that trigger unexpected behavior. Fuzzing is a technique used to find bugs and vulnerabilities in software by providing it with invalid, unexpected, or random data as input. libAFL is a library that can be used to implement fuzzing in software development projects.
The context provided is a brief description of libAFL and its purpose, without any specific details or examples. To provide a more comprehensive summary, additional information about the library's features, use cases, or any notable vulnerabilities it has helped identify would be helpful.
🏷In-Memory Fuzzing with LibAFL
In-Memory Fuzzing with LibAFL
The context provided discusses the use of libAFL, a fuzzing library, for in-memory fuzzing. Key points include the technique's focus on finding bugs and vulnerabilities in software through memory management, function calls, and input handling. The video also touches on general programming and software development topics such as code optimization, debugging, and testing. However, due to the language barrier, more specific details or examples could not be extracted from the context provided.
[Fuzzing with libAFL] How to Use libAFL for In-Memory Fuzzing
The context provided is not in English, but rather a mix of Hindi and English. Since the question asks for a comprehensive and informative summary based on the context, I will provide a summary to the best of my ability.
The context appears to be a video transcript discussing the use of libAFL, a fuzzing library, for in-memory fuzzing. Some key points that can be summarized from the context:
- The video discusses how to use libAFL for in-memory fuzzing, which is a technique for finding bugs and vulnerabilities in software.
- It mentions various concepts and techniques related to in-memory fuzzing, such as memory management, function calls, and input handling.
- The video also seems to cover some general programming and software development topics, such as code optimization, debugging, and testing.
- However, due to the language barrier, I was unable to extract more specific details or examples from the context provided.
Unfortunately, without a proper English transcript or context, I cannot provide a more comprehensive summary. If you are able to provide the context in English, I would be happy to revisit and provide a more detailed and informative summary.
[PDF] Forming Faster Firmware Fuzzers - USENIX
This paper proposes SAFIRE FUZZ, a new and performant rehosting and fuzzing approach for embedded binary-only ARM firmware. Instead of rehosting target firmware using a general-purpose emulator, SAFIRE FUZZ employs a technique called "near-native rehosting" that runs firmware directly on a more powerful ARM system, while inserting fuzzing instrumentation on-the-fly.
Key highlights:
Motivation
- Existing rehosting solutions suffer from high emulator-induced overhead, leading to less-than-optimal execution speeds for firmware fuzzing.
- SAFIRE FUZZ aims to overcome this by leveraging the similarities between the ARM instruction sets used in embedded systems and more powerful desktop/server ARM cores.
Approach
- SAFIRE FUZZ uses dynamic binary rewriting to execute the firmware on a powerful ARM Cortex-A core, while inserting fuzzing instrumentation.
- It follows the High-Level Emulation (HLE) approach to handle hardware interactions by replacing them with HAL-based hooks.
Evaluation
- SAFIRE FUZZ is evaluated against state-of-the-art rehosting frameworks like HALucinator and Fuzzware.
- It achieves up to 690x speedup compared to HALucinator and up to 147x compared to Fuzzware during 24-hour fuzzing campaigns.
- SAFIRE FUZZ also discovers up to 30% more basic blocks than the compared approaches.
Contributions
- Proposal of SAFIRE FUZZ: a high-performance near-native rehosting framework for interactive execution of embedded ARM firmware.
- Tight integration of in-process fuzzing with dynamic binary rewriting techniques and HAL function hooking.
- Evaluation showing that near-native rehosting outperforms rehosting approaches built on top of general-purpose emulators.
🏷Stateful Fuzzing Capabilities
Stateful Fuzzing Capabilities
The section 'Stateful Fuzzing Capabilities' explores the challenges and methodologies associated with stateful fuzzing, as opposed to stateless fuzzing. While stateless fuzzers like AFL have been successful in testing systems without state models, stateful systems require different approaches due to their reliance on sessions and state models. The key difference lies in the need for stateful fuzzers to mutate not just individual messages but also the order of messages (the 'trace') to achieve full coverage. Stateless fuzzers lack the capabilities to handle stateful systems effectively, such as sending messages over the network and avoiding system restarts after each message. The research community has developed various approaches to tackle stateful fuzzing, including bypassing statefulness by bringing the system under test (SUT) to a certain state and dealing with statefulness by sending entire traces and mutating both messages and their order. Benchmarking stateful fuzzers is also challenging, as state coverage is crucial in addition to code coverage. The paper concludes that while stateful fuzzing is an active area of research, it remains less mature and scalable compared to stateless fuzzing.
