Chameth.com - posts like artisanal-docker-images, g15-ram-upgrade, obfuscating-kotlin-proguard, why-you-should-be-using-https

Migrating from GitHub to Forgejo

2026-04-30T00:00:00Z

When Microsoft bought GitHub in 2018 my kneejerk reaction — like so many others — was to start looking for alternatives. For a while I self hosted a Gitea instance but I never totally bought into it: some repositories I still pushed to GitHub, some I pushed to Gitea and they got mirrored, and I ended up causing myself problems when I got the two confused. Part of the problem was that the GitHub UI was faster and cleaner than Gitea’s at the time; using Gitea felt like a chore compared to GitHub. I ended up not maintaining it and eventually binning it and just going back to GitHub.

An all-too familiar unicorn

Fast forward eight years, and GitHub is about what we all imagined when Microsoft bought it. If you take the most pessimistic way of counting, they have zero nines of reliability¹. If you take the most generous, they have a single nine. That’s around 30 minutes of downtime every day. It feels like it must be more than that, given how many times you see the damned unicorn.

Uptime aside, they’ve crowbarred LLMs in all over the place; they don’t have the time to maintain official GitHub actions; the ones they do maintain have Copilot running roughshod all over them²; actions themselves are slow to run³, often have weird transient failures, and there are so many footguns that I’m pretty sure GitHub actions should be an entire category in the CWE top 10.

Speaking of security, while I was drafting this post a remote code execution was reported and the details are… impressive. The actual security issue was a pretty stupid oversight. Those happen. But they end up being able to execute code as a globally shared git user with access to all the other repositories on the node. What? I can’t quite get my head around it. No sandboxing, no containers, it just runs as a git user?

So, yeah, I’m not a fan of GitHub in its current state. A few months back I took the plunge and set up Forgejo, a fork of Gitea where they did radical things like add tests and ensure that it’s community operated not twisted for commercial use. Forgejo also potentially has some fun security problems⁴, but the way I host it mitigates more-or-less all problems. It’s also slightly less shocking when an open source project has lapses in basic security versus a $2 trillion corporation.

It’s taken a bit of doing, but I’m very happy with the setup I have.

The basic setup

I run all my server software using Docker, and Forgejo is no exception. They have decent documentation on how to get it running, and publish rootless images which is nice (and diminishes my urge to make an artisanal version myself). You can set configuration options using environment variables, which is perfect for containers, although the names can end up a bit unwieldy. Like this thing: FORGEJO__repository.signing__SIGNING_NAME=Chris Smith. It gets the job done, but the environment-to-ini mapping is pretty ugly.

I didn’t want to expose Forgejo publicly, as I wanted to avoid having to try and secure it⁵, or dealing with bots scraping it, or users signing up, and so on. I wanted somewhere to store my git repositories, handle CI for me, and mirror them somewhere public for other people to see and interact with. If you’ve read some of my other posts you’ll probably guess where this is heading: Tailscale. Alongside the Forgejo container, I run a Tailscale instance with a serve.json that covers both the web frontend and the SSH listener used for git operations:

{
    "TCP": {
        "22": {
            "TCPForward": "forgejo:2222"
        },
        "443": {
            "HTTPS": true
        }
    },
    "Web": {
        "${TS_CERT_DOMAIN}:443": {
            "Handlers": {
                "/": {
                    "Proxy": "http://forgejo:3000"
                }
            }
        }
    },
    "AllowFunnel": {
        "${TS_CERT_DOMAIN}:443": false
    }
}

This allows me to interact normally with Forgejo from any of my devices or servers (which all run Tailscale), while not exposing it to the Internet at all. It also handles the TLS certificates automatically, which saves me setting up something else to do that.

For CI, I run the Forgejo runner image on the same server, and gave it a dind container to use to actually run the workloads. dind is the cutesy name for Docker-in-Docker, basically a Docker daemon running inside a Docker container. That keeps the CI workload separated nicely from the “production” workload running on the server, which is nice from both a security and a monitoring point of view.

