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AFLrustrust
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c2rust version of afl-fuzz
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# american fuzzy lop rust rust (afl++.rs) **This is not AFLRustRust from the fuzzing competition. For it, go to <https://github.com/AFLplusplus/LibAFL/tree/main/fuzzers/fuzzbench_forkserver>** This is a autogenerated rust version of afl_fuzz, generated with c2rust. It's 100% PoC :) Speed wise, this is very comparable for my test binary. Please report stats. To run the rust version afl, install rustup/cargo, then use ```bash cargo run --release --bin afl_fuzz -- -i .. ``` The rust mode was 100% autogenerated off the current dev version of afl++. You might need to install c2rust. For Linux: ```bash rustup install nightly-2019-12-05 rustup component add --toolchain nightly-2019-12-05 rustfmt rustc-dev rustup install nightly-2019-12-05 rustup component add --toolchain nightly-2019-12-05 rustfmt rustc-dev ``` To recreate the bindings from upstream AFLplusplus: ```sh intercept-build make NO_PYTHON=1 CFLAGS="-w" c2rust transpile --binary afl_fuzz --binary afl_showmap --binary afl_tmin --binary afl_analyze --binary afl_gotcpu --binary afl_analyze compile_commands.json ``` (to clean all autogenerated build files, I use `rm -rf **/*.rs *.rs Cargo.toml rust-toolchain target`) Note that all binaries use underscore instead of dashes. # american fuzzy lop plus plus (afl++) <img align="right" src="https://raw.githubusercontent.com/andreafioraldi/AFLplusplus-website/master/static/logo_256x256.png" alt="AFL++ Logo">  Release Version: [2.63c](https://github.com/AFLplusplus/AFLplusplus/releases) Github Version: 2.63d includes all necessary/interesting changes from Google's afl 2.56b Originally developed by Michal "lcamtuf" Zalewski. Repository: [https://github.com/AFLplusplus/AFLplusplus](https://github.com/AFLplusplus/AFLplusplus) afl++ is maintained by: * Marc "van Hauser" Heuse <mh@mh-sec.de>, * Heiko "hexcoder-" Eißfeldt <heiko.eissfeldt@hexco.de>, * Andrea Fioraldi <andreafioraldi@gmail.com> and * Dominik Maier <mail@dmnk.co>. Note that although afl now has a Google afl repository [https://github.com/Google/afl](https://github.com/Google/afl), it is unlikely to receive any notable enhancements: [https://twitter.com/Dor3s/status/1154737061787660288](https://twitter.com/Dor3s/status/1154737061787660288) ## The enhancements compared to the original stock afl Many improvements were made over the official afl release - which did not get any feature improvements since November 2017. Among other changes afl++ has a more performant llvm_mode, supports llvm up to version 11, QEMU 3.1, more speed and crashfixes for QEMU, better *BSD and Android support and much, much more. Additionally the following features and patches have been integrated: * AFLfast's power schedules by Marcel Böhme: [https://github.com/mboehme/aflfast](https://github.com/mboehme/aflfast) * The new excellent MOpt mutator: [https://github.com/puppet-meteor/MOpt-AFL](https://github.com/puppet-meteor/MOpt-AFL) * InsTrim, a very effective CFG llvm_mode instrumentation implementation for large targets: [https://github.com/csienslab/instrim](https://github.com/csienslab/instrim) * C. Holler's afl-fuzz Python mutator module and llvm_mode whitelist support: [https://github.com/choller/afl](https://github.com/choller/afl) * Custom mutator by a library (instead of Python) by kyakdan * Unicorn mode which allows fuzzing of binaries from completely different platforms (integration provided by domenukk) * LAF-Intel or CompCov support for llvm_mode, qemu_mode and unicorn_mode * NeverZero patch for afl-gcc, llvm_mode, qemu_mode and unicorn_mode which prevents a wrapping map value to zero, increases coverage * Persistent mode and deferred forkserver for qemu_mode * Win32 PE binary-only fuzzing with QEMU and Wine * Radamsa mutator (enable with `-R` to add or `-RR` to run it exclusively). * QBDI mode to fuzz android native libraries via QBDI framework * The new CmpLog instrumentation for LLVM and QEMU inspired by [Redqueen](https://www.syssec.ruhr-uni-bochum.de/media/emma/veroeffentlichungen/2018/12/17/NDSS19-Redqueen.pdf) * LLVM