Key Findings from Research
-
Execution Speed: The execution speed was vastly different between the two target applications (x509 Certificate Parser and SSL Server). This was due to the fact that running the SSL Server required a lot of setup, which took a significant amount of time. ([PDF] Effectiveness of fuzz testing high-security applications - DiVA portal]())diva-portal.se
-
Hangs: Both applications experienced multiple unique hangs, which are not as interesting as crashes but may still be indicative of memory issues or potential denial of service attacks. ([PDF] Effectiveness of fuzz testing high-security applications - DiVA portal]())diva-portal.se
-
Crashes: No crashes were encountered during the fuzzing, likely due to the extensive testing that mbed TLS undergoes before each release. However, more time spent on fuzzing may have yielded different results. ([PDF] Effectiveness of fuzz testing high-security applications - DiVA portal]())diva-portal.se
-
Stability: The program stability when fuzzing the SSL Server was 73.36%, which is not optimal for AFL to discern meaningful effects from random changes. This was due to the use of a pseudo-random number generator in mbed TLS. ([PDF] Effectiveness of fuzz testing high-security applications - DiVA portal]())diva-portal.se
Approaches Used by Stateful Fuzzers
-
Bypassing Statefulness: Bringing the SUT to a certain state using prefixes, adding artificial loops, using fuzzing targets, or cramming multiple messages into one. ()arxiv.org
-
Dealing with Statefulness: Sending entire traces to the SUT and making mutations on both messages and their order. Some fuzzers also try to infer and use the state model of the SUT. ()arxiv.org
Benchmarking Stateful Fuzzers
Benchmarking stateful fuzzers is challenging, as code coverage alone is not sufficient - state coverage is also crucial. The only benchmark framework for stateful protocols, ProFuzzBench, currently only compares fuzzers based on code coverage. ()
arxiv.org
Conclusion
The paper highlights that while the research community is actively working on stateful fuzzing, most approaches are not scalable and are tailored to specific SUTs. Stateful fuzzing remains a much younger field compared to stateless fuzzing, and the paper sheds light on the challenges and techniques, paving the way for further research. ()
arxiv.org
🏷Monitoring and Analyzing Fuzzing Campaigns
Monitoring and Analyzing Fuzzing Campaigns
The section 'Monitoring and Analyzing Fuzzing Campaigns' in the report 'Exploring the Advanced Features and Use Cases of LibAFL Fuzzer' focuses on the methodologies and tools used to observe and evaluate the progress and effectiveness of fuzzing campaigns. It emphasizes the importance of real-time monitoring to detect anomalies, crashes, and other significant events during the fuzzing process. The section also discusses various metrics and analytical techniques that can be employed to measure the performance of the fuzzing campaign, such as code coverage, execution speed, and the number of bugs detected. Additionally, it highlights the use of visualization tools and dashboards to provide a comprehensive overview of the fuzzing activities, enabling researchers to make informed decisions and optimize their fuzzing strategies. The integration of these monitoring and analysis tools is crucial for identifying weaknesses in the fuzzing process and improving the overall efficiency and effectiveness of the campaign.
Fuzzing101 with LibAFL - Part IV: Fuzzing LibTIFF - GitLab
This post covers fuzzing the libtiff library to solve Exercise 4 from the Fuzzing101 repository. The goal is to find CVE-2016-9297 in version 4.0.6 of libtiff.
The author treats the
tiffinfoKey components used in this fuzzer include:
- InMemoryCorpus and OnDiskCorpus: Manage the input corpus and output directories.
- QemuExecutor: Executes the target binary in a QEMU-emulated environment, allowing for cross-architecture fuzzing.
- QemuFilesystemBytesHelper and QemuGPRegisterHelper: QEMU-specific helpers that manage the input bytes and register state, respectively.
- Syscall Hook: Intercepts and modifies specific syscalls (e.g.,
,read
,mmap
) to control the input to the target binary.munmap - StdScheduledMutator and StdMutationalStage: Perform mutations on the input corpus, including token-based mutations.
- Launcher: Spawns and manages multiple fuzzer instances in parallel.
The post provides a detailed, step-by-step walkthrough of the fuzzer implementation, covering each component and its role in the overall fuzzing process. The author also discusses the rationale behind certain design decisions, such as treating the target as a blackbox and cross-compiling for a different architecture.
By the end of the post, the author has successfully set up the fuzzing environment, integrated the necessary components, and started the fuzzing process, which eventually leads to the discovery of the target vulnerability (CVE-2016-9297). .
epi052.gitlab.io
Summary of the Context
The context provided discusses the development of LIBAFL LIBFUZZER, a fuzzer based on the LIBAFL framework, which aims to provide a drop-in replacement for the widely used LIBFUZZER fuzzer. The key points are:
- LIBAFL Framework: LIBAFL is a fuzzing framework developed by the same group that created AFL++, which allows researchers to develop fuzzers at a component level, making it easier to experiment with different fuzzing techniques.
