rizin VS SciPy

Just for the record, for nicer inspection of files with such debug information, including compressed sections, and debuginfod support, Rizin[1] can be used, since starting from the 0.7.0 release[2] all of those were added.

[1] https://rizin.re

[2] https://github.com/rizinorg/rizin/releases/tag/v0.7.0

Rizin[1] is also able to uplift native code to the new RzIL, which is based on the BAP Core Theory[2] and is essentially an extension of SMT theories of bitvectors, bitvector-indexed arrays of bitvectors and effects[3].

[1] https://rizin.re/

[2] https://binaryanalysisplatform.github.io/bap/api/master/bap-...

[3] https://github.com/rizinorg/rizin/blob/dev/doc/rzil.md

Everything Hiew can do, Rizin[1] can do too, and is completely free and open source[2] under LGPL3 license. Moreover, it supports more architectures, platforms, and file formats, as well as GUI in Qt - Cutter[3][4]. If something is missing in Rizin but presented in Hiew, please let us know by opening the issue with details.

[1] https://rizin.re

[2] https://github.com/rizinorg/rizin

[3] https://cutter.re

[4] https://github.com/rizinorg/cutter

This is precisely what we are trying to do at Rizin[1][2]. Though the primary goal of the tool/framework is static analysis. All that portability across OSes, their versions, platforms and architectures, etc is definitely hard. If anyone is interested in these subjects, all contributions are welcome. For example, check out our "RzDebug" label, marking debugging issues[3].

[1] https://rizin.re

[2] https://github.com/rizinorg/rizin

[3] https://github.com/rizinorg/rizin/labels/RzDebug

Okay, so, your comment about a "Dasher + Guitar Hero music theory/improvisation practice program" just sent me down a huge rabbit hole...

Well, rabbit hole(s) plural, I guess, most not directly related. :D

Largely because I made the "mistake" of looking at your HN profile & discovering you're also in NZ & we seem to have somewhat overlapping interests (and an affinity for "bacon" in account names, apparently), so, some thoughts[0]... :)

# Topic 1: Nissan Leaf VSP hacking

After reading your recent posts (https://ianrrees.github.io//2023/07/03/vsp-hacking.html & https://ianrrees.github.io//2023/08/05/voltage-glitch-inject...) on this topic & noting your remark about wanting to try reverse engineering a firmware image, I found the following thesis PDF (via a brief google search for `"reverse engineer" "firmware" "Renesas"`):

* "AUTOMOTIVE FIRMWARE EXTRACTION AND ANALYSIS TECHNIQUES" by Jan Van den Herrewegen https://etheses.bham.ac.uk/id/eprint/11516/1/VandenHerrewege...

Not really what I was anticipating finding but seems relevant to your interests--I don't think it was already in your resource list.

While the thesis addresses the Renesas 78K0 rather than the Renesas 78K0R, from a brief look at the "Flash Protection" PDF Application Note in your resource list it seems there's a large overlap.

Perhaps most significantly the author presents "novel methods" that combine bootloader binary analysis with constraint-based power glitching in an effort to improve on the results described in "Shaping the Glitch".

While I haven't read the entire 186 pages :D they theorize that using their approach extracting 8kB firmware might only take ~10 hours.

And, most helpfully, they even published their source code under the GPL here: https://github.com/janvdherrewegen/bootl-attacks

So, an interesting adjacent read even if it turns out not to be directly applicable to your situation.

Given I have an interest in & a little experience with firmware reversing my original thought was to maybe provide some hopefully helpful references that more generically related to firmware reversing but more specific is good too, I guess. :)

In terms of reverse engineering tooling, I've used Rizin/Cutter/radare2 previously: https://rizin.re https://cutter.re

On the CAN tooling/info front, you might be interested in taking a look at my "Adequate CAN" list which I originally wrote-up for a client a couple years ago: https://gitlab.com/RancidBacon/adequate-can

Some other probably outdated reverse engineering tooling links of mine: https://web.archive.org/web/20200119074540/http://www.labrad...

In terms of how to approach RE, other than just "getting started & digging in" & learning by doing, I've sometimes found it informative to read other people's firmware reverse engineering write-ups to learn about potentially useful approaches/tools.