mode Ngram coverage by Adrian Herrera [https://github.com/adrianherrera/afl-ngram-pass](https://github.com/adrianherrera/afl-ngram-pass) A more thorough list is available in the PATCHES file. | Feature/Instrumentation | afl-gcc | llvm_mode | gcc_plugin | qemu_mode | unicorn_mode | | ----------------------- |:-------:|:---------:|:----------:|:----------------:|:------------:| | NeverZero | x | x(1) | (2) | x | x | | Persistent mode | | x | x | x86[_64]/arm[64] | x | | LAF-Intel / CompCov | | x | | x86[_64]/arm[64] | x86[_64]/arm | | CmpLog | | x | | x86[_64]/arm[64] | | | Whitelist | | x | x | (x)(3) | | | Non-colliding coverage | | x(4) | | (x)(5) | | | InsTrim | | x | | | | | Ngram prev_loc coverage | | x(6) | | | | | Context coverage | | x | | | | | Snapshot LKM support | | x | | (x)(5) | | neverZero: (1) default for LLVM >= 9.0, env var for older version due an efficiency bug in llvm <= 8 (2) GCC creates non-performant code, hence it is disabled in gcc_plugin (3) partially via AFL_CODE_START/AFL_CODE_END (4) Only for LLVM >= 9 and not all targets compile (5) upcoming, development in the branch (6) not compatible with LTO and InsTrim and needs at least LLVM >= 4.1 So all in all this is the best-of afl that is currently out there :-) For new versions and additional information, check out: [https://github.com/AFLplusplus/AFLplusplus](https://github.com/AFLplusplus/AFLplusplus) To compare notes with other users or get notified about major new features, send a mail to <afl-users+subscribe@googlegroups.com>. See [docs/QuickStartGuide.md](docs/QuickStartGuide.md) if you don't have time to read this file. ## Branches The following branches exist: * [master/trunk](https://github.com/AFLplusplus/AFLplusplus/) : stable state of afl++ - it is synced from dev from time to time when we are satisfied with it's stability * [dev](https://github.com/AFLplusplus/AFLplusplus/tree/dev) : development state of afl++ - bleeding edge and you might catch a checkout which does not compile or has a bug. *We only accept PRs in dev!!* * (any other) : experimental branches to work on specific features or testing new functionality or changes. For releases, please see the [Releases](https://github.com/AFLplusplus/AFLplusplus/releases) tab. ## Google Summer of Code 2020 (and any other students and enthusiast developers) We are happy to be part of [Google Summer of Code 2020](https://summerofcode.withgoogle.com/organizations/5100744400699392/)! :-) We have several ideas we would like to see in AFL++ to make it even better. However, we already work on so many things that we do not have the time for all the big ideas. This can be your way to support and contribute to AFL++ - extend it to something cool. We have an idea list in [docs/ideas.md](docs/ideas.md). For everyone who wants to contribute (and send pull requests) please read [CONTRIBUTING.md](CONTRIBUTING.md) before your submit. ## Building and installing afl++ afl++ has many build options. The easiest is to build and install everything: ```shell $ sudo apt install build-essential libtool-bin python3 automake bison libglib2.0-dev libpixman-1-dev clang python-setuptools $ make distrib $ sudo make install ``` Note that "make distrib" also builds llvm_mode, qemu_mode, unicorn_mode and more. If you just want plain afl then do "make all", however compiling and using at least llvm_mode is highly recommended for much better results - hence in this case ```shell $ make source-only ``` is what you should choose. These build targets exist: * all: just the main afl++ binaries * binary-only: everything for binary-only fuzzing: qemu_mode, unicorn_mode, libdislocator, libtokencap, radamsa * source-only: everything for source code fuzzing: llvm_mode, libdislocator, libtokencap, radamsa * distrib: everything (for both binary-only and source code fuzzing) * man: creates simple man pages from the help option of the programs * install: installs everything you have compiled with the build options above * clean: cleans everything. for qemu_mode and unicorn_mode it means it deletes all downloads as well * code-format: format the code, do this before you commit and send a PR please! * tests: runs test cases to ensure that all features are still working