- LIBAFL LIBFUZZER: LIBAFL LIBFUZZER is a fuzzer developed using the LIBAFL framework, designed to be a near-complete replacement for LIBFUZZER, with support for the most common features without introducing new build requirements.
- Evaluation on FuzzBench: The paper evaluates the performance of LIBAFL LIBFUZZER on the FuzzBench platform, comparing it to LIBFUZZER in both coverage and bug-finding benchmarks.
- Performance Insights: LIBAFL LIBFUZZER performed well on the coverage benchmarks but struggled with some bug-based benchmarks, primarily due to issues with OOM handling and handling of custom mutators and crossover methods.
- Future Improvements: The authors discuss plans to address the identified weaknesses in LIBAFL LIBFUZZER, such as improving OOM handling and exploring options for string-specialized mutations.
Overall, the context presents LIBAFL LIBFUZZER as a promising alternative to LIBFUZZER, with the potential to provide continued support and newly-discovered fuzzing techniques to LIBFUZZER-oriented fuzz harnesses. .
aflplus.plus
[PDF] A Comparison of Fuzzing Tools - University of Twente Student Theses
This work demonstrates the complete operation of three fuzzers, namely American Fuzzy Lop (AFL), LibFuzzer, and Angora Fuzzer, as well as a comparison of these fuzzers with program metrics such as code coverage, types of bugs or vulnerabilities detected, number of bugs detected, and execution speed, which in turn measures the fuzzer's performance.
Key Findings
- Code Coverage: LibFuzzer achieved the highest code coverage, covering 82.08% of lines, 93.33% of functions, and 79.49% of branches in the FuzzGoat program. AFL covered 81.5% of lines, 92.3% of functions, and 70.1% of branches. Angora Fuzzer had the lowest coverage at 22.15% of lines, 57.14% of functions.
- Types of Bugs Detected: LibFuzzer detected two types of bugs - Divide by Zero and Signed Integer Overflow. AFL detected only the Divide by Zero bug. Angora Fuzzer failed to detect any bugs.
- Number of Bugs Detected: Both AFL and LibFuzzer detected 2 bugs in total, while Angora Fuzzer detected 0 bugs.
- Execution Speed: LibFuzzer took approximately 2 seconds to detect both bugs, while AFL took around 5 seconds. Angora Fuzzer's execution speed could not be determined as it failed to fuzz the target program.
In summary, the results show that LibFuzzer outperformed the other two fuzzers in terms of code coverage and bug detection capabilities. AFL also performed reasonably well, while Angora Fuzzer struggled to fuzz the target programs effectively. .
utwente.nl
[PDF] LibAFL QEMU: A Library for Fuzzing-oriented Emulation
This paper presents LIBAFL QEMU, a new library for fuzzing with emulation based on QEMU. The library is designed to allow flexible usage of the underlying QEMU emulator to run a wide variety of targets, with ready-to-use instrumentations available for fuzzing needs.
The key contributions of this work are:
- Development of a library for fuzzing-oriented emulation with a powerful interface and many different built-in capabilities.
- Presentation of two use cases on how to fuzz an Android library and a Windows kernel driver, and comparison with the state-of-the-art.
- Release of the framework and the examples as open-source software.
The authors address the limitations of existing QEMU forks used in fuzzing by offering a well-integrated, maintainable and up-to-date solution. LIBAFL QEMU not only offers an advantage in terms of fuzzer development with a customizable interface, but it can also outperform the most used solution for userspace fuzzing, AFL++QEMU, in terms of speed, uncovered coverage and bugs found.
In addition to userspace applications, the authors make use of QEMU's features to the fullest by handling full system targets. The implementation can execute embedded applications or full virtual machines that require the most recent versions of QEMU, and the evaluation shows it can reach comparable performance to the state-of-the-art KAFL for Windows kernel fuzzing, with the overhead expected by a software emulator.
Overall, LIBAFL QEMU provides a flexible and performant solution for binary-only fuzzing, bridging the gap between the latest versions of QEMU and the world of fuzzing. .
hal.science
What Is Fuzz Testing and How Does It Work?
Fuzz testing, also known as fuzzing, is an automated software testing method that injects invalid, malformed, or unexpected inputs into a system to reveal software defects and vulnerabilities. The process involves three key components:
- The Poet: This component creates the malformed inputs or test cases, which can be random, template evolutionary, or generational.
- The Courier: This component delivers the test cases to the target software.
- The Oracle: This component detects if a failure has occurred in the target system.