The uncanny valley of actions

Forgejo actions are basically like GitHub actions: they run a series of steps using one or more container images. You define those steps in the exact same clunky YAML format as with GitHub. The big difference between them is that GitHub defaults to using an absolutely massive base image filled with out-of-date preinstalled software, and Forgejo leaves that for the administrator to configure. You could, of course, just use the same image as GitHub, but lugging around a multi-gigabyte image that doesn’t even have recent versions of the software I want isn’t really my style. Instead, I set about using a plain debian image as the base, and then did a face-palm when I tried to run a “normal” action and remembered they’re all written in JavaScript, so expect node to exist.

Accommodating JavaScript isn’t really my style, either, so I did the very sensible thing of writing my own suite of actions in Go. Being written in Go means they can be compiled statically, published as a container, and manage their own very limited dependencies. For example, the checkout action is built into an alpine container with git added, while the dockerbuild action uses the buildah image as a base. Each image has the tools it needs, pinned to a recent version, and nothing more. This is very much not a sensible approach to take for most people, but it made me happy. With six basic actions, I could replace almost all the ad-hoc workflows I had in GitHub⁶.

Other than the base images and my self-inflicted JavaScript machinations, everything works the same as GitHub. But faster. So much faster. Most of my CI workflows finish on Forgejo before GitHub would even have allocated a runner to the job⁷. And I can actually view the logs without them glitching out.

Step aside Dependabot, here comes Renovate

One of the boons and/or banes of hosting code on GitHub is access to Dependabot⁸. It’s a tool that monitors your dependencies, and automatically submits PRs when new versions are available. It also does security alerts, but they’re comically bad for Go at least⁹. I do like having dependencies automatically handled, especially with a cooldown, which is how I had Dependabot configured before moving.

To replace it, I’ve configured Renovate, an open source alternative. I’d seen it used a bunch before: both by projects on GitHub who prefer it over Dependabot, and by people using GitLab and other platforms where Dependabot isn’t. It mostly does the same things, but Renovate has a few very nice extras.

Firstly, it supports transcluding config from another repo. So in each of my many, many projects, I just have this stub of a renovate config:

{
  "$schema": "https://docs.renovatebot.com/renovate-schema.json",
  "extends": [
    "local>meta/renovate"
  ]
}

Then the actual config lives in a default.json in the meta/renovate repository. I mirror that repository to GitHub, if you want to have a look. This is an amazing feature. When I enabled cooldowns in my Dependabot config it was a slog to go through all my active repositories and make the same change over and over again. With Renovate I can configure that centrally. As far as I know Dependabot can’t do anything like that, even for enterprises.

Within that central config I use some of the other nice features. It has the ability to auto merge changes. I use this to automatically accept changes from trusted projects, or my own libraries:

    {
      "matchManagers": ["gomod"],
      "matchPackagePatterns": [
        "^github\\.com\\/csmith\\/",
        "^chameth\\.com\\/"
      ],
      "matchUpdateTypes": ["minor", "patch"],
      "automerge": true,
      "minimumReleaseAge": "0 days"
    },
    {
      "matchManagers": ["gomod"],
      "matchPackagePatterns": [
        "^github\\.com\\/csmith\\/",
        "^chameth\\.com\\/"
      ],
      "matchUpdateTypes": ["major"],
      "minimumReleaseAge": "0 days"
    },

This takes any minor or patch release of my libraries, and automerges it with no cooldown. For major version changes it overrides the cooldown but doesn’t automerge. To automerge Dependabot PRs you have to write an action, implement that logic yourself, and hope you’ve successfully avoided all the footguns inherent in that.

Speaking of major version updates, Renovate also has an option to automatically update the imports in Go packages when it’s offering a major update. In the config I have:

"postUpdateOptions": ["gomodTidy", "gomodUpdateImportPaths"],

Which means that every time it does an update it’ll run go mod tidy, and it’ll update the import paths. In Go, v2 of a module has a different import path to v1 (so they can coexist if transient dependencies need different versions), so usually you have to go through and find and replace those paths. Renovate handles that for me now! I still have to address whatever breaking changes are in the new version, but there’s a lot less grunt work.

