libwebp VS NumPy

The thing that concerns me most is looking at the fix it is very difficult to see why this fix is correct. It also appears as there is lots of code without explicit bounds checks. It makes me worried because while the logic may be safe this makes the logic very complex. I wonder what the cost would be to add an explicit, local bounds check at every array access. This would serve as a backup that is much easier to verify. I suspect the cost would be relatively small. Small enough that I personally would be happy to pay it.

https://github.com/webmproject/libwebp/commit/902bc919033134...

This is also a great reminded that fuzzing isn't a solution to memory unsafe languages and libraries. If anything the massive amount of bugs found via fuzzing should scare us as it is likely only scratching the surface of the vulnerabilities that still lie in the code, a couple too many branches away from being likely to be found by fuzzing.

There's a follow-up fix, according to Debian[0]: https://github.com/webmproject/libwebp/commit/95ea5226c87044...

[0]: https://security-tracker.debian.org/tracker/CVE-2023-4863

The breakage [0] was introduced by the creator [1] of the project. If you want to audit 1674 commits over the past 12 years, it'd be easier to just audit the full project.

[0] https://github.com/webmproject/libwebp/commit/21735e06f7c1cb...

[1] https://github.com/webmproject/libwebp/commit/c3f41cb47e5f32...

If you like the command line, then you can use ffmpeg and ImageMagick, or use libwebp directly

The webp parser code is open source. Which means that even if Google decides to hide/obscure the code for webp, they'd legally not be allowed to prevent you from using older versions of the webp parser library. The only thing they could do is patent it, and then companies in the US (which has software patents, unfortunately) would have to pay royalties to decode it anyway; but here comes the next point

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]