Fuzz testing offers several benefits to a security and quality program, including the ability to identify vulnerabilities that may not be detected by traditional testing methods.
Defensics Fuzz Tester by Synopsys
Defensics, an industry-leading fuzz tester by Synopsys, is a comprehensive automated solution that empowers organizations to intelligently test their software and uncover unknown vulnerabilities. Defensics offers several key functionalities:
- Generational Testing: Defensics incorporates an advanced algorithm to increase testing coverage and produce results with high accuracy. It offers over 250 prebuilt protocol test suites and the ability to customize tests for proprietary protocols.
- Protocol Testing Coverage: Defensics supports a broader range of protocols, interfaces, and RFCs than any other tool on the market (nearly 300).
- Broad Coverage: Defensics can test software and devices using 5G and 4G LTE networks to identify vulnerabilities in emerging technologies, such as the Internet of Things and 5G.
By leveraging the power of Defensics, organizations can effectively secure their software and address unknown vulnerabilities without compromising the safety and quality performance of their products. .
synopsys.com
🖍 考察
Research Findings
LibAFL is a powerful and highly customizable fuzzing library written in Rust, designed to provide the benefits of off-the-shelf fuzzers with a high degree of flexibility and adaptability. It supports multiple platforms, including Windows, macOS, Linux, and Android, and can be built in no_std mode for use in constrained environments like embedded devices. Key features include:
- High Performance: Optimized for performance, with users reaching up to 120k executions per second in Frida-mode on a phone.
- Scalability: Scales almost linearly across cores and multiple machines through its Low-Level Message Passing (LLMP) system.
- Multi-Platform Support: Works on Windows, macOS, Linux, and Android on both x86_64 and aarch64 architectures.
- Modularity: Highly modular, enabling users to replace any part of the library, such as the input format or instrumentation backend.
- Instrumentation Backends: Supports various instrumentation backends such as SanitizerCoverage, Frida, QEMU, and TinyInst.
- Structured Input Types: Supports structured input types like abstract syntax trees.
- Documentation and Examples: Comprehensive documentation and example fuzzers are available on its GitHub repository.
Estimation
The primary issues not fully addressed in the research findings include the specific use cases and practical applications of LibAFL. To address these, we can break down the problem into smaller units:
-
Use Cases in Different Environments:
- Embedded Systems: Given its no_std mode, LibAFL can be used in resource-constrained environments.
- Multi-Core Systems: Its LLMP system allows for efficient use in multi-core and distributed systems.
- Security Testing: With its high performance and support for various instrumentation backends, it is suitable for security testing of applications.
-
Practical Applications:
- Bug Detection: Effective in identifying bugs and vulnerabilities in software through memory management, function calls, and input handling.
- Stateful Fuzzing: Capable of handling stateful systems by mutating not just individual messages but also the order of messages.
- Real-Time Monitoring: Tools and methodologies for real-time monitoring and analysis of fuzzing campaigns, including metrics like code coverage and execution speed.
Combining these units, we can conclude that LibAFL is versatile and can be applied in various domains, from embedded systems to large-scale distributed environments, making it a valuable tool for developers and security researchers.
Analysis
The research findings and estimations provide a comprehensive overview of LibAFL's capabilities and potential use cases. However, several critical points warrant further analysis:
- Performance Metrics: While high performance is highlighted, specific benchmarks and comparisons with other fuzzing tools would provide a clearer picture of its efficiency.
- Scalability: The LLMP system's scalability is promising, but real-world examples and case studies would validate its effectiveness.
- Stateful vs. Stateless Fuzzing: The distinction between stateful and stateless fuzzing is crucial. LibAFL's ability to handle stateful systems through trace mutation is a significant advantage, but its practical implementation and success rates need further exploration.
Integrating these insights, LibAFL emerges as a robust and adaptable fuzzing library suitable for a wide range of applications. Its modularity and support for various platforms and instrumentation backends make it a versatile tool for both developers and security researchers. Future research should focus on performance benchmarks, real-world scalability, and detailed case studies to further validate its capabilities.
Future Research
Reflecting on the limitations of the current research, the following new research themes are proposed:
- Performance Benchmarking of LibAFL: Comparative analysis with other fuzzing tools.
- Real-World Scalability Case Studies: Practical examples of LibAFL's scalability in multi-core and distributed environments.
- Stateful Fuzzing Effectiveness: Detailed exploration of stateful fuzzing techniques and success rates.
- Integration with Other Security Tools: Potential for integrating LibAFL with other security testing frameworks.
- User Experience and Usability: Assessing the ease of use and learning curve for new users.
These themes will help address the current research gaps and provide a more comprehensive understanding of LibAFL's capabilities and applications.