It can also run custom commands¹⁰, so for example in my legotapas project I have this extra snippet in the renovate.json:

  "postUpgradeTasks": {
    "commands": [
      "sh -c 'rm plate_*.go'",
      "go run ./cmd/generate"
    ],
    "executionMode": "branch",
    "fileFilters": ["*.go"]
  }

I won’t go into the gory details, but this handles automatically regenerating a bunch of files that change every time a certain dependency is updated. Previously I had a separate action to do that on GitHub; just being able to drop commands in the config file is much nicer.

To actually run Renovate, I simply have a workflow in Forgejo on a schedule that uses their official Docker image. It also triggers if I update the config, which is nice when I add a new rule, as it’ll immediately update the open PRs to comply. Overall, Renovate has felt like a pretty big quality-of-life upgrade.

Odd problems

Not everything has been smooth sailing. Sometimes when Renovate automerged a change, it would just… disappear. The PR was closed as merged, but the commit never landed on master. It wasn’t a huge issue as the next Renovate run would just recreate it, but it would then disable the automerge and I’d get confused as to why. It turns out there was a major problem with how I had everything set up. Renovate was telling Forgejo to automerge when the required checks pass, but I had no branch protection rules set up in Forgejo, so as soon as the PR was created it was eligible to merge. As the CI was so fast I hadn’t noticed that the automerges were happening before the CI results were posted.

The disappearing commits were because the repository was getting into a bad state when Renovate tried to automerge multiple changes instantly in the same repo. Forgejo has a bunch of maintenance commands that helped to fix that problem, and I then wrote a little script to enable branch protection rules so the CI would actually run. The automerging has been smooth since.

Another issue I had early on was that merging would fail because the GPG keyring was locked. I had Forgejo configured to sign all the automatic commits, so you can verify what commits came from my infrastructure. Forgejo is the only process using the keyring, so I wasn’t sure why it was becoming locked. It turns out that GnuPG uses the hostname as part of the lockfile, and because Forgejo was running in Docker it was getting random hostnames based on the container ID. Explicitly setting a hostname made GnuPG and by extension Forgejo much happier.

Webhooks and updating

Forgejo has this amazing feature: you can add a webhook that’s called for all your repos. GitHub, for some reason, supports this for organisations but not people. Even though the two are treated the same in a lot of places. It doesn’t seem like it would be hard, and it’s such an obvious thing to want, but… no. I wrote a whole tool to add webhooks for me to get around this. Now I don’t need it, Forgejo does the right thing!

One particularly interesting use of these webhooks has been making my Docker containers auto-update when a new image is pushed. I previously had a webhook receiver for a few projects that would update them automatically, but GitHub package notifications were extremely laggy. Every other webhook fired within a minute or so of the event happening, but package webhooks would sometimes take 30-60+ minutes to fire. I opened a support case with GitHub and it got escalated to engineering and fixed about 10 weeks later¹¹. It was better for a while, and then got worse again. Trying to do continuous deployment with a 30 minute random lag was frustrating.

So now I had reliable webhooks, but the update process was a bit gross. I have a hangup about internet-facing services not having access to the Docker socket. It just feels like too big a security risk. So I had a process that received hooks, and then another that figured out if containers needed to be updated and restarted them. Adding a new container to the system meant recompiling the second process, which was a massive faff.

Fortunately, I was saved from this mess by my friend Greg. He wrote adze, a tool that receives webhooks and uses Docker Compose to update matching containers. He wrote a blog post about it. It’s all one process, but that’s no longer a blocker for me because the webhooks can be delivered privately from my Forgejo instance without exposing adze to the internet. It finds matching containers automatically based on the images they’re using¹², and combined with Forgejo not forcing me to add a webhook to every single repository makes my life so much easier. I write some code, push it to Forgejo, and within a minute or so it’s running on my dev instance.

