# Custom Mutators in AFL++ This file describes how you can implement custom mutations to be used in AFL. For now, we support C/C++ library and Python module, collectivelly named as the custom mutator. Implemented by - C/C++ library (`*.so`): Khaled Yakdan from Code Intelligence () - Python module: Christian Holler from Mozilla () ## 1) Introduction Custom mutators can be passed to `afl-fuzz` to perform custom mutations on test cases beyond those available in AFL. For example, to enable structure-aware fuzzing by using libraries that perform mutations according to a given grammar. The custom mutator is passed to `afl-fuzz` via the `AFL_CUSTOM_MUTATOR_LIBRARY` or `AFL_PYTHON_MODULE` environment variable, and must export a fuzz function. Now afl also supports multiple custom mutators which can be specified in the same `AFL_CUSTOM_MUTATOR_LIBRARY` environment variable like this. ```bash export AFL_CUSTOM_MUTATOR_LIBRARY="full/path/to/mutator_first.so;full/path/to/mutator_second.so" ``` Please see [APIs](#2-apis) and [Usage](#3-usage) for detail. The custom mutation stage is set to be the first non-deterministic stage (right before the havoc stage). Note: If `AFL_CUSTOM_MUTATOR_ONLY` is set, all mutations will solely be performed with the custom mutator. ## 2) APIs C/C++: ```c void *afl_custom_init(afl_t *afl, unsigned int seed); uint32_t afl_custom_fuzz_count(void *data, const u8 *buf, size_t buf_size); size_t afl_custom_fuzz(void *data, uint8_t *buf, size_t buf_size, u8 **out_buf, uint8_t *add_buf, size_t add_buf_size, size_t max_size); size_t afl_custom_post_process(void *data, uint8_t *buf, size_t buf_size, uint8_t **out_buf); int32_t afl_custom_init_trim(void *data, uint8_t *buf, size_t buf_size); size_t afl_custom_trim(void *data, uint8_t **out_buf); int32_t afl_custom_post_trim(void *data, int success); size_t afl_custom_havoc_mutation(void *data, u8 *buf, size_t buf_size, u8 **out_buf, size_t max_size); uint8_t afl_custom_havoc_mutation_probability(void *data); uint8_t afl_custom_queue_get(void *data, const uint8_t *filename); void afl_custom_queue_new_entry(void *data, const uint8_t *filename_new_queue, const uint8_t *filename_orig_queue); void afl_custom_deinit(void *data); ``` Python: ```python def init(seed): pass def fuzz_count(buf, add_buf, max_size): return cnt def fuzz(buf, add_buf, max_size): return mutated_out def post_process(buf): return out_buf def init_trim(buf): return cnt def trim(): return out_buf def post_trim(success): return next_index def havoc_mutation(buf, max_size): return mutated_out def havoc_mutation_probability(): return probability # int in [0, 100] def queue_get(filename): return True def queue_new_entry(filename_new_queue, filename_orig_queue): pass ``` ### Custom Mutation - `init`: This method is called when AFL++ starts up and is used to seed RNG and set up buffers and state. - `queue_get` (optional): This method determines whether the custom fuzzer should fuzz the current queue entry or not - `fuzz_count` (optional): When a queue entry is selected to be fuzzed, afl-fuzz selects the number of fuzzing attempts with this input based on a few factors. If however the custom mutator wants to set this number instead on how often it is called for a specific queue entry, use this function. This function in mostly useful if **not** `AFL_CUSTOM_MUTATOR_ONLY` is used. - `fuzz` (optional): This method performs custom mutations on a given input. It also accepts an additional test case. Note that this function is optional - but it makes sense to use it. You would only skip this if `post_process` is used to fix checksums etc. so you are using it e.g. as a post processing library. - `havoc_mutation` and `havoc_mutation_probability` (optional): `havoc_mutation` performs a single custom mutation on a given input. This mutation is stacked with the other mutations in havoc. The other method, `havoc_mutation_probability`, returns the probability that `havoc_mutation` is called in havoc. By default, it is 6%. - `post_process` (optional): For some cases, the format of the mutated data returned from the custom mutator is not suitable to directly execute the target with this input. For example, when using libprotobuf-mutator, the data returned is in a protobuf format which corresponds to a given grammar. In order to execute the target, the protobuf data must be converted to the plain-text format expected by the target. In such scenarios, the user can define the `post_process` function. This function is then transforming the data into the format expected by the API before executing the target. - `queue_new_entry` (optional): This methods is called after adding a new test case to the queue. - `deinit`: The last method to be called, deinitializing the state. Note that there are also three functions for trimming as described in the next section. ### Trimming Support The generic trimming routines implemented in AFL++ can easily destroy the structure of complex formats, possibly leading to a point where