tinyaes 1.0.2rc1

tiny-AES-c wrapper in Cython

tiny-AES-c Cython wrapper

tinyaes is a few lines Cython wrapper for the tiny-AES-c library, a Small portable AES128/192/256 in C.

The library offers a few modes, CTR mode is the only one currently wrapped. Given the C API works modifying a buffer in-place, the wrapper offers:

• CTR_xcrypt_buffer(..) that works on all bytes convertible types, and encrypting a copy of the buffer,
• CTR_xcrypt_buffer_inplace(..) that works on bytearrays only, modifying the buffer in-place.

Release notes

• 1.0.2rc1 (Apr 7, 2021):
  • added release Python 3.9 on Windows, Linux (manylinux1) and OSX
  • updated upstream tiny-AES-c with some cleanups and small optimizations
• 1.0.1 (Jun 8, 2020):
  • release Python 3.6 OSX and Windows wheels
  • updated upstream tiny-AES-c with some code changes
• 1.0.0 (Feb 20, 2020): updated readme (no code changes)
• 1.0.0a3 (Feb 7, 2020): fix bytes in-place mutation error
• 1.0.0a2 (Jan 29, 2020): first public release

Like to help?

The CI is up and running, but on Linux only, running a minimal test suite that uses hypothesis, and that allowed me to find a first bug, a missed variable replacement that had nefarious consequences.

The source package released on PyPI should be usable on Windows and MacOS too, just pip install tinyaes.

The development instead is Linux centered, without any guide yet, but the CI script can be a guide.

TL;DR

• Download Just and put it in your PATH.
• just test should install the library and the dependencies and run the tests using your default Python version.
• Inspect the justfile for some hints about what happens.

Thanks

The library is very minimal, but nonetheless, it uses a lot of existing software. I'd like to thank:

• Cython developer for their wonderful "product", both the library and the documentation.

• Kudos to kokke for their tiny-AES-c library, very minimal and easy to build and wrap for any usage that needs only the few AES modes it exposes.

• Just developers for their automation tool, I use in most of my projects.

• A huge thank to all the hypothesis authors to their fantastic library, that helped me to find an miss-named variable bug that I worked very hard to add in a 6 lines of code wrapper! And to this Data-driven testing with Python article that had left me with the desire to try the library.