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https://github.com/risicle/cpytraceafl

CPython bytecode instrumentation and forkserver tools for fuzzing pure python and mixed python/c code using AFL
https://github.com/risicle/cpytraceafl

afl-fuzz bytecode-manipulation coverage cpython python tracing

Last synced: 2 months ago
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CPython bytecode instrumentation and forkserver tools for fuzzing pure python and mixed python/c code using AFL

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# cpytraceafl



CPython bytecode instrumentation and forkserver tools for fuzzing python code using AFL.



The tools in this repository enable coverage-guided fuzzing of pure python and mixed python/c

code using [American Fuzzy Lop](https://github.com/google/AFL) (even better,

[AFL++](https://github.com/vanhauser-thc/AFLplusplus)).



There are three main parts to this:



 - A bytecode rewriter using a technique inspired by inspired by Ned Batchelder's "wicked hack"

   detailed at https://nedbatchelder.com/blog/200804/wicked_hack_python_bytecode_tracing.html.

   In this case, the rewriter identifies "basic blocks" in the python bytecode and abuses the

   `code` object's `lnotab` (line-number table) to mark each basic block as a new "line".

   These new "lines" are what trigger CPython's line-level trace hooks. The result of this being

   that we can get our trace hook executed on every new basic block.

 - A minimal & fast tracehook written in C, tallying visited locations to sysv shared memory.

 - A basic forkserver implementation.



Preparing code for fuzzing involves a couple of steps. The first thing that should happen in

the python process is a call to `install_rewriter()`. It's important that this is done very

early as any modules that are imported before this will not be properly instrumented.



```python

from cpytraceafl.rewriter import install_rewriter



install_rewriter()

```



`install_rewriter()` can optionally be provided with a `selector` controlling which code objects

are instrumented and to what degree.



Following this, modules can be imported as normal and will be instrumented by the monkeypatched

`compile` functions. It's usually a good idea to initialize the test environment next, 

performing as many setup procedures as possible before the input file is read. This may

include doing an initial run of the function under test to ensure any internal imports or caches

are set up. This is because we want to minimize work that has to be done post-fork - any work

done now only has to be done once,



After calling



```python

from cpytraceafl import fuzz_from_here



fuzz_from_here()

```



the `fork()` will have been made and tracing started. You now simply read your input file and

call your function under test.



Examples for fuzzing some common packages are provided in [examples/](./examples/).



As for hooking this script up to AFL, I tend to use the included

[dummy-afl-qemu-trace](./dummy-afl-qemu-trace) shim script to fool AFL's QEmu mode into

communicating directly with the python process.



## Fuzzing mixed python/c code



As of version 0.4.0, `cpytraceafl` can gather trace information from C extension modules that

have been compiled with AFL instrumentation (e.g. using `llvm_mode`). This means that it can

be used to seamlessly fuzz projects which have a mix of python and C "speedups". This is

important not only because a lot of python format-parsing packages use this approach, but

because issues revealed in native code are far more likely to have security implications.



Including instrumented native code requires a little more care when preparing a target for

fuzzing. For instance, it's important to ensure the `cpytraceafl.tracehook` module has been

imported and it has had its `set_map_start(...)` function provided with a valid memory

area *before* any instrumented extension modules are loaded. This is because simply loading an

instrumented native module will cause it to attempt to log its execution trace somewhere.



The example [pillow_pcx_example.py](./examples/pillow_pcx_example.py) demonstrates a fuzzing

target taking the necessary precautions into account.



It's possible that you're _only_ interested in tracing the native code, using `cpytraceafl`

just as a driver, in which case you can omit the early `install_rewriter()` call and all

the weirdness involved with that.



## Regular expressions



[cpytraceafl-regex](https://github.com/risicle/cpytraceafl-regex) is a companion,

`re`-replacement regex implementation with added instrumentation that should aid AFL in

generating examples that pass regular expressions used in the target code, or

exercise them in interesting ways. Without this, AFL will just see regular expressions

as a black box that will act as a barrier to path exploration.