you have a lot of test cases in the queue that your Python module cannot process anymore but your target application still accepts. This is especially the case when your target can process a part of the input (causing coverage) and then errors out on the remaining input. In such cases, it makes sense to implement a custom trimming routine. The API consists of multiple methods because after each trimming step, we have to go back into the C code to check if the coverage bitmap is still the same for the trimmed input. Here's a quick API description: - `init_trim` (optional): This method is called at the start of each trimming operation and receives the initial buffer. It should return the amount of iteration steps possible on this input (e.g. if your input has n elements and you want to remove them one by one, return n, if you do a binary search, return log(n), and so on). If your trimming algorithm doesn't allow you to determine the amount of (remaining) steps easily (esp. while running), then you can alternatively return 1 here and always return 0 in `post_trim` until you are finished and no steps remain. In that case, returning 1 in `post_trim` will end the trimming routine. The whole current index/max iterations stuff is only used to show progress. - `trim` (optional) This method is called for each trimming operation. It doesn't have any arguments because we already have the initial buffer from `init_trim` and we can memorize the current state in the data variables. This can also save reparsing steps for each iteration. It should return the trimmed input buffer, where the returned data must not exceed the initial input data in length. Returning anything that is larger than the original data (passed to `init_trim`) will result in a fatal abort of AFL++. - `post_trim` (optional) This method is called after each trim operation to inform you if your trimming step was successful or not (in terms of coverage). If you receive a failure here, you should reset your input to the last known good state. In any case, this method must return the next trim iteration index (from 0 to the maximum amount of steps you returned in `init_trim`). Omitting any of three trimming methods will cause the trimming to be disabled and trigger a fallback to the builtin default trimming routine. ### Environment Variables Optionally, the following environment variables are supported: - `AFL_CUSTOM_MUTATOR_ONLY` Disable all other mutation stages. This can prevent broken testcases (those that your Python module can't work with anymore) to fill up your queue. Best combined with a custom trimming routine (see below) because trimming can cause the same test breakage like havoc and splice. - `AFL_PYTHON_ONLY` Deprecated and removed, use `AFL_CUSTOM_MUTATOR_ONLY` instead trimming can cause the same test breakage like havoc and splice. - `AFL_DEBUG` When combined with `AFL_NO_UI`, this causes the C trimming code to emit additional messages about the performance and actions of your custom trimmer. Use this to see if it works :) ## 3) Usage ### Prerequisite For Python mutator, the python 3 or 2 development package is required. On Debian/Ubuntu/Kali this can be done: ```bash sudo apt install python3-dev # or sudo apt install python-dev ``` Then, AFL++ can be compiled with Python support. The AFL++ Makefile detects Python 2 and 3 through `python-config` if it is in the PATH and compiles `afl-fuzz` with the feature if available. Note: for some distributions, you might also need the package `python[23]-apt`. In case your setup is different, set the necessary variables like this: `PYTHON_INCLUDE=/path/to/python/include LDFLAGS=-L/path/to/python/lib make`. ### Custom Mutator Preparation For C/C++ mutator, the source code must be compiled as a shared object: ```bash gcc -shared -Wall -O3 example.c -o example.so ``` Note that if you specify multiple custom mutators, the corresponding functions will be called in the order in which they are specified. e.g first `post_process` function of `example_first.so` will be called and then that of `example_second.so` ### Run C/C++ ```bash export AFL_CUSTOM_MUTATOR_LIBRARY="/full/path/to/example_first.so;/full/path/to/example_second.so" afl-fuzz /path/to/program ``` Python ```bash export PYTHONPATH=`dirname /full/path/to/example.py` export AFL_PYTHON_MODULE=example afl-fuzz /path/to/program ``` ## 4) Example Please see [example.c](../examples/custom_mutators/example.c) and [example.py](../examples/custom_mutators/example.py) ## 5) Other Resources - AFL libprotobuf mutator - [bruce30262/libprotobuf-mutator_fuzzing_learning](https://github.com/bruce30262/libprotobuf-mutator_fuzzing_learning/tree/master/4_libprotobuf_aflpp_custom_mutator) - [thebabush/afl-libprotobuf-mutator](https://github.com/thebabush/afl-libprotobuf-mutator) - [XML Fuzzing@NullCon 2017](https://www.agarri.fr/docs/XML_Fuzzing-NullCon2017-PUBLIC.pdf) - [A bug detected by AFL + XML-aware mutators](https://bugs.chromium.org/p/chromium/issues/detail?id=930663)