as they should * unit: perform unit tests (based on cmocka) * help: shows these build options [Unless you are on Mac OS X](https://developer.apple.com/library/archive/qa/qa1118/_index.html) you can also build statically linked versions of the afl++ binaries by passing the STATIC=1 argument to make: ```shell $ make all STATIC=1 ``` These build options exist: * STATIC - compile AFL++ static * ASAN_BUILD - compiles with memory sanitizer for debug purposes * PROFILING - compile with profiling information (gprof) * AFL_NO_X86 - if compiling on non-intel/amd platforms * LLVM_CONFIG - if your distro doesn't use the standard name for llvm-config (e.g. Debian) e.g.: make ASAN_BUILD=1 Note that afl++ is faster and better the newer the compilers used are. Hence at least gcc-9 and especially llvm-9 should be the compilers of choice. If your distribution does not have them, you can use the Dockerfile: ```shell $ cd AFLplusplus $ sudo docker build -t aflplusplus . ``` ## Challenges of guided fuzzing Fuzzing is one of the most powerful and proven strategies for identifying security issues in real-world software; it is responsible for the vast majority of remote code execution and privilege escalation bugs found to date in security-critical software. Unfortunately, fuzzing is also relatively shallow; blind, random mutations make it very unlikely to reach certain code paths in the tested code, leaving some vulnerabilities firmly outside the reach of this technique. There have been numerous attempts to solve this problem. One of the early approaches - pioneered by Tavis Ormandy - is corpus distillation. The method relies on coverage signals to select a subset of interesting seeds from a massive, high-quality corpus of candidate files, and then fuzz them by traditional means. The approach works exceptionally well but requires such a corpus to be readily available. In addition, block coverage measurements provide only a very simplistic understanding of the program state and are less useful for guiding the fuzzing effort in the long haul. Other, more sophisticated research has focused on techniques such as program flow analysis ("concolic execution"), symbolic execution, or static analysis. All these methods are extremely promising in experimental settings, but tend to suffer from reliability and performance problems in practical uses - and currently do not offer a viable alternative to "dumb" fuzzing techniques. ## The afl-fuzz approach American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple but rock-solid instrumentation-guided genetic algorithm. It uses a modified form of edge coverage to effortlessly pick up subtle, local-scale changes to program control flow. Simplifying a bit, the overall algorithm can be summed up as: 1) Load user-supplied initial test cases into the queue, 2) Take the next input file from the queue, 3) Attempt to trim the test case to the smallest size that doesn't alter the measured behavior of the program, 4) Repeatedly mutate the file using a balanced and well-researched variety of traditional fuzzing strategies, 5) If any of the generated mutations resulted in a new state transition recorded by the instrumentation, add mutated output as a new entry in the queue. 6) Go to 2. The discovered test cases are also periodically culled to eliminate ones that have been obsoleted by newer, higher-coverage finds; and undergo several other instrumentation-driven effort minimization steps. As a side result of the fuzzing process, the tool creates a small, self-contained corpus of interesting test cases. These are extremely useful for seeding other, labor- or resource-intensive testing regimes - for example, for stress-testing browsers, office applications, graphics suites, or closed-source tools. The fuzzer is thoroughly tested to deliver out-of-the-box performance far superior to blind fuzzing or coverage-only tools. ## Instrumenting programs for use with AFL PLEASE NOTE: llvm_mode compilation with afl-clang-fast/afl-clang-fast++ instead of afl-gcc/afl-g++ is much faster and has many cool features. See llvm_mode/ - however few code does not compile with llvm. We support llvm versions 3.8.0 to 11. When source code is available, instrumentation can be