LIEF VS pharos

My team is working on this problem in the context of creating Node.js single-executable applications. While the naive approach of just appending data at the end of the binary works, it is not friendly with code-signature in macOS and Windows given that signing operates on PE and Mach-O sections.

We have recently open-sourced a small tool called Postject (https://github.com/postmanlabs/postject), which is able to inject arbitrary data as proper ELF/Mach-O/PE sections for all major operating systems (with AIX support coming). The tool also provides C/C++ cross-platform headers for easily traversing the final binary and introspect whether the segment is present or not.

The tool is based on the LIEF (https://github.com/lief-project/LIEF) project.

At Postman, we are making use of this on our custom Node.js single-executable applications and soon on our custom Electron.js builds too.

https://github.com/lief-project/LIEF https://lief-project.github.io/doc/latest/api/python/index.html

Bintropy: Entropy-based packing detection featuring multiple modes (whole binary, per section or segment). Based on the awesome LIEF library, therefore supports ELF, PE, Mach-O.

I've been looking at LIEF toolkit for similar purposes (https://github.com/lief-project/LIEF). It's a python framework for cross platform binary analysis. I'm curious, does rabin2 support dll format?

OOAnalyzer is wonderful for recovering class/function information C++ that makes manual vtable/RTTI perusing relatively moot. I ultimately installed it along with the rest of Pharos on Linux and it was a couple days of figuring before I got things running.

Something that could be viable is e.g. learning the heuristics in a tool like https://github.com/cmu-sei/pharos instead of hand-crafting them. More coarse information like function partitioning, is this function a constructor etc.

That's why I eventually started looking for a more automated approach like https://github.com/cmu-sei/pharos.

I recently started analysing a game with Ghidra. I found the plugin mentioned was actually fairly useless in my case (but this was version 9, maybe things have changed), instead I used OOAnalyzer [1] and its associated Ghidra plugin - great for bootstrapping a class hierarchy, which is otherwise extremely tedious to do. It took 24 hours and ~100GB of memory, so I ran it on AWS, splitting the analysis in parts according to [2]. Also I’d recommend looking into scripting, it can save you a lot of time in repetitive scenarios.

[1] https://github.com/cmu-sei/pharos/blob/master/tools/ooanalyz...

There's no reason to be alarmed just because you've been running for 14 hours and using 11gb of ram. But you should make sure you're not running out of ram. You should also absolutely be following the step-by-step guide which you already found. You don't want to have to restart from scratch if something goes wrong.