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🏷 Introduction to LibAFL
Introduction - The LibAFL Fuzzing Library
LibAFL gives you many of the benefits of an off-the-shelf fuzzer, while being completely customizable. Some highlight features currently include: multi platform ...
AFLplusplus/LibAFL: Advanced Fuzzing Library - GitHub
LibAFL is a collection of reusable pieces of fuzzers, written in Rust. It is fast, multi-platform, no_std compatible, and scales over cores and machines. It ...
LibAFL: A Framework to Build Modular and Reusable Fuzzers
To tackle this problem, in this paper we propose LibAFL, a framework to build modular and reusable fuzzers. We discuss the different components generally used ...
LibAFL: A Framework to Build Modular and Reusable Fuzzers
To tackle this problem, in this paper we propose LibAFL, a framework to build modular and reusable fuzzers. We discuss the different components ...
LibAFL: A framework to build modular and reusable fuzzers - Eurecom
To tackle this problem, in this paper we propose LibAFL, a framework to build modular and reusable fuzzers. We discuss the different components generally used ...
LibAFL: A Framework to Build Modular and Reusable Fuzzers
This causes a terrible performance when the fuzzer is scaled across multiple cores [23]. ... Boosting Fuzzer. Efficiency: An Information Theoretic ...
Fuzzing: a survey | Cybersecurity | Full Text
#### Fuzzing: a survey | Cybersecurity | Full Text
This paper presents a comprehensive review and summary of fuzzing and its latest progress. Firstly, the authors compare fuzzing with other vulnerability discovery solutions, and then introduce the concepts and key challenges of fuzzing. They then focus on the state-of-the-art coverage-based fuzzing, which has made great progress in recent years. Finally, the authors summarize the techniques integrated with fuzzing, the applications, and possible new trends of fuzzing.
#### Background
The paper discusses traditional vulnerability discovery techniques, including static analysis, dynamic analysis, taint analysis, symbolic execution, and fuzzing. It summarizes the advantages and disadvantages of each technique.
#### Fuzzing
The paper explains the working process of fuzzing, which consists of four main stages: testcase generation, testcase running, program execution state monitoring, and analysis of exceptions. It also discusses different types of fuzzers, such as generation-based vs. mutation-based, white box vs. gray box vs. black box, and directed vs. coverage-based. The key challenges in fuzzing, such as how to mutate seed inputs, achieve high code coverage, and pass validations, are also discussed.
#### Coverage-based Fuzzing
The paper focuses on coverage-based fuzzing, which has proved to be quite effective and efficient. It explains how coverage-based fuzzers, such as AFL, track code coverage through instrumentation and use this information to guide the fuzzing process.
#### Techniques Integrated in Fuzzing
The paper summarizes various techniques that have been integrated into fuzzing to address the key challenges, including static analysis, taint analysis, code instrumentation, symbolic execution, and machine learning techniques.
#### Fuzzing Towards Different Applications
The paper discusses how fuzzing has been applied to different types of applications, such as file format fuzzing, kernel fuzzing, and protocol fuzzing, and the specific challenges and solutions in each domain.
#### New Trends of Fuzzing
The paper discusses potential new trends in fuzzing, such as the use of smart fuzzing techniques, new technologies like machine learning, and new system/hardware features.
[Cite this article](https://cybersecurity.springeropen.com#citeas)
🏷 Key Features of LibAFL
Fuzzing101 with LibAFL - Part I: Fuzzing Xpdf - GitLab
It is fast, multi-platform, no_std compatible, and scales well over cores and machines. LibAFL is written and maintained by Andrea Fioraldi, ...
LibAFL - write fuzzers that outperform libfuzzer and afl, completely in ...
Our fuzzing framework, LibAFL, is written in Rust from scratch and allows you to create almost any fuzzer. It already outperforms many well- ...
LibAFL - Advanced Fuzzing Library - Slot Your Fuzzer Together In ...
Advanced Fuzzing Library - Slot your own fuzzers together and extend their features using Rust. LibAFL is written and maintained by Andre...
LibAFL: A Framework to Build Modular and Reusable Fuzzers
This technique mutates the inputs based on instructioncoverage data, or feedback information, collected during the target's execution, to uncover new paths in ...
Fuzzing_in - libAFL is a fuzzing library that can help... - Facebook
libAFL is a fuzzing library that can help identify vulnerabilities in software by generating inputs that trigger unexpected behavior.
🏷 In-Memory Fuzzing with LibAFL
[Fuzzing with libAFL] How to Use libAFL for In-Memory Fuzzing ...