injected by a companion tool that works as a drop-in replacement for gcc or clang in any standard build process for third-party code. The instrumentation has a fairly modest performance impact; in conjunction with other optimizations implemented by afl-fuzz, most programs can be fuzzed as fast or even faster than possible with traditional tools. The correct way to recompile the target program may vary depending on the specifics of the build process, but a nearly-universal approach would be: ```shell $ CC=/path/to/afl/afl-gcc ./configure $ make clean all ``` For C++ programs, you'd would also want to set `CXX=/path/to/afl/afl-g++`. The clang wrappers (afl-clang and afl-clang++) can be used in the same way; clang users may also opt to leverage a higher-performance instrumentation mode, as described in [llvm_mode/README.md](llvm_mode/README.md). Clang/LLVM has a much better performance and works with LLVM version 3.8.0 to 11. Using the LAF Intel performance enhancements are also recommended, see [llvm_mode/README.laf-intel.md](llvm_mode/README.laf-intel.md) Using partial instrumentation is also recommended, see [llvm_mode/README.whitelist.md](llvm_mode/README.whitelist.md) When testing libraries, you need to find or write a simple program that reads data from stdin or from a file and passes it to the tested library. In such a case, it is essential to link this executable against a static version of the instrumented library or to make sure that the correct .so file is loaded at runtime (usually by setting `LD_LIBRARY_PATH`). The simplest option is a static build, usually possible via: ```shell $ CC=/path/to/afl/afl-gcc ./configure --disable-shared ``` Setting `AFL_HARDEN=1` when calling 'make' will cause the CC wrapper to automatically enable code hardening options that make it easier to detect simple memory bugs. Libdislocator, a helper library included with AFL (see [libdislocator/README.md](libdislocator/README.md)) can help uncover heap corruption issues, too. PS. ASAN users are advised to review [docs/notes_for_asan.md](docs/notes_for_asan.md) file for important caveats. ## Instrumenting binary-only apps When source code is *NOT* available, the fuzzer offers experimental support for fast, on-the-fly instrumentation of black-box binaries. This is accomplished with a version of QEMU running in the lesser-known "user space emulation" mode. QEMU is a project separate from AFL, but you can conveniently build the feature by doing: ```shell $ cd qemu_mode $ ./build_qemu_support.sh ``` For additional instructions and caveats, see [qemu_mode/README.md](qemu_mode/README.md). If possible you should use the persistent mode, see [qemu_mode/README.persistent.md](qemu_mode/README.persistent.md). The mode is approximately 2-5x slower than compile-time instrumentation, is less conducive to parallelization, and may have some other quirks. If [afl-dyninst](https://github.com/vanhauser-thc/afl-dyninst) works for your binary, then you can use afl-fuzz normally and it will have twice the speed compared to qemu_mode. A more comprehensive description of these and other options can be found in [docs/binaryonly_fuzzing.md](docs/binaryonly_fuzzing.md) ## Good examples and writeups Here are some good writeups to show how to effectively use AFL++: * [https://aflplus.plus/docs/tutorials/libxml2_tutorial/](https://aflplus.plus/docs/tutorials/libxml2_tutorial/) * [https://bananamafia.dev/post/gb-fuzz/](https://bananamafia.dev/post/gb-fuzz/) * [https://securitylab.github.com/research/fuzzing-challenges-solutions-1](https://securitylab.github.com/research/fuzzing-challenges-solutions-1) If you are interested in fuzzing structured data (where you define what the structure is), these two links have you covered: * [https://github.com/bruce30262/libprotobuf-mutator_fuzzing_learning/tree/master/4_libprotobuf_aflpp_custom_mutator](https://github.com/bruce30262/libprotobuf-mutator_fuzzing_learning/tree/master/4_libprotobuf_aflpp_custom_mutator) * [https://github.com/thebabush/afl-libprotobuf-mutator](https://github.com/thebabush/afl-libprotobuf-mutator) If you find other good ones, please send them to us :-) ## Power schedules The power schedules were copied from Marcel Böhme's excellent