slsa VS cli

Short answer: not where it counts.

My work focuses on recognizing known functions in obfuscated binaries, but there are some papers you might want to check out related to deobfuscation, if not necessarily using ML for deobfuscation or decompilation.

My take is that ML can soundly defeat the "easy" and more static obfuscation types (encodings, control flow flattening, splitting functions). It's low hanging fruit, and it's what I worked on most, but adoption is slow. On the other hand, "hard" obfuscations like virtualized functions or programs which embed JIT compilers to obfuscate at runtime... as far as I know, those are still unsolved problems.

This is a good overview of the subject, but pretty old and doesn't cover "hard" obfuscations: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1566145.

https://www.jinyier.me/papers/DATE19_Obf.pdf uses deobfuscation for RTL logic (FGPA/ASIC domain) with SAT solvers. Might be useful for a point of view from a fairly different domain.

https://advising.cs.arizona.edu/~debray/Publications/generic... uses "semantics-preserving transformations" to shed obfuscation. I think this approach is the way to go, especially when combined with dynamic/symbolic analysis to mitigate virt/jit types of transformations.

I'll mention this one as a cautionary tale: https://dl.acm.org/doi/pdf/10.1145/2886012 has some good general info but glosses over the machine learning approach. It considers Hex-rays' FLIRT to be "machine learning", but FLIRT just hashes signatures, can be spoofed (i.e. https://siliconpr0n.org/uv/issues_with_flirt_aware_malware.p...), and is useless against obfuscation.

Eventually I think SBOM tools like Black Duck[1] and SLSA[2] will incorporate ML to improve the accuracy of even figuring out what dependencies a piece of software actually has.

[1]: https://www.synopsys.com/software-integrity/software-composi...

[2]: https://slsa.dev/

The dependency chain is certified! SLSA!

The things you mentioned are not solved by a typical "SBOM" but e.g. CycloneDX has extra fields to record provenance and pedigree and things like in-toto (https://in-toto.io/) or SLSA (https://slsa.dev/) also aim to work in this field.

I've spent the last six months in this field and people will tell you that this or that is an industry best practice or "a standard" but in my experience none of that is true. Everyone is still trying to figure out how best to protect the software supply chain security and things are still very much in flux.

It's multi-pronged and I imagine adopters may use a subset of features. Broadly, I think folks are going to be interested in a) branch/tag/reference protection rules, b) file protection rules (monorepo or otherwise, though monorepos do pose a very apt usecase for gittuf), and c) general key management for those who primarily care about Git signing.

For those who care about a and b, I think the work we want to do to support [in-toto attestations](https://github.com/in-toto/attestation) for [SLSA's upcoming source track](https://github.com/slsa-framework/slsa/issues/956) could be very interesting as well.

It doesn't have to be. Corporations which are FedRAMP[1] compliant, have to build software reproducibly in a fully isolated environment, only from reviewed code.[2]

[1] https://en.wikipedia.org/wiki/FedRAMP

[2] https://slsa.dev/

SLSA stands for Supply chain Levels for Software Artifacts, and it is a framework that aims to provide a set of best practices for the software supply chain, with a focus on OSS. It was created by Google, and it is now part of the OpenSSF. It consists of four levels of assurance, from Level 1 to Level 4, that correspond to different degrees of protection against supply chain attacks. Our CTO Paolo Mainardi mentioned SLSA in a very good article on software supply chain security, and we also mentioned it in another article about securing OCI Artifacts on Kubernetes.

SLSA.dev

GitHub CLI - GitHub on the command line. Great for creating PRs, etc.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

This Docker image is designed to support implementing Github Actions with Python. As of version 4.0.0., it starts with the official python docker image as the base which is a Debian OS. It specifically uses python:3-slim to keep the image size down for faster loading of Github Actions that use pyaction. On top of the base, we've installed curl gpg, git, and the GitHub CLI. We added curl and gpg because they are needed to install the GitHub CLI, and they may come in handy anyway (especially curl) when implementing a GitHub Action.

You might be interested in GitHub's cli tool, which is open source, if you want to access GitHub without running their proprietary JS code.

https://cli.github.com/

👉 Note: This script uses the GitHub CLI. So make sure you've installed that if you haven't already. Instructions here.

View on GitHub

(Context: I'm pretty thick into Nix, and have been for about four years. Most of this post is focussed on the NixOS desktop experience, so DevOps nerds, ymmv.)

Unpopular opinion: Nix is not that hard.

What's "hard" from a nix-promotion strategy is motivating people to understand why they would want the benefits it offers. Mostly because Nix, especially with home-manager, dramatically worsens UX for several day-to-day tasks, simply by violating the Law of Least Surprise every couple of hours in normal use.

I want a fully idempotent, version-locked, rewindable user environment, with a version-controlled central config, because I have half a dozen devices that, for reasons, I need to keep perfectly interchangeable with one another. Most users do not want this, for the simple fact that mutating their configs and differentiating them locally on specific machines is not a bug, but a feature.

Even more than that, it's an expectation that most software developers share as well.

Case in point: I filed a bug against the GitHub CLI last week. If any org has the scope and motivation to build software that's compatible with NixOS, an OS most of whose users are developers, it should be GitHub, which is, at least notionally, all about developers, developers, developers. A change in GH required a config format migration, which was sensibly done by opening the config .yml and rewriting it.

Of course, this breaks NixOS not just in practice but in principle. NixOS/home-manager makes config files read-only. Surprise! https://github.com/cli/cli/issues/8462

The response from GitHub was basically, "yeah, we knew this was going to happen, we mentioned it to the packagers at NixOS, but we did it anyway, because it was still the best way to proceed for us." (And they weren't wrong.)

Now, once a month is an annoyance, but I run into these problems daily. I can't imagine any sane person -- which I am not -- would persist with using it.

Why do I keep using NixOS, then? Because I am terribly and disproprotionately annoyed by small changes in my user experience, which I find disruptive to my workflow and hence threaten my success. For me, forbidding apps from mutating the config files I established for them is a selling point. Being able to version-control an idempotent declarative config for all of them at once is heaven.

Unless you're like me, you'll hate NixOS. But some were meant for Nix.

Because

To use a proper dynamic auth for ghcr.io you can create a "credential helper" and then it is supported by flux, see here: https://fluxcd.io/flux/cheatsheets/oci-artifacts/#authentication Unfortunately the "official" credential helper for ghcr.io doesn't exist. I use this simple script as a helper: https://gist.github.com/pkit/a98411d21ecc9293066f4579088187d1 Which requires gh cli to be installed.

This Docker image is designed to support implementing Github Actions with Python. As of version 4.0.0., it starts with the official python docker image as the base which is a Debian OS. It specifically uses python:3-slim to keep the image size down for faster loading of Github Actions that use pyaction. On top of the base, we've installed curl gpg, git, and the GitHub CLI. We added curl and gpg because they are needed to install the GitHub CLI, and they may come in handy anyway (especially curl) when implementing a GitHub Action.

Two CLI tools I install right away are the GitHub CLI (via brew) and the Netlify CLI (via npm).