#### [Fuzzing with libAFL] How to Use libAFL for In-Memory Fuzzing
The context provided is not in English, but rather a mix of Hindi and English. Since the question asks for a comprehensive and informative summary based on the context, I will provide a summary to the best of my ability.
The context appears to be a video transcript discussing the use of libAFL, a fuzzing library, for in-memory fuzzing. Some key points that can be summarized from the context:
- The video discusses how to use libAFL for in-memory fuzzing, which is a technique for finding bugs and vulnerabilities in software.
- It mentions various concepts and techniques related to in-memory fuzzing, such as memory management, function calls, and input handling.
- The video also seems to cover some general programming and software development topics, such as code optimization, debugging, and testing.
- However, due to the language barrier, I was unable to extract more specific details or examples from the context provided.
Unfortunately, without a proper English transcript or context, I cannot provide a more comprehensive summary. If you are able to provide the context in English, I would be happy to revisit and provide a more detailed and informative summary.
[PDF] Forming Faster Firmware Fuzzers - USENIX
First, it retrieves the fuzzing input from LibAFL ... For additional analysis of found crashes and a comparison to other rehosting tools, we refer to Appendix A.
🏷 Stateful Fuzzing Capabilities
[PDF] Effectiveness of fuzz testing high-security applications - DiVA portal
This paper investigates the use of fuzz testing as a method to easily discover bugs in software with high security requirements. 1.2 Scope. The ...
Is Stateful Fuzzing Really Challenging? - arXiv
Fuzzing has been proven extremely effective in finding vulnerabilities in software. When it comes to fuzz stateless systems, analysts have ...
🏷 Monitoring and Analyzing Fuzzing Campaigns
Fuzzing101 with LibAFL - Part IV: Fuzzing LibTIFF - GitLab
The repo focuses on AFL++ usage, but this series of posts aims to solve the exercises using LibAFL instead. We'll be exploring the library and ...
[PDF] LIBFUZZER on top of LIBAFL
We find that LIBAFL LIBFUZZER performed very well on the coverage benchmarks while struggling with the bug-based benchmarks conducted in the. SBFT fuzzing ...
[PDF] LibAFL QEMU: A Library for Fuzzing-oriented Emulation - HAL
In this paper, we described a use case on how to fuzz an Android library with both tools and compared the outcome of the fuzzing campaigns. In ...
What Is Fuzz Testing and How Does It Work? | Synopsys
#### What Is Fuzz Testing and How Does It Work?
Fuzz testing, also known as fuzzing, is an automated software testing method that injects invalid, malformed, or unexpected inputs into a system to reveal software defects and vulnerabilities. The process involves three key components:
1. **The Poet**: This component creates the malformed inputs or test cases, which can be random, template evolutionary, or generational.
2. **The Courier**: This component delivers the test cases to the target software.
3. **The Oracle**: This component detects if a failure has occurred in the target system.
Fuzz testing offers several benefits to a security and quality program, including the ability to identify vulnerabilities that may not be detected by traditional testing methods.
#### Defensics Fuzz Tester by Synopsys
[Defensics](https://www.synopsys.com/software-integrity/security-testing/fuzz-testing.html), an industry-leading fuzz tester by Synopsys, is a comprehensive automated solution that empowers organizations to intelligently test their software and uncover unknown vulnerabilities. Defensics offers several key functionalities:
1. **Generational Testing**: Defensics incorporates an advanced algorithm to increase testing coverage and produce results with high accuracy. It offers over 250 prebuilt protocol test suites and the ability to customize tests for proprietary protocols.
2. **Protocol Testing Coverage**: Defensics supports a broader range of protocols, interfaces, and RFCs than any other tool on the market (nearly 300).
3. **Broad Coverage**: Defensics can test software and devices using 5G and 4G LTE networks to identify vulnerabilities in emerging technologies, such as the Internet of Things and 5G.
By leveraging the power of Defensics, organizations can effectively secure their software and address unknown vulnerabilities without compromising the safety and quality performance of their products.
[PDF] A Comparison of Fuzzing Tools - University of Twente Student Theses
This work demonstrates the complete operation of three fuzzers, namely American Fuzzy Lop (AFL), LibFuzzer, and Angora Fuzzer, as well as a comparison of these ...
📖 レポートに利用されていない参考文献
検索結果: 76件追加のソース: 0件チャット: 1件
Baby Fuzzer - The LibAFL Fuzzing Library
This chapter discusses a naive fuzzer using the LibAFL API. You will learn about basic entities such as State , Observer , and Executor . While the following ...
[PDF] LibAFL: A Framework to Build Modular and Reusable Fuzzers
We hope this can help to shed light on current advancements in fuzzing and provide a solid base for comparative and extensible research in the future. 1 ...