AFLfast implementation and expand on the ability to discover new paths and therefore may increase the code coverage. The available schedules are: - explore (default) - fast - coe - quad - lin - exploit - mmopt (experimental) - rare (experimental) In parallel mode (-M/-S, several instances with the shared queue), we suggest to run the master using the explore or fast schedule (-p explore) and the slaves with a combination of cut-off-exponential (-p coe), exponential (-p fast), explore (-p explore) and mmopt (-p mmopt) schedules. If a schedule does not perform well for a target, restart the slave with a different schedule. In single mode, using -p fast is usually slightly more beneficial than the default explore mode. (We don't want to change the default behavior of afl, so "fast" has not been made the default mode). More details can be found in the paper published at the 23rd ACM Conference on Computer and Communications Security [CCS'16](https://www.sigsac.org/ccs/CCS2016/accepted-papers/) ## Choosing initial test cases To operate correctly, the fuzzer requires one or more starting file that contains a good example of the input data normally expected by the targeted application. There are two basic rules: - Keep the files small. Under 1 kB is ideal, although not strictly necessary. For a discussion of why size matters, see [perf_tips.md](docs/perf_tips.md). - Use multiple test cases only if they are functionally different from each other. There is no point in using fifty different vacation photos to fuzz an image library. You can find many good examples of starting files in the testcases/ subdirectory that comes with this tool. PS. If a large corpus of data is available for screening, you may want to use the afl-cmin utility to identify a subset of functionally distinct files that exercise different code paths in the target binary. ## Fuzzing binaries The fuzzing process itself is carried out by the afl-fuzz utility. This program requires a read-only directory with initial test cases, a separate place to store its findings, plus a path to the binary to test. For target binaries that accept input directly from stdin, the usual syntax is: ```shell $ ./afl-fuzz -i testcase_dir -o findings_dir /path/to/program [...params...] ``` For programs that take input from a file, use '@@' to mark the location in the target's command line where the input file name should be placed. The fuzzer will substitute this for you: ```shell $ ./afl-fuzz -i testcase_dir -o findings_dir /path/to/program @@ ``` You can also use the -f option to have the mutated data written to a specific file. This is useful if the program expects a particular file extension or so. Non-instrumented binaries can be fuzzed in the QEMU mode (add -Q in the command line) or in a traditional, blind-fuzzer mode (specify -n). You can use -t and -m to override the default timeout and memory limit for the executed process; rare examples of targets that may need these settings touched include compilers and video decoders. Tips for optimizing fuzzing performance are discussed in [perf_tips.md](docs/perf_tips.md). Note that afl-fuzz starts by performing an array of deterministic fuzzing steps, which can take several days, but tend to produce neat test cases. If you want quick & dirty results right away - akin to zzuf and other traditional fuzzers - add the -d option to the command line. ## Interpreting output See the [docs/status_screen.md](docs/status_screen.md) file for information on how to interpret the displayed stats and monitor the health of the process. Be sure to consult this file especially if any UI elements are highlighted in red. The fuzzing process will continue until you press Ctrl-C. At a minimum, you want to allow the fuzzer to complete one queue cycle, which may take anywhere from a couple of hours to a week or so. There are three subdirectories created within the output directory and updated in real-time: - queue/ - test cases for every distinctive execution path, plus all the starting files given by the user. This is the synthesized corpus mentioned in section 2. Before using this corpus for any other purposes, you can shrink it to a smaller size using the afl-cmin tool. The tool will find a