Skip to content
/ Awesome-AFL Public
forked from Microsvuln/Awesome-AFL

A curated list of different AFL forks and AFL inspired fuzzers with detailed equivalent academic papers with AFL-fuzzing tutorials

License

GPL-3.0 license
0 stars 65 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

CrackerCat/Awesome-AFL

BranchesTags
 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

131 Commits
LICENSE
LICENSE
 
 
README.md
README.md
 
 

Repository files navigation

Awesome-AFL

Welcome to Awesome AFL

A curated list of different AFL forks and AFL inspired fuzzers with detailed equivalent academic papers including AFL-fuzzing tutorials

Projects

  • AFL by Michal Zalewski

    • Original & first versions of AFL fuzzer, american fuzzy lop is a free security-oriented fuzzer that employs genetic algorithms in order to efficiently increase code coverage of the test cases. So far it helped in detection of significant software bugs in dozens of major free software projects, including X.Org Server, PHP, OpenSSL, pngcrush, bash, Firefox, BIND, Qt, and SQLite.

  • AFL++ by van Hauser

    • afl++ is afl 2.56b with community patches, AFLfast power schedules, qemu 3.1 upgrade + laf-intel support, MOpt mutators, InsTrim instrumentation, unicorn_mode and a lot more!

  • WinAFL by Ivan Fratric

    • A fork of AFL for fuzzing Windows binaries

  • afl-dyninst by Cisco Talos Lab

    • American Fuzzy Lop + Dyninst == AFL Fuzzing blackbox binaries

  • TriforceAFL by Jesse Hertz and Tim Newsham of nccgroup

    • This is a patched version of AFL that supports full-system fuzzing using QEMU. The included QEMU has been updated to allow tracing of branches when running a system emulator for x86_64. Extra instructions have been added to start AFL's forkserver, make fuzz settings, and mark the start and stop of test cases.

  • AFL-abiondo by Abiondo

  • aflsmart by Maintained by Thuan Pham

  • aflfast by Marcel Böhme

  • WineAFLplusplusDEMO by Andrea Fioraldi

    • A set of helpers and examples to fuzz Win32 binaries with AFL++ QEMU

  • afl-sensitive by Heng Yin

  • Redqueen by Syssec lab of Ruhr university of germany

  • afl-pin by van Hauser

    • run AFL with pintool

  • Driller by Shellphish team of University of Santa Barbara

    • Augmenting AFL with Symbolic execution, a powerful symbolic execution engine aims at hybrid fuzzing

  • AngoraFuzzer

    • Angora is a mutation-based fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution. Angora: Efficient Fuzzing by Principled Search (https://arxiv.org/abs/1803.01307)

  • VUzzer by Systems and Network Security Group at VU Amsterdam

  • Manul by Maksim Shudrak

  • QSYM by SSLab of Georgia Tech University

    • QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

  • netafl by gavz

    • winAFL patch to enable network-based apps fuzzing

  • Unicorefuzz by The Computer Security Group at Berlin University of Technology

  • SharpFuzz: AFL-based fuzz testing for .NET by Nemanja Mijailovic

  • Nautilus 2.0 - a grammar based feedback fuzzer by Syssec lab of Ruhr university of germany

  • frida-js-afl-instr by Andrea Fioraldi

  • UnTracer-AFL by Stefan Nagy (snagy2@vt.edu) and Matthew Hicks (mdhicks2@vt.edu)

  • kleefl by julieeen

    • Seeding fuzzers with symbolic execution

  • AFLGo Maintained by @mboehme, @thuanpv, and @strongcourage

    • AFLGo is an extension of American Fuzzy Lop (AFL). Given a set of target locations (e.g., folder/file.c:582), AFLGo generates inputs specifically with the objective to exercise these target locations (https://mboehme.github.io/paper/CCS17.pdf)

  • afl-dyninst Maintained by van Hauser

    • American Fuzzy Lop + Dyninst == AFL Fuzzing blackbox binaries

  • afl-dynamorio Maintained by van Hauser

    • run AFL with dynamorio - binary-only fuzzing with dynamorio and afl

  • FairFuzz Maintained by Caroline Lemieux of UC-Berkeley

    • An AFL extension to increase code coverage by targeting rare branches. FairFuzz has a particular advantage on programs with highly nested structure (packet analyzers, xmllint, programs compiled with laf-inte, etc) (http://www.carolemieux.com/fairfuzz-ase18.pdf)