## Trophy cabinet



`cpytraceafl` has been used to find:



 - Pillow: [CVE-2020-10177](https://nvd.nist.gov/vuln/detail/CVE-2020-10177),

   [CVE-2020-10378](https://nvd.nist.gov/vuln/detail/CVE-2020-10378),

   [CVE-2020-10379](https://nvd.nist.gov/vuln/detail/CVE-2020-10379),

   [CVE-2020-10994](https://nvd.nist.gov/vuln/detail/CVE-2020-10994),

   [CVE-2020-11538](https://nvd.nist.gov/vuln/detail/CVE-2020-11538).

 - bsdiff4: [CVE-2020-15904](https://nvd.nist.gov/vuln/detail/CVE-2020-15904)

 - asyncpg: [CVE-2020-17446](https://nvd.nist.gov/vuln/detail/CVE-2020-17446)

 - clickhouse-driver: [CVE-2020-26759](https://nvd.nist.gov/vuln/detail/CVE-2020-26759)



## Q & A



### Is there any point in fuzzing python? Isn't it too slow?



Well, yes and no. My experience has been that fuzzing python code is simply "a bit different"

from fuzzing native code - you tend to be looking for different things. In terms of raw speed,

fuzzing python is certainly not fast, but iteration rates I tend to work with aren't completely

dissimilar to what I'm used to getting with AFL's Qemu mode (of course, no two fuzzing targets

are really directly comparable).



Because of the memory-safe nature of pure python code, it's also more uncommon for issues

uncovered through fuzzing to be security issues - logical flaws in parsing tend to lead to

unexpected/unhandled exceptions. So it's still a rather useful tool in simply looking for bugs.

It can be used, for example, to generate a corpus of example inputs for your test suite which

exercise a large amount of the code.



However, note that while *pure* python code may be memory safe, as soon as you start using

the C api, Cython, or even start playing with the `ctypes` module, it is *not*.



### Does basic block analysis make any sense for python code?



From a rigorous academic stance, and for some uses, possibly not - you've got to keep in mind

that half the bytecode instructions could result in calls out to more arbitrary python or

(uninstrumented) native code that could have arbitrary side effects. But for our needs it works

well enough (recall that AFL coverage analysis is robust to random instrumentation

sites being omitted through `AFL_INST_RATIO` or `AFL_INST_LIBS`).



### Doesn't abusing `lnotab` break python's debugging mechanisms?



Absolutely it does. Don't use instrumented programs to debug problematic cases - use it to

generate problematic inputs. Analyze them with instrumentation turned off.



### I'm getting `undefined symbol: __afl_area_ptr`



Looks like you're trying to import an (instrumented) native extension module before the

`cpytraceafl.tracehook` module has been loaded (which is what provides that symbol).



### I'm getting Segmentation Faults after importing an instrumented native module



You probably also need to provide `cpytraceafl.tracehook.set_map_start(...)` with a valid

writeable memory area before the import. Assuming you're not interested in the trace associated

with the import process, this can just be a dummy which you later discard. I'd recommend either

using an `mmap` object or `sysv_ipc.SharedMemory`. When `fuzz_from_here()` is called, this will

be replaced with right one.



It's also possible the instrumented module was built with a different AFL `MAP_SIZE_POW2` from

that in `cpytraceafl.MAP_SIZE_BITS`.



### Do I need a specially-built/instrumented version of cpython to use this?



No, you can use your normal distribution-installed python. If you're just looking at

fuzzing pure python, you don't need to even think about building any binaries with

funny compilers.



You may be interested in building c/c++/cython-based modules or their underlying native

libraries with instrumentation if that's what you're trying to fuzz, but I suspect using

a natively-instrumented _cpython_ would be quite complicated and extremely slow.



### Do you have any tips on detecting memory errors in cpython extensions?



I have tended to use `tcmalloc`'s debugging modes with `TCMALLOC_PAGE_FENCE` and

`TCMALLOC_PAGE_FENCE_NEVER_RECLAIM` enabled. In fact I have

[a fork](https://github.com/gperftools/gperftools/compare/master...risicle:ris-extras)

of `gperftools` containing some additional `tcmalloc` hacks I've found useful.



One problem with this of course is that much of cpython's  memory is allocated

using its own memory pool allocator, which is largely invisible to the `malloc`

implementation. So I've also got

[a patch for cpython](https://gist.github.com/risicle/12c6f20518807699d816b8cb4389b840)

which adds a very basic canary mechanism to its pool allocator (at the slight expense of

memory efficiency).