LibAFL Introductory workshop - Atredis Partners
This allows a fuzzer to progress by iterating on an input, tracking down interesting features in the target.
[PDF] LIBAFL LIBFUZZER - Eurecom
LIBAFL, developed by the group which originally made AFL++, offers researchers the ability to develop fuzzers at a component level, allowing researchers to ...
libafl - Rust - Docs.rs
LibAFL Core is the main library and contains the fuzzing components and their implementations. A large part of this library depends only on Rust core+alloc and, ...
AFLplusplus/LibAFL-legacy: AFL++ as a library - GitHub
LibAFL is a framework to build fuzzers, with support for mutithreading. The main concept behind LibAFL is not to build the "best" fuzzer, but to ...
[PDF] LibAFL QEMU: A Library for Fuzzing-oriented Emulation
LIBAFL QEMU features fully work with the 7 aforementioned architectures. The design of the breakpoint system is different between. 2https://qemu-project ...
Fuzzing with libAFL] libAFL ForkSever Introduction and running ...
[Fuzzing with libAFL] How to use shared memory mode with libAFL forkserver fuzzer
Fuzzing Rust Using Cargo-libafl (LibAFL-based Fuzzer)
LibAFL: The Advanced Fuzzing Library - Andrea Fioraldi & Dominik ...
GitHub - andreafioraldi/libafl_quickjs_fuzzing: An example fuzzer ...
American Fuzzy Lop (software) - Wikipedia
GitHub - fkie-cad/butterfly: LibAFL components for stateful fuzzing
Concolic Tracing & Hybrid Fuzzing - The LibAFL Fuzzing Library
In hybrid fuzzing, we combine this tracing + solving approach with more traditional fuzzing techniques. Concolic Tracing in LibAFL, SymCC and SymQEMU. The ...
Fuzzing101 with LibAFL - Part III: Fuzzing tcpdump - GitLab
In the repo, he has created exercises and solutions meant to teach the basics of fuzzing to anyone who wants to learn how to find ...
Fuzzing in Depth - AFLplusplus
Fuzzing source code is a three-step process: Compile the target with a special compiler that prepares the target to be fuzzed efficiently. This step is ...
Advanced fuzzing unmasks elusive vulnerabilities
The technique of only instrumenting the the execution path of a vulnerability to see if fuzzing can find it - or why it fails is very valuable ...
LibAFL
GitHub - cpuu/awesome-fuzzing: A curated list of awesome Fuzzing ...
Mutation-Based Fuzzing - The Fuzzing Book
Introduction to Rust fuzzing
GitHub - AFLplusplus/cargo-libafl: Fuzz Rust code with LibAFL
[PDF] µFUZZ: Redesign of Parallel Fuzzing using Microservice Architecture
Abstract. Fuzzing has been widely adopted as an effective testing tech- nique for detecting software bugs. Researchers have explored.
Crash analysis - more info about crash in/from LibAFL · Issue #44
Wrote a harness to fuzz a lib using LibAFL. I get a crash within LibAFL, but cannot reproduce it outside LibAFL (almost identical harness ...
Recent Papers Related To Fuzzing | FuzzingPaper - GitHub Pages
Abstract: Fuzzing is a method to discover software bugs and vulnerabilities by automatic test input generation which has found tremendous recent interest in ...
[PDF] Fuzzing in the 2020s: novel approaches and solutions
• We present a case study that re-implements a differential fuzzer using ... Eventually, AFL++ will be a frontend fuzzer using LibAFL as back-.
[PDF] Fallaway: High Throughput Stateful Fuzzing By Making AFL* State ...
Fuzzing is a popular software test technique. Stateful fuzzing refers to testing stateful software, such as the software implementing vital ...
Luciano Remes | What is a Fuzzer?
GitHub - RickdeJager/TrackmaniaFuzzer: LibAFL-based snapshot ...
GitHub - IntelLabs/PreSiFuzz: Pre-Silicon Hardware Fuzzing Toolkit
OffensiveCon2022: Case Studies of Fuzzing with Xen | PPT
Scaling AFL to a 256 thread machine - Gamozo Labs Blog
Performance is critical to my work and I've been researching specifically fuzzer performance and scaling for the past 5 years. It's ...
[PDF] Forming Faster Firmware Fuzzers - USENIX
We propose SAFIREFUZZ: a high-performance near- native rehosting framework for interactive execution of embedded ARM firmware. • We prove its ...
The art of Fuzzing: Introduction. - Bushido Security
Efficiency comparison of fuzzing tools. | Download Scientific Diagram
[Fuzzing with libAFL] Fuzzing Damn Vulnerable C Program with ...