  • Superion Maintained by zhunki

  • UnTracer-AFL Maintained by FoRTE-Research

    • An AFL implementation with UnTracer (our coverage-guided tracer)

  • neuzz Maintained by Dongdongshe

  • FuzzFactory Maintained by Rohan Padhye

    • FuzzFactory is an extension of AFL that generalizes coverage-guided fuzzing to domain-specific testing goals. FuzzFactory allows users to guide the fuzzer's search process without having to modify anything in AFL's search algorithm (https://dl.acm.org/doi/10.1145/3360600)

  • kAFL Maintained by RUB-SysSec

  • AFLNet Maintained by Thuan Pham

  • Grimoire Maintained by Tim Blazytko

  • JQF Maintained by Rohan Padhye of UC-Berkeley

    • JQF is a feedback-directed fuzz testing platform for Java, which uses the abstraction of property-based testing. JQF is built on top of junit-quickcheck: a tool for generating random arguments for parametric Junit test methods. JQF enables better input generation using coverage-guided fuzzing algorithms such as Zest. (https://cs.berkeley.edu/~rohanpadhye/files/zest-issta19.pdf)

  • PerfFuzz Maintained by Caroline Lemieux of UC-Berkeley

    • PerfFuzz: Automatically Generate Pathological Inputs for C/C++ programs : Performance problems in software can arise unexpectedly when programs are provided with inputs that exhibit pathological behavior. But how can we find these inputs in the first place? PerfFuzz can generate such inputs automatically: given a program and at least one seed input, PerfFuzz automatically generates inputs that exercise pathological behavior across program locations, without any domain knowledge.PerfFuzz uses multi-dimensional performance feedback and independently maximizes execution counts for all program locations. This enables PerfFuzz to find a variety of inputs that exercise distinct hot spots in a program. (http://www.carolemieux.com/perffuzz-issta2018.pdf)

  • Ankou Maintained by Valentin Manès aka Jilyac

    • Ankou is a source-based grey-box fuzzer. It intends to use a more rich fitness function by going beyond simple branch coverage and considering the combination of branches during program execution. The details of the technique can be found in our paper "Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference", which is published in ICSE 2020. (https://www.jiliac.com/files/ankou-icse2020.pdf)

  • Savior Maintained by @yaohway

    • SAVIOR, a new hybrid testing framework pioneering a bug-driven principle. Unlike the existing hybrid testing tools, SAVIOR prioritizes the concolic execution of the seeds that are likely to uncover more vulnerabilities. Moreover, SAVIOR verifies all vulnerable program locations along the executing program path. By modeling faulty situations using SMT constraints, SAVIOR reasons the feasibility of vulnerabilities and generates concrete test cases as proofs. Our evaluation shows that the bug-driven approach outperforms mainstream automated testing techniques, including state-of-the-art hybrid testing systems driven by code coverage. (https://arxiv.org/pdf/1906.07327.pdf)

  • T-Fuzz Maintained by @HexHive

    • To improve coverage, existing approaches rely on imprecise heuristics or complex input mutation techniques (e.g., symbolic execution or taint analysis) to bypass sanity checks. Our novel method tackles coverage from a different angle: by removing sanity checks in the target program. T-Fuzz leverages a coverage guided fuzzer to generate inputs. Whenever the fuzzer can no longer trigger new code paths, a light-weight, dynamic tracing based technique detects the input checks that the fuzzergenerated inputs fail. These checks are then removed from the target program. Fuzzing then continues on the transformed program, allowing the code protected by the removed checks to be triggered and potential bugs discovered. Fuzzing transformed programs to find bugs poses two challenges: (1) removal of checks leads to over-approximation andfalse positives, and (2) even for true bugs, the crashing input on the transformed program may not trigger the bug in the original program. 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. (https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8418632)

  • afl-compiler-fuzzer Maintained by Alex Groce from TrailOfBits

    • This is basically afl, run as usual, except that the afl-fuzz-compiler executable should (considerably) improve effectveness when fuzzing any target that takes C-like (in terms of syntax, e.g., Java, Solidity, Rust, C#, Swift, Javascript, Scala, etc. etc.) language files as input.