Anyway, hopefully some of this is helpful!

[0] I have a tendency to be a little... "verbose" and/or "thorough" (depending on one's POV :) ) so I'll probably split this over a couple of comments, in case I run out of steam while writing and for topic separation.

See our FAQ[1] on why we forked. As three years passed and both projects are actively developed, the divergence has grown a lot since. We aim for exposing the proper API instead of relying just commands, see e.g. our new Python bindings and rz-bindgen[2]. We have completely different concept of projects, new intermediate language - RzIL[3], and many other things. And under the new organization Cutter is a first-class citizen, not an afterthought as before.

[1] https://rizin.re/posts/faq/

[2] https://rizin.re/posts/gsoc-2022-rz-bindgen/

[3] https://github.com/rizinorg/rizin/blob/dev/doc/rzil.md

I guess it is a rite of passage to rewrite it. I'm doing it for SciPy too together with Propack in [1]. Somebody already mentioned your repo. Thank you for your efforts.

[1]: https://github.com/scipy/scipy/issues/18566

Fortran codes have caused many problems for the Python package Scipy, and some of them are now being rewritten in C: e.g., https://github.com/scipy/scipy/pull/19121. Not only does R have many Fortran codes, there are also many R packages using Fortran codes: https://github.com/r-devel/r-svn, https://github.com/cran?q=&type=&language=fortran&sort=. Modern Fortran is a fine language but most legacy Fortran codes use the F77 style. When I update the R package quantreg, which uses many Fortran codes, I get a lot of warning messages. Not sure how the Fortran codes in the R ecosystem will be dealt with in the future, but they recently caused an issue in R due to the lack of compiler support for Fortran: https://blog.r-project.org/2023/08/23/will-r-work-on-64-bit-arm-windows/index.html. Some renowned packages like glmnet already have their Fortran codes rewritten in C/C++: https://cran.r-project.org/web/packages/glmnet/news/news.html

There are several lessons here: a) vanilla conda-forge numpy and scipy versions come with openblas, and it works pretty well, b) do not use netlib unless your matrices are small and you need to do a lot of SVDs, or idek why c) Apple's veclib/accelerate is super fast, but it is also numerically unstable. So much so that the scipy's devs dropped any support of it back in 2018. Like dang. That said, they are apparently are bring it back in, since the 13.3 release of macOS Ventura saw some major improvements in accelerate performance.

First, if you read through that scipy issue (https://github.com/scipy/scipy/issues/18118 ) the author was willing and able to relicense PRIMA under a 3-clause BSD license which is perfectly acceptable for scipy.

For the numerical recipes reference, there is a mention that scipy uses a slightly improved version of Powell's algorithm that is originally due to Forman Acton and presumably published in his popular book on numerical analysis, and that also happens to be described & included in numerical recipes. That is, unless the code scipy uses is copied from numerical recipes, which I presume it isn't, NR having the same algorithm doesn't mean that every other independent implementation of that algorithm falls under NR copyright.

Reading the discussions under a previous thread titled "More Descent, Less Gradient"( https://news.ycombinator.com/item?id=23004026 ), I guess people might be interested in PRIMA ( www.libprima.net ), which provides the reference implementation for Powell's renowned gradient/derivative-free (zeroth-order) optimization methods, namely COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA.

PRIMA solves general nonlinear optimizaton problems without using derivatives. It implements Powell's solvers in modern Fortran, compling with the Fortran 2008 standard. The implementation is faithful, in the sense of being mathmatically equivalent to Powell's Fortran 77 implementation, but with a better numerical performance. In contrast to the 7939 lines of Fortran 77 code with 244 GOTOs, the new implementation is structured and modularized.

There is a discussion to include the PRIMA solvers into SciPy ( https://github.com/scipy/scipy/issues/18118 ), replacing the buggy and unmaintained Fortran 77 version of COBYLA, and making the other four solvers available to all SciPy users.

if that makes your eyes bleed, what do you think about this? https://github.com/scipy/scipy/blob/main/scipy/special/specfun/specfun.f (heh)