BandFuzz
Vulnerability-oriented directed fuzzing for binary programs ...
Proposed projects · Issue #119 · AFLplusplus/LibAFL - GitHub
We would like to fully integrate hypervisor-based fuzzing to LibAFL QEMU, with an up-to-date kernel module and integration with the current ...
Which one should I use -Libfuzzer or Google Fuzztest? - Reddit
I am trying to develop a deep learning library from scratch (in C,C++,,mostly c++) and I would like to know which of these tools should I ...
Spawning Instances - The LibAFL Fuzzing Library
The instance becomes a client and can now communicate with all other nodes. Launching nodes manually has the benefit that you can have multiple nodes with ...
LibAFL, the Advanced Fuzzing Library reached 0.8.0 - develop ...
Fuzzing with libAFL] How to install libAFL on system and what are ...
Chips & Salsa: This Hardware Does Not Exist - Intel Community
Defects found by fuzzing software prior to release can be fixed without impacting end users. The industry-wide effort to ensure that silicon and ...
fuzzing-tutorial/README_en.md at main - GitHub
Curated list of classic fuzzing books, papers about fuzzing at information security top conferences over the years, commonly used fuzzing tools, ...
GitHub - wcventure/FuzzingPaper: Recent Fuzzing Paper
Application Fuzzing - CyberHoot
USENIX Security '22 - Fuzzing Hardware Like Software
What is fuzz testing? | Definition from TechTarget
Fuzzing for Software Security (Artech House Information Security and Privacy): Takanen, Ari, Demott, Jared D, Miller, Charles: 9781596932142: ...
amazon.comCatchFuzz: Reliable active anti-fuzzing techniques against coverage ...
Fuzzing techniques that can automatically detect software vulnerabilities are used widely today. However, attackers also abuse these fuzzing ...
[PDF] SBFT Tool Competition 2023 - Fuzzing Track - arXiv
Abstract—This report outlines the objectives, methodology, challenges, and results of the first Fuzzing Competition held at SBFT 2023.
Vader Modular Fuzzer - Draper Laboratory
Fuzzing finds a program's cyber vulnerabilities by repeatedly introducing different data inputs to test for unwanted behaviors such as crashes or memory faults.
Finding Vulnerabilities using Advanced Fuzz… | The AdaCore Blog
Phases of the fuzzing process | Download Scientific Diagram
Vulnerability detection through machine learning-based fuzzing: A ...
Exploit the Fuzz – Exploiting Vulnerabilities in 5G Core Networks ...
Sustainability | Free Full-Text | An Automated Vulnerability ...
Mitigating vulnerabilities in endpoint network stacks | Microsoft ...
Fuzzing Binary Only Targets - AFLplusplus
AFL++, libfuzzer, and other fuzzers are great if you have the source code of the target. ... If a binary rewriter works for your target then you can use afl-fuzz ...
GitHub - seemoo-lab/VirtFuzz: VirtFuzz is a Linux Kernel Fuzzer ...
GitHub - epi052/fuzzing-101-solutions: Companion repository to the ...
Is Stateful Fuzzing Really Challenging? - arXiv
Fuzzing has been proven extremely effective in finding vulnerabilities in software. When it comes to fuzz stateless systems, analysts have ...
Secure Coding in C and C++ Using Fuzz Testing
Fuzzing: The Indispensable Tool for Improving Cybersecurity
What is Fuzzing in Cybersecurity? | Beyond Security
A brief introduction to fuzzing and why it's an important tool for ...
[PDF] Study and Comparison of General Purpose Fuzzers - Arijit Pramanik
In this paper, we evaluate 3 such general-purpose fuzzing tools namely libFuzzer, American Fuzzy Lop(AFL) and honggfuzz on 2 metrics, i.e. their bug finding.
(PDF) Dissecting American Fuzzy Lop -- A FuzzBench Evaluation
The goal of this paper is to provide a comprehensive understanding of the internal mechanisms of AFL by performing experiments and by comparing ...
Modularizing Directed Greybox Fuzzing for Binaries over Multiple ...
Our research fills the gap in the existing literature by extending LibAFL's methodology to encompass directed greybox fuzzing with native ...
Applied Sciences | Free Full-Text | P-Fuzz: A Parallel Grey-Box ...
Fuzzing projects with american fuzzy lop (AFL)
Applied Sciences | Free Full-Text | On the Analysis of Coverage ...
Fuzz Testing (Fuzzing) Tutorial
Fuzz Testing - Software Testing - GeeksforGeeks
要約の参照
#### Answer
LibAFL is an advanced fuzzing library that offers numerous features and use cases for d...
📊 ドメイン統計
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