  • Eclipser Maintained by KAIST-Softsec Lab

    • Eclipser is a binary-based fuzz testing tool that improves upon classic coverage-based fuzzing by leveraging a novel technique called grey-box concolic testing. The details of the technique can be found in our paper "Grey-box Concolic Testing on Binary Code", which is published in ICSE 2019.

  • Parmesan Maintained by Sebastian Österlund of VUSEC

    • ParmeSan is a sanitizer-guided greybox fuzzer based on Angora. we present sanitizer-guided fuzzing, a new design point in this space that specifically optimizes for bug coverage. For this purpose, we make the key observation that while the instrumentation performed by existing software sanitizers are regularly used for detecting fuzzer-induced error conditions, they can further serve as a generic and effective mechanism to identify interesting basic blocks for guiding fuzzers. We present the design and implementation of ParmeSan, a new sanitizer-guided fuzzer that builds on this observation. We show that ParmeSan greatly reduces the TTE of real-world bugs, and finds bugs 37% faster than existing state-of-the-art coverage-based fuzzers (Angora) and 288% faster than directed fuzzers (AFLGo), while still covering the same set of bugs. ParmeSan: Sanitizer-guided Greybox Fuzzing

  • Weizz-Fuzzer by Andrea Fioraldi

    • Fuzzing technologies have evolved at a fast pace in recent years, revealing bugs in programs with ever increasing depth and speed. Applications working with complex formats are however more difficult to take on, as inputs need to meet certain format-specific characteristics to get through the initial parsing stage and reach deeper behaviors of the program. Unlike prior proposals based on manually written format specifications, we propose a technique to automatically generate and mutate inputs for unknown chunk-based binary formats. We identify dependencies between input bytes and comparison instructions, and use them to assign tags that characterize the processing logic of the program. Tags become the building block for structure-aware mutations involving chunks and fields of the input. Our technique can perform comparably to structure-aware fuzzing proposals that require human assistance. Our prototype implementation Weizz revealed 16 unknown bugs in widely used programs. WEIZZ: Automatic Grey-Box Fuzzing for Structured Binary Formats Slides-ISSTA-2020

  • MOpt-AFL by puppet-meteor

    • MOpt-AFL is a AFL-based fuzzer that utilizes a customized Particle Swarm Optimization (PSO) algorithm to find the optimal selection probability distribution of operators with respect to fuzzing effectiveness. More details can be found in the technical report. The installation of MOpt-AFL is the same as AFL's. MOpt: Optimized Mutation Scheduling for Fuzzers

  • Kirenenko by Chengyu Song from University of California, Riverside

    • Super Fast Concolic Execution Engine based on Source Code Taint Tracing

  • UAFuzz by @strongcourage

    • Binary-level Directed Fuzzing for Use-After-Free Vulnerabilities, built on top of AFL-QEMU.

  • Intriguer by Seclab@Yonsei University

    • Hybrid fuzzing, which combines fuzzing and concolic execution, is promising in light of the recent performance improvements in concolic engines. We have observed that there is room for further improvement: symbolic emulation is still slow, unnecessary constraints dominate solving time, resources are overly allocated, and hard-to-trigger bugs are missed. To address these problems, we present a new hybrid fuzzer named Intriguer. The key idea of Intriguer is field-level constraint solving, which optimizes symbolic execution with field-level knowledge. Intriguer performs instruction-level taint analysis and records execution traces without data transfer instructions like mov. Intriguer then reduces the execution traces for tainted instructions that accessed a wide range of input bytes, and infers input fields to build field transition trees. With these optimizations, Intriguer can efficiently perform symbolic emulation for more relevant instructions and invoke a solver for complicated constraints only. Our evaluation results indicate that Intriguer outperforms the state-of-the-art fuzzers: Intriguer found all the bugs in the LAVAM(5h) benchmark dataset for ground truth performance, and also discovered 43 new security bugs in seven real-world programs. We reported the bugs and received 23 new CVEs. Intriguer: Field-Level Constraint Solving for Hybrid Fuzzing

  • TinyAFL by linhlhq

    • TinyAFL is built on top of AFL and TinyInst. It can be fuzz on windows user-mode application without source (supports both x32 and x64) but it is not so reliable and dirty.