I have IRC notifications set up for when adze does updates, and I still have some old ones that announce GitHub package webhooks. I keep seeing things like this and chuckling:

15:44:42 <@ircjag> [ADZE] chameth.com: pending updating git.yak-wall.ts.net/public/chameth.com
15:44:45 <@ircjag> [ADZE] chameth.com: success updating git.yak-wall.ts.net/public/chameth.com
17:11:54 <@ircjag> [GHCR] Container pushed to csmith/chameth.com:dev.

I pushed a change at 15:43, Forgejo built it and pushed the image (to both its own registry and to GitHub), then at 15:44 adze updated the container to the new version. An hour and a half later GitHub gets around to delivering its own webhook.

Mirroring and PRs

The final piece I had to figure out was how to actually interact with people. Most of my projects are small and don’t get a massive amount of public contributions, but the option should still be there. And ideally not involve anyone having to figure out how to e-mail patches¹³.

Mirroring outwards is simple: Forgejo supports it natively. Whenever I push to Forgejo, it pushes out to the corresponding repo on GitHub. I’ve also started mirroring some projects to Codeberg, to avoid having GitHub as my single “public” presence. But what happens when I get a pull request? If I merge it in the GitHub web UI, Forgejo will push its own branch over the top and get rid of it¹⁴. I could pull the changes, merge them, and push the result to Forgejo but then I don’t get the benefit of any CI.

I ended up writing a small private tool that deals with this for me. I give it a GitHub PR, and it effectively imports it into Forgejo for me. I created a prs organisation, and it’ll create a fork of the project in that org if it doesn’t exist. Then it pushes the PR contents to a branch, and creates a PR from the prs repo into the main one. It runs as a user which can write to the PRs org, but only submit pull requests to public repositories outside of it.

When the PR is imported, I can review it in the Forgejo UI, and then I can approve the CI workflows for that particular PR. This gives me some extra defence-in-depth in case anyone is trying to abuse one of those action footguns. Once the CI passes, I can merge it as normal in the Forgejo UI, it gets mirrored out to GitHub, and GitHub marks the PR as merged automatically. It’s not the most elegant solution, but the actual workflow is pretty smooth, especially for the low volumes of PRs I get.

With that, I have Forgejo set up to do everything I need from a git host, and I’m no longer hamstrung every time GitHub goes down.

This way of counting isn’t particularly fair, but it’s somewhat realistic. GitHub break out their uptime for things like git operations, pull requests, actions, webhooks and packages. Realistically if any of those independent things are down then my overall workflow doesn’t, well, work. ↩︎
To be clear, I’m not objecting to the use of Copilot here. It’s a tool. But if you use a tool to do something fairly simple and it spews out 35 commits, including at least 5 reverts, then either it’s a terrible tool or you’re using it very wrong. I didn’t hunt down this example, I came across it while trying to debug something and wasted a bunch of time trying to figure out what on earth was going on. ↩︎
You could self-host runners, which may help, except they’ve already threatened to charge you for the privilege of bringing your own runners. ↩︎
If people could stop finding RCEs in git services for a few days so I can stop redrafting this intro, that’d be great. ↩︎
I’m feeling pretty smug about this decision after reading about the security issues. ↩︎
There’s one holdout: my dockerfiles repo which is a very special snowflake with a very complex workflow and I’m still deciding how to handle it. ↩︎
Which is to be expected, given I’ve got a server dedicated to one person, and they’re trying to serve however-many million users at once. That doesn’t make it any less nice, though! ↩︎
I’d link to it but there’s nowhere really good to link to. It’s just been subsumed into the ‘security’ functions of GitHub, along with a bunch of copilot nonsense. ↩︎
The go project maintain a vulnerability database that includes details about which exact symbols are affected, and the govulncheck tool uses this to only report security issues in code that’s actually used. Dependabot doesn’t do that: it constantly alerts about code that’s not imported, so won’t ever be shipped. ↩︎
In the default config it doesn’t allow anything of the sort, of course. You can whitelist individual commands, but because this is a private server I can merrily whitelist .* and do whatever I like. ↩︎
The turnaround time isn’t particularly impressive, but the fact that they kept the support case open, and the (human!) support agent updated me several times is. I don’t have a lot of good things to say about GitHub, but I don’t think I’ve ever had a support experience that good from a company of a similar size. Especially as I wasn’t a paying customer at the time. ↩︎
Why didn’t I do that in the first place? It seems so obvious now… ↩︎
I really like the idea of an e-mail based workflow, but I absolutely hate actually doing it. ↩︎
This is what happened to me a lot when I was half using Gitea. ↩︎