smaller subset of files offering equivalent edge coverage. - crashes/ - unique test cases that cause the tested program to receive a fatal signal (e.g., SIGSEGV, SIGILL, SIGABRT). The entries are grouped by the received signal. - hangs/ - unique test cases that cause the tested program to time out. The default time limit before something is classified as a hang is the larger of 1 second and the value of the -t parameter. The value can be fine-tuned by setting AFL_HANG_TMOUT, but this is rarely necessary. Crashes and hangs are considered "unique" if the associated execution paths involve any state transitions not seen in previously-recorded faults. If a single bug can be reached in multiple ways, there will be some count inflation early in the process, but this should quickly taper off. The file names for crashes and hangs are correlated with the parent, non-faulting queue entries. This should help with debugging. When you can't reproduce a crash found by afl-fuzz, the most likely cause is that you are not setting the same memory limit as used by the tool. Try: ```shell $ LIMIT_MB=50 $ ( ulimit -Sv $[LIMIT_MB << 10]; /path/to/tested_binary ... ) ``` Change LIMIT_MB to match the -m parameter passed to afl-fuzz. On OpenBSD, also change -Sv to -Sd. Any existing output directory can be also used to resume aborted jobs; try: ```shell $ ./afl-fuzz -i- -o existing_output_dir [...etc...] ``` If you have gnuplot installed, you can also generate some pretty graphs for any active fuzzing task using afl-plot. For an example of how this looks like, see [http://lcamtuf.coredump.cx/afl/plot/](http://lcamtuf.coredump.cx/afl/plot/). ## Parallelized fuzzing Every instance of afl-fuzz takes up roughly one core. This means that on multi-core systems, parallelization is necessary to fully utilize the hardware. For tips on how to fuzz a common target on multiple cores or multiple networked machines, please refer to [docs/parallel_fuzzing.md](docs/parallel_fuzzing.md). The parallel fuzzing mode also offers a simple way for interfacing AFL to other fuzzers, to symbolic or concolic execution engines, and so forth; again, see the last section of [docs/parallel_fuzzing.md](docs/parallel_fuzzing.md) for tips. ## Fuzzer dictionaries By default, afl-fuzz mutation engine is optimized for compact data formats - say, images, multimedia, compressed data, regular expression syntax, or shell scripts. It is somewhat less suited for languages with particularly verbose and redundant verbiage - notably including HTML, SQL, or JavaScript. To avoid the hassle of building syntax-aware tools, afl-fuzz provides a way to seed the fuzzing process with an optional dictionary of language keywords, magic headers, or other special tokens associated with the targeted data type -- and use that to reconstruct the underlying grammar on the go: [http://lcamtuf.blogspot.com/2015/01/afl-fuzz-making-up-grammar-with.html](http://lcamtuf.blogspot.com/2015/01/afl-fuzz-making-up-grammar-with.html) To use this feature, you first need to create a dictionary in one of the two formats discussed in [dictionaries/README.md](dictionaries/README.md); and then point the fuzzer to it via the -x option in the command line. (Several common dictionaries are already provided in that subdirectory, too.) There is no way to provide more structured descriptions of the underlying syntax, but the fuzzer will likely figure out some of this based on the instrumentation feedback alone. This actually works in practice, say: [http://lcamtuf.blogspot.com/2015/04/finding-bugs-in-sqlite-easy-way.html](http://lcamtuf.blogspot.com/2015/04/finding-bugs-in-sqlite-easy-way.html) PS. Even when no explicit dictionary is given, afl-fuzz will try to extract existing syntax tokens in the input corpus by watching the instrumentation very closely during deterministic byte flips. This works for some types of parsers and grammars but isn't nearly as good as the -x mode. If a dictionary is really hard to come by, another option is to let AFL run for a while and then use the token capture library that comes as a companion utility with AFL. For that, see [libtokencap/README.md](libtokencap/README.tokencap.md). ## Crash triage The coverage-based grouping of