  • DeepFuzzer by Ljiee

    • In this paper, we present DeepFuzzer, an enhanced greybox fuzzer with qualified seed generation, balanced seed selection, and hybrid seed mutation. First, we use symbolic execution in a lightweight approach to generate qualified initial seeds which then guide the fuzzer through complex checks. Second, we apply a statistical seed selection algorithm to balance the mutation frequency between different seeds. Further, we develop a hybrid mutation strategy. The random and restricted mutation strategies are combined to maintain a dynamic balance between global exploration and deep search . DeepFuzzer: Accelerated Deep Greybox Fuzzing

  • MEUZZ by RiS3 Lab Of Northeastern University

    • Seed scheduling is a prominent factor in determining the yields of hybrid fuzzing. Existing hybrid fuzzers schedule seeds based on fixed heuristics that aim to predict input utilities. However, such heuristics are not generalizable as there exists no one-size-fits-all rule applicable to different programs. They may work well on the programs from which they were derived, but not others. To overcome this problem, we design a Machine learning-Enhanced hybrid fUZZing system (MEUZZ), which employs supervised machine learning for adaptive and generalizable seed scheduling. MEUZZ determines which new seeds are expected to produce better fuzzing yields based on the knowledge learned from past seed scheduling decisions made on the same or similar programs. MEUZZ's learning is based on a series of features extracted via code reachability and dynamic analysis, which incurs negligible runtime overhead (in microseconds). Moreover, MEUZZ automatically infers the data labels by evaluating the fuzzing performance of each selected seed. MEUZZ: Smart Seed Scheduling for Hybrid Fuzzing

  • winafl-powermopt by Hardik Shah

    • winafl with mopt mutators and afl fast power schedulers

  • SymQEMU by Sebastian Poeplau and Aurélien Francillon

    • Symbolic execution is a powerful technique for software analysis and bug detection. Compilation-based symbolic execution is a recently proposed flavor that has been shown to improve the performance of symbolic execution significantly when source code is available. We demonstrate a novel technique to enable compilation-based symbolic execution of binaries (i.e., without the need for source code). Our system, SymQEMU, builds on top of QEMU, modifying the intermediate representation of the target program before translating it to the host architecture. This enables SymQEMU to compile symbolic-execution capabilities into binaries and reap the associated performance benefits while maintaining architecture independence. We present our approach and implementation, and we show that it outperforms the state-of-the-art binary symbolic executors S2E and QSYM with statistical significance; on some benchmarks, it even achieves better performance than the source-based SymCC. Moreover, our tool has found a previously unknown vulnerability in the well-tested libarchive library, demonstrating its utility in testing real-world software. SymQEMU - Compilation-based symbolic execution for binaries

  • uAFL - Typestate-Guided Fuzzer for Discovering Use-after-Free Vulnerabilities

    • Existing coverage-based fuzzers usually use the individual control flow graph (CFG) edge coverage to guide the fuzzing process, which has shown great potential in finding vulnerabilities. However, CFG edge coverage is not effective in discovering vulnerabilities such as use-after-free (UaF). This is because, to trigger UaF vulnerabilities, one needs not only to cover individual edges, but also to traverse some (long) sequence of edges in a particular order, which is challenging for existing fuzzers. To this end, we propose to model UaF vulnerabilities as typestate properties, and develop a typestateguided fuzzer, named UAFL, for discovering vulnerabilities violating typestate properties. Given a typestate property, we first perform a static typestate analysis to find operation sequences potentially violating the property. Our fuzzing process is then guided by the operation sequences in order to progressively generate test cases triggering property violations. In addition, we also employ an information flow analysis to improve the efficiency of the fuzzing process. We have performed a thorough evaluation of UAFL on 14 widely-used real-world programs. The experiment results show that UAFL substantially outperforms the state-of-the-art fuzzers, including AFL, AFLFast, FairFuzz, MOpt, Angora and QSYM, in terms of the time taken to discover vulnerabilities. We have discovered 10 previously unknown vulnerabilities, and received 5 new CVEs.