Upgrading the RAM in a Dell G15 laptop

2023-07-29T00:00:00Z

The Dell G15

I currently use a Dell G15 laptop for work. It has served me well for a little over a year, but recently it has been struggling a little with my day-to-day workload. It came with 32GB of RAM — the highest possible specification at the time¹ — but that is apparently no longer enough for me.

For a recent project, I was working on a Rust library used in an Android app. That meant running the usual glut of Android tools (Android Studio, an emulator and at least one Gradle daemon) alongside a normal IDE (IntelliJ IDEA). Throw in a web browser and a couple of electron apps, and I often managed to use all 32GB.

When you start swapping memory out to an encrypted disk — even an SSD — it doesn’t make for great performance. At first, I tried to work around this by enabling the Linux out-of-memory (OOM) killer, but it turns out that it’s not too good with Electron apps: it will kill the large browser process, but then the small Electron wrapper will just respawn it.

Can it be upgraded or not?

The obvious solution to not having enough RAM is to add more RAM. A quick look in the manual showed this might not be possible, though. The manual includes the following “Memory specifications” table:

Description	Values
Memory slots	Two SODIMM slots
Memory type	DDR5
Memory speed	4800
Maximum memory configuration	32GB
Minimum memory configuration	8GB
Memory size per slot	8GB or 16GB
Memory configurations supported	8 GB, 1 x 8 GB, DDR5, 4800 MHz 16 GB, 1 x 16 GB, DDR5, 4800 MHz 16 GB, 2 x 8 GB, DDR5, 4800 MHz, dual-channel 32 GB, 2 x 16 GB, DDR5, 4800 MHz, dual-channel

That unambiguously says that an upgrade from 32GB is not possible. I gave up.

Later though, I was complaining about memory issues to a friend, and he pointed out a Dell forum thread where a couple of people claim to have successfully installed dual-channel 32GB modules. Since the alternative was getting an entire new PC after only a year, I decided to give it a go.

The upgrade attempt

I ordered a pair of Crucial 32GB DDR5-4800 SODIMMs, and after they turned up dismantled the laptop. The G15 comes apart pretty normally: there are uncovered screws on the bottom holding the lower part of the case on. With those removed and some gentle prying, it pops off, and you get access to the battery, GPU and motherboard.

The first thing I saw was this:

The G15 motherboard adamantly proclaiming that it doesn’t want 32GB SODIMMs

Not one but two labels that indicate it will only accept 8GB or 16GB modules. Oh well, what’s the worst that can happen?

Oops?

I dutifully installed the new modules, reconnected the battery and put the case back together. I pressed the power button, and… nothing. None of the usual garish lights that immediately come on, no screen output, just a dead laptop. After reading some more of the user manual, I found that there is a tiny status LED on the side next to the ethernet port. Forcing the laptop to power off and back on again, the status LED blinked a distress code at me: 2 amber blinks, 4 white blinks. The manual says that is — unsurprisingly — a memory fault.

I figured at this point that the manual and labels on the motherboard were probably right. I took the laptop apart again, reinstalled the original 2x16GB modules, reassembled it, and pressed the power button. It didn’t boot. I don’t spend a lot of time fiddling inside computers, but I’ve done it enough that I’m reasonably confident I can’t entirely break a computer while swapping some RAM modules. I took to Googling², and found an interesting article that said Dell laptops don’t like to boot after RAM changes unless you clear the CMOS by popping out the battery for 15 minutes.

I opened the laptop up, and looked around for the CMOS battery. There wasn’t one. Turns out they don’t exist any more. I left the main battery disconnected for a while to see if it would help, and it didn’t.