crashes usually produces a small data set that can be quickly triaged manually or with a very simple GDB or Valgrind script. Every crash is also traceable to its parent non-crashing test case in the queue, making it easier to diagnose faults. Having said that, it's important to acknowledge that some fuzzing crashes can be difficult to quickly evaluate for exploitability without a lot of debugging and code analysis work. To assist with this task, afl-fuzz supports a very unique "crash exploration" mode enabled with the -C flag. In this mode, the fuzzer takes one or more crashing test cases as the input and uses its feedback-driven fuzzing strategies to very quickly enumerate all code paths that can be reached in the program while keeping it in the crashing state. Mutations that do not result in a crash are rejected; so are any changes that do not affect the execution path. The output is a small corpus of files that can be very rapidly examined to see what degree of control the attacker has over the faulting address, or whether it is possible to get past an initial out-of-bounds read - and see what lies beneath. Oh, one more thing: for test case minimization, give afl-tmin a try. The tool can be operated in a very simple way: ```shell $ ./afl-tmin -i test_case -o minimized_result -- /path/to/program [...] ``` The tool works with crashing and non-crashing test cases alike. In the crash mode, it will happily accept instrumented and non-instrumented binaries. In the non-crashing mode, the minimizer relies on standard AFL instrumentation to make the file simpler without altering the execution path. The minimizer accepts the -m, -t, -f and @@ syntax in a manner compatible with afl-fuzz. Another recent addition to AFL is the afl-analyze tool. It takes an input file, attempts to sequentially flip bytes, and observes the behavior of the tested program. It then color-codes the input based on which sections appear to be critical, and which are not; while not bulletproof, it can often offer quick insights into complex file formats. More info about its operation can be found near the end of [docs/technical_details.md](docs/technical_details.md). ## Going beyond crashes Fuzzing is a wonderful and underutilized technique for discovering non-crashing design and implementation errors, too. Quite a few interesting bugs have been found by modifying the target programs to call abort() when say: - Two bignum libraries produce different outputs when given the same fuzzer-generated input, - An image library produces different outputs when asked to decode the same input image several times in a row, - A serialization / deserialization library fails to produce stable outputs when iteratively serializing and deserializing fuzzer-supplied data, - A compression library produces an output inconsistent with the input file when asked to compress and then decompress a particular blob. Implementing these or similar sanity checks usually takes very little time; if you are the maintainer of a particular package, you can make this code conditional with `#ifdef FUZZING_BUILD_MODE_UNSAFE_FOR_PRODUCTION` (a flag also shared with libfuzzer) or `#ifdef __AFL_COMPILER` (this one is just for AFL). ## Common-sense risks Please keep in mind that, similarly to many other computationally-intensive tasks, fuzzing may put a strain on your hardware and on the OS. In particular: - Your CPU will run hot and will need adequate cooling. In most cases, if cooling is insufficient or stops working properly, CPU speeds will be automatically throttled. That said, especially when fuzzing on less suitable hardware (laptops, smartphones, etc), it's not entirely impossible for something to blow up. - Targeted programs may end up erratically grabbing gigabytes of memory or filling up disk space with junk files. AFL tries to enforce basic memory limits, but can't prevent each and every possible mishap. The bottom line is that you shouldn't be fuzzing on systems where the prospect of data loss is not an acceptable risk. - Fuzzing involves billions of reads and writes to the filesystem. On modern systems, this will be usually heavily cached, resulting in fairly modest "physical" I/O - but there are many factors that may