  • fuzzilli maintained by Samuel Groß from Google Project Zero

    • A (coverage-)guided fuzzer for dynamic language interpreters based on a custom intermediate language ("FuzzIL") which can be mutated and translated to JavaScript.

  • winnie by SSLab of Georgia Tech University

    • WINNIE : Fuzzing Windows Applications with Harness Synthesis and Fast Cloning In this paper, we propose two solutions to address the challenges Windows fuzzing faces. Our system, WINNIE, first tries to synthesize a harness for the application, a simple program that directly invokes target functions, based on sample executions. It then tests the harness, instead of the original complicated program, using an efficient implementation of fork on Windows. Using these techniques, WINNIE can bypass irrelevant GUI code to test logic deep within the application. We used WINNIE to fuzz 59 closed-source Windows binaries, and it successfully generated valid fuzzing harnesses for all of them. In our evaluation, WINNIE can support 2.2x more programs than existing Windows fuzzers could, and identified 3.9x more program states and achieved 26.6x faster execution. In total, WINNIE found 61 unique bugs in 32 Windows binaries.

  • fuzzolic by Software Analysis and Optimization Laboratory at Sapienza University of Rome

    • In this paper, we investigate whether techniques borrowed from the fuzzing domain can be applied to check whether symbolic formulas are satisfiable in the context of concolic and hybrid fuzzing engines, providing a viable alternative to classic SMT solving techniques. We devise a new approximate solver, FUZZY-SAT, and show that it is both competitive with and complementary to state-of-the-art solvers such as Z3 with respect to handling queries generated by hybrid fuzzers Fuzzing Symbolic Expressions

  • aflteam by Thuan Pham, Manh-Dung Nguyen and Ta Quang Trung

    • Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing Parallel coverage-guided greybox fuzzing is the most common setup for vulnerability discovery at scale. However, so far it has received little attention from the research community compared to single-mode fuzzing, leaving open several problems particularly in its task allocation strategies. Current approaches focus on managing micro tasks, at the seed input level, and their task division algorithms are either ad-hoc or static. In our framework, we leverage research on graph partitioning and search algorithms to propose a systematic and dynamic task allocation solution that works at the macro-task level. First, we design an attributed graph to capture both the program structures (e.g., program call graph) and fuzzing information (e.g., branch coverage). Second, our graph partitioning algorithm divides the global program search space into sub-search-spaces. Finally our search algorithm prioritizes these sub-search-spaces (i.e., tasks) and explores them to maximize code coverage and number of bugs found.

  • Registered Report: Dissecting American Fuzzy Lop - A FuzzBench Evaluation

    • AFL is one of the most used and extended fuzzing projects, adopted by industry and academic researchers alike. While the community agrees on AFL's effectiveness at discovering new vulnerabilities and at its outstanding usability, many of its internal design choices remain untested to date. Security practitioners often clone the project "as-is" and use it as a starting point to develop new techniques, usually taking everything under the hood for granted. Instead, we believe that a careful analysis of the different parameters could help modern fuzzers to improve their performance and explain how each choice can affect the outcome of security testing, either negatively or positively. The goal of this paper is to provide a comprehensive understanding of the internal mechanisms of AFL by performing experiments and comparing different metrics used to evaluate fuzzers. This will prove the efficacy of some patterns and clarify which aspects are instead outdated. To achieve this, we set up nine unique experiments that we carried out on the popular Fuzzbench platform. Each test focuses on a different aspect of AFL, ranging from its mutation approach to the feedback encoding scheme and the scheduling methodologies. Our preliminary findings show that each design choice affects different factors of AFL. While some of these are positively correlated with the number of detected bugs or the target coverage, other features are related to usability and reliability. Most important, the outcome of our experiments will indicate which parts of AFL we should preserve in modern fuzzers.

