slsa VS sealed-secrets

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

External-Secrets Operator : A Kubernetes operator that integrates external secret management systems like AWS Secrets Manager, HashiCorp Vault, Google Secrets Manager, and many more. The operator reads information from external APIs and automatically injects the values into a Kubernetes Secret (Alternatives : Vault, SOPS, Sealed Secrets)

I like sealed secrets (https://github.com/bitnami-labs/sealed-secrets) a lot. It's like 1Password, but for apps in kubernetes. You only need to secure a private key, and can throw encrypted secrets in a public github repo or anywhere you want.

It's owned by VMware (Broadcom) now, so you have to decide which company you hate more.

If you have noticed, you are setting secrets in plain text on the application-configmap.yml file, which is not ideal and is not a best practice for security. The best way to do this securely would be to use AWS Secrets Manager, an external service like HashiCorp Vault, or Sealed Secrets. To learn more about these methods see the blog post Shhhh... Kubernetes Secrets Are Not Really Secret!.

Yeah documentation is hard and I'm guilty (as a former maintainer of SealedSecrets)

SealedSecrets was designed with "write only" secrets in mind.

Turns out a lot of people need to access the current secrets because they need to update a part of a "composite" secret.

There are two kinds of "composite" secrets, one easy and one harder, but if you don't know how to do it, even the easier is hard:

1. Secret with multiple data "items" (also called keys in K8s Secret jargon but that's confusing when there is encryption involved). I.e. good old "data":{"foo": "....", "bar": "..."}

2. Secrets where data within one item is actually a config file with cleartext and secrets mixed up in one single string (usually some JSON or YAML or TOML)

Case 1 is "easy" to deal with once you realize that sealed secrets files are just text files and you can just manually merge and update encryoted data items. We even created a "merge" and some "raw" encryption APIs to make that process a little less "copy pasta" but it's still hard to have a good UX that works for everyone.

Case 2 is harder. We did implement a data templating feature that allows you to generate a config file via a go-template that keeps the cleartext parts in clear and uses templating directives to inject the secret parts where you want (referencing the encrypted the items)

The main problem with case 2 is that it's undocumented.

The feature landed in 2021:

https://github.com/bitnami-labs/sealed-secrets/pull/580

I noticed that people at my current $dayjob used sealed secrets for years and it took me a while to understand that the reason they hated it was that they didn't know about that fundamental feature.

And how to blame them!? It's still undocumented!

In my defense I spent so much effort before and after I left VMware to lobby so that the project got the necessary staffing so it wouldn't die of bitrot that I didn't have much time left to work on documentation. Which is a bit said and probably just an excuse :-)

That said, I'm happy that the project is alive and the current maintainers are taking care of it against the forces of entropy. Perhaps some doc work would be useful too. Unfortunately I don't have time for now.

This might be OT, and forgive me, but I think one of the best practices for Encrypting and Managing secrets in Kubernetes is to use Sealed Secrets, they allow your secrets to be securely stored in git with the rest of the configuration and yet no one with access to the Git repository will be able to read them. I say this might be OT, because Sealed Secrets are trying to mitigate a different threat, the threat of the secrets at rest somewhere, and not "live in the cluster", where in theory all the ingredients to decrypt the secrets would still live.

The addition of Consul and Vault gives me a few things. For one, right now I'm handling secrets with a mixture of SOPS and Sealed Secrets. I use Vault in my professional life, and have used both Vault and Consul at my last job. Vault is a beast, so I may as well get better at it; plus its options for secret injection are better.

Use Sealed Secrets Operator.

sealed-secrets (sealed)