alter this equation. It is your responsibility to monitor for potential trouble; with very heavy I/O, the lifespan of many HDDs and SSDs may be reduced. A good way to monitor disk I/O on Linux is the 'iostat' command: ```shell $ iostat -d 3 -x -k [...optional disk ID...] ``` ## Known limitations & areas for improvement Here are some of the most important caveats for AFL: - AFL detects faults by checking for the first spawned process dying due to a signal (SIGSEGV, SIGABRT, etc). Programs that install custom handlers for these signals may need to have the relevant code commented out. In the same vein, faults in child processes spawned by the fuzzed target may evade detection unless you manually add some code to catch that. - As with any other brute-force tool, the fuzzer offers limited coverage if encryption, checksums, cryptographic signatures, or compression are used to wholly wrap the actual data format to be tested. To work around this, you can comment out the relevant checks (see examples/libpng_no_checksum/ for inspiration); if this is not possible, you can also write a postprocessor, as explained in examples/post_library/ (with AFL_POST_LIBRARY) - There are some unfortunate trade-offs with ASAN and 64-bit binaries. This isn't due to any specific fault of afl-fuzz; see [docs/notes_for_asan.md](docs/notes_for_asan.md) for tips. - There is no direct support for fuzzing network services, background daemons, or interactive apps that require UI interaction to work. You may need to make simple code changes to make them behave in a more traditional way. Preeny may offer a relatively simple option, too - see: [https://github.com/zardus/preeny](https://github.com/zardus/preeny) Some useful tips for modifying network-based services can be also found at: [https://www.fastly.com/blog/how-to-fuzz-server-american-fuzzy-lop](https://www.fastly.com/blog/how-to-fuzz-server-american-fuzzy-lop) - AFL doesn't output human-readable coverage data. If you want to monitor coverage, use afl-cov from Michael Rash: [https://github.com/mrash/afl-cov](https://github.com/mrash/afl-cov) - Occasionally, sentient machines rise against their creators. If this happens to you, please consult [http://lcamtuf.coredump.cx/prep/](http://lcamtuf.coredump.cx/prep/). Beyond this, see INSTALL for platform-specific tips. ## Special thanks Many of the improvements to the original afl and afl++ wouldn't be possible without feedback, bug reports, or patches from: ``` Jann Horn Hanno Boeck Felix Groebert Jakub Wilk Richard W. M. Jones Alexander Cherepanov Tom Ritter Hovik Manucharyan Sebastian Roschke Eberhard Mattes Padraig Brady Ben Laurie @dronesec Luca Barbato Tobias Ospelt Thomas Jarosch Martin Carpenter Mudge Zatko Joe Zbiciak Ryan Govostes Michael Rash William Robinet Jonathan Gray Filipe Cabecinhas Nico Weber Jodie Cunningham Andrew Griffiths Parker Thompson Jonathan Neuschaefer Tyler Nighswander Ben Nagy Samir Aguiar Aidan Thornton Aleksandar Nikolich Sam Hakim Laszlo Szekeres David A. Wheeler Turo Lamminen Andreas Stieger Richard Godbee Louis Dassy teor2345 Alex Moneger Dmitry Vyukov Keegan McAllister Kostya Serebryany Richo Healey Martijn Bogaard rc0r Jonathan Foote Christian Holler Dominique Pelle Jacek Wielemborek Leo Barnes Jeremy Barnes Jeff Trull Guillaume Endignoux ilovezfs Daniel Godas-Lopez Franjo Ivancic Austin Seipp Daniel Komaromy Daniel Binderman Jonathan Metzman Vegard Nossum Jan Kneschke Kurt Roeckx Marcel Boehme Van-Thuan Pham Abhik Roychoudhury Joshua J. Drake Toby Hutton Rene Freingruber Sergey Davidoff Sami Liedes Craig Young Andrzej Jackowski Daniel Hodson Nathan Voss Dominik Maier Andrea Biondo Vincent Le Garrec Khaled Yakdan Kuang-che Wu Josephine Calliotte Konrad Welc ``` Thank you! (For people sending pull requests - please add yourself to this list :-) ## Contact Questions? Concerns? Bug reports? The contributors can be reached via [https://github.com/AFLplusplus/AFLplusplus](https://github.com/AFLplusplus/AFLplusplus) There is also a mailing list for the afl project; to join, send a mail to <afl-users+subscribe@googlegroups.com>. Or, if you prefer to browse archives first, try: [https://groups.google.com/group/afl-users](https://groups.google.com/group/afl-users)