  • Guiding Greybox Fuzzing with Mutation Testing by Isabella Laybourn, Rohan Padhye and other members of Carnegie-Mellon's cmu-pasta lab

    • Mu2 is a greybox fuzznig procedure that agugments the code coverage feedback to save new inputs if they increase the mutation scrore across all previously generated inputs.

  • Fuzzing with Data Dependency Information by Alessandro Mantovani, Andrea Fioraldi, Davide Balzarotti from S3 Security group of EURECOM

    • Recent advances in fuzz testing have introduced several forms of feedback mechanisms, motivated by the fact that for a large range of programs and libraries, edgecoverage alone is insufficient to reveal complicated bugs. Inspired by this line of research, we examined existing program representations looking for a match between expressiveness of the structure and adaptability to the context of fuzz testing. In particular, we believe that data dependency graphs (DDGs) represent a good candidate for this task, as the set of information embedded by this data structure is potentially useful to find vulnerable constructs by stressing combinations of def-use pairs that would be difficult for a traditional fuzzer to trigger. Since some portions of the dependency graph overlap with the control flow of the program, it is possible to reduce the additional instrumentation to cover only “interesting” data-flow dependencies, those that help the fuzzer to visit the code in a distinct way compared to standard methodologies.

  • Registered Report: DATAFLOW Towards a Data-Flow-Guided Fuzzer by Adrian Herrera from Engineering & Computer Science at ANU

    • We present datAFLow, a greybox fuzzer driven by lightweight data-flow profiling. Whereas control-flow edges represent the order of operations in a program, data-flow edges capture the dependencies between operations that produce data values and the operations that consume them: indeed, there may be no control dependence between those operations. As such, data-flow coverage captures behaviors not visible as control flow and intuitively discovers more or different bugs. Moreover, we establish a framework for reasoning about data-flow coverage, allowing the computational cost of exploration to be balanced with precision

  • sFuzz by seal9055

    • This is a coverage-guided, emulation based greybox fuzzer that makes use of a custom Just-In-Time compiler to achieve near-native performance. It works by lifting RISC-V elf binaries to an intermediate representation before JIT compiling them to x86 during execution. During JIT compilation the code is instrumented to enable fuzzing-improvements such as coverage tracking, asan, cmpcov, or snapshot-based fuzzing.

  • LibAFL Advanced Fuzzing Library - Slot your Fuzzer together in Rust! Scales across cores and machines. For Windows, Android, MacOS, Linux, no_std, ...

  • stateafl by Roberto Natella

    • StateAFL is a fuzzer designed for network servers. It extends the original idea of the AFL fuzzer, which automatically evolves fuzz inputs to maximize code coverage. In addition to code coverage, StateAFL seeks to maximize protocol state coverage. The aim of this tool is to contribute towards a completely-automated solution for stateful protocol fuzzing (similarly to what AFL was able to achieve for stateless programs) and to promote a wider application of fuzzing in real-world systems. The fuzzer does not require developers to implement custom message parsers for the protocol under test. StateAFL automatically infers the current protocol state of the server. At compile-time, it instruments the target server with probes on memory allocations and network I/O operations. At run-time, it takes snapshots of long-lived data within process memory for each protocol iteration (see figure), and it applies fuzzy hashing to map the in-memory state to a unique protocol state.

  • K-Scheduler by Dongdong She

    • Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis We observe that centrality measures from graph analysis naturally provide these three properties and therefore can efficiently approximate the likelihood of reaching unvisited edges by mutating a seed. We therefore build a graph called the edge horizon graph that connects seeds to their closest unvisited nodes and compute the seed node’s centrality to measure the potential edge coverage gain from mutating a seed. We implement our approach in K-Scheduler and compare with many popular seed scheduling strategies. We find that K-Scheduler increases feature coverage by 25.89% compared to Entropic and edge coverage by 4.21% compared to the next best AFL-based seed scheduler, in arithmetic mean on 12 Google FuzzBench programs. It also finds 3 more previously-unknown bugs than the next-best AFL-based seed scheduler

Tutorials

About

A curated list of different AFL forks and AFL inspired fuzzers with detailed equivalent academic papers with AFL-fuzzing tutorials

Resources

Readme

License

GPL-3.0 license
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published