Unexpected success

I started to get worried: if I couldn’t fix this, I wouldn’t be able to work until I got a new PC, and that wasn’t really in my budget at the minute. I sat reading old forum threads and help guides³, none of which were actually useful. Out of nowhere, though, the laptop booted up.

Naturally, I immediately shut the laptop down again, opened it up, and switched back to the new RAM modules. Then I turned it on again and sat waiting. After about 15 minutes of it looking totally dead, it turned on and showed a BIOS warning about the hardware configuration being changed. It then booted perfectly normally, and all 64GB of RAM was visible and usable.

My theory is that the forum threads were right: Dell laptops are funny about RAM upgrades. But somehow in removing the physical CMOS battery, they’ve kept the same “you have to wait 15 minutes” behaviour just without any indication that’s what’s happening. Regardless, I now have enough RAM even for the greediest of IDEs and Electron apps.

Bizarrely, the maximum spec has decreased to 16GB since then. ↩︎
In the generic sense. I use Kagi these days. ↩︎
On my phone because, y’know, the laptop was busted. ↩︎

Reproducible Builds and Docker Images

2022-02-18T00:00:00Z

XKCD 2347: Dependency

Reproducible builds are builds which you are able to reproduce byte-for-byte, given the same source input. Your initial reaction to that statement might be “Aren’t nearly all builds ‘reproducible builds’, then? If I give my compiler a source file it will always give me the same binary, won’t it?” It sounds simple, like it’s something that should just be fundamentally true unless we go out of our way to break it, but in reality it’s actually quite a challenge. A group of Debian developers have been working on reproducible packages for the best part of a decade and while they’ve made fantastic progress, Debian still isn’t reproducible. Before we talk about why it’s a hard problem, let’s take a minute to ponder why it’s worth that much effort.

On supply chain attacks

Suppose you want to run some open-source software. One of the many benefits of open-source software is that anyone can look at the source and, in theory, spot bugs or malicious code. Some projects even have sponsored audits or penetration tests to affirm that the software is safe. But how do you actually deploy that software? You’re probably not building from source - more likely you’re using a package manager to install a pre-built version, or downloading a binary archive, or running a docker image. How do you know whoever prepared those binary artifacts did so from an un-doctored copy of the source? How do you know a middle-man hasn’t decided to add malware to the binaries to make money?

Even worse: if the software you’re trying to use includes any dependencies, you have the same issue of trust with them. Maybe your supplier isn’t compromising the software, but that doesn’t mean their supplier isn’t. The beauty-cum-horror of a supply chain attack is that it can target the weakest link anywhere along the supply chain. Even if there aren’t any binary files involved, dependencies can still be attacked: what if npmjs.com or proxy.golang.org or github.com return a different version of a dependency-of-a-dependency when the request comes from your IP address? It doesn’t even need to be a modified dependency, it could be a perfectly un-tampered, properly signed copy of the source, just from an older version with a known vulnerability.

Enter stage left: reproducible builds, here to save the day! If the build process is reproducible then you - or anyone else on the internet - can perform the same build on the same source and validate the output has the same checksum or hash. If Debian publish a binary package and an independent re-builder comes up with the exact same build artifact, there’s a reasonably good chance that the build is good. An attacker would have to compromise both the build machine and the re-build machine to do anything nefarious. The more re-builders there are, the less feasible a supply chain attack is.

So why isn’t software just reproducible?

Compilers

As a bit of an experiment, I asked some friends to run the following for me and report the answer:

echo -e "#include <stdio.h>\nint main() { printf(\"Hello\"); return 0; }" | \
  gcc -x c -o hello.out - && \
  sha256sum hello.out

This compiles a super-simple hello world program and then prints the SHA-256 hash of the resulting binary. Here are the results:

Hash	System	GCC
1f62feab5a06861dc575201d807781926d1ae49fb113da018fde8b670a1346f7	Arch	11.2.0
b8e6f2c7082be69f65ffa5e7a3d749eb47866a1b2e1ec19efb63cc59a8b160cd	Debian	8.3.0
cbad2e47a22c234b5e7fa55e029a8db4d64ac7a962e2176bd2e1373d78954088	Debian	8.3.0
e0f6bbc13b29fea8cfa2a975ba4661e781323298aec166c8311d342e6f93c4a6	Alpine	10.3.1
e379156895e06c7a0bf18ac4d648860edcb2655576b0ab9fab172bd6c8b92075	Debian	10.2.1
7ffdaee4eb64e016b89dc5e54d2c8eebab3cebafe2c7aa97de627b5972ecea46	Debian	11.2.0
8ae52cc166743b6ae1eb3e14179ef33de5061a04237f8f97088c896c41a2f698	Arch	11.1.0
8ae52cc166743b6ae1eb3e14179ef33de5061a04237f8f97088c896c41a2f698	Arch	11.1.0

As you can see, there are barely any duplicates. Even the same version of GCC on the same OS sometimes produces different results. And this is the most basic program I could write! Differences arise from the compiler version, the build flags, the libraries installed, and a whole host of other factors. If you compile a Go application instead of a C one, then by default the compiler will include debug information in the binary. This includes the full path to the source file on disk, so building a project in /home/chris/ will produce a different binary to building the same source in /tmp. Future versions of Go are also going to stamp in other meta-data such as VCS info, so building inside and outside a Git repository will produce different binaries.

Iteration order

So we’ve pinned our build environment, we’re manipulating timestamps when adding files to archives, now what? Imagine part of the build process involves looping through all the files in a directory and doing something. What order do these files get iterated in? Well, that very much depends on the filesystem and perhaps when the files themselves were created. To ensure this is reproducible we need to explicitly sort any such operation so that it’s always consistent. This iteration could be happening in a tool that’s called by another tool that’s called by a build script, so the fix isn’t necessarily straight-forward.

Side note: a bug war story

I’ve personally been victim to this kind of non-determinism. I was working on an Android app, and committed a new test that worked fine on my machine, and worked fine on the CI server. But it failed consistently for a colleague.

We both did fresh checkouts of the source, and ran the tests. Mine passed, his failed. He sent me an archive of his checkout in case there was something weird going on there, and the tests passed on my machine. We compared hashes of our checkouts, and they were the same. It was obviously environmental somehow, but everything else worked fine, and the build system went to great pains to ensure things were the same.

After a lot of debugging, I worked out that his test was running with a different version of a library to me, despite the libraries being defined in the build files and the build files being identical. After even more debugging it turned out there were two versions of the library on the classpath, and the ordering of them was different between my machine and his.

The actual issue turned out to be that the build tool generated the classpath by iterating over the library files, and that iteration was done in order of file creation time. The two libraries were added at different points in the project history, so the creation time in your local cache depended on which versions of the app you’d built in the past. With no cache everything worked as expected but there was a slim range of commits where only one library was in use, and if you had run the tests during that period your cache was effectively poisoned.

We fixed the issue by excluding the older version of the library (which was being pulled in as a transient dependency), and filed a bug against the build tool to make the classpath properly deterministic. I think that stands as the most difficult to diagnose bug I’ve ever dealt with.

Interestingly, if you iterate over a map in Go, the iteration is deliberately non-deterministic. That’s an attempt to defeat Hyrum’s Law and prevent developers from relying on whatever the current behaviour happens to be. This actually makes it easier to make things reproducible as the problem is loud and in-your-face, rather than subtle and hard to spot.

Other sources

There’s an awful lot of other places that non-determinism can come from. If the app pulls in dependencies, their versions have to be pinned, otherwise your build changes depending on the latest release of that dependency. If the build process pulls any information from a website, it’s liable to change. Hopefully the website is under your control so that you can version the resource and pin that version. Obviously, anything to do with dates or the current user will probably cause problems. Timezones and locales can cause subtle differences.

What about Docker?

Docker comes with some good and some bad points for reproducibility. The biggest advantage is that it inherently completely describes the build environment; it should work exactly the same from one system to another, even across different OS families. The biggest drawback is it sprays timestamps around like no-one’s business. Each layer in a container image is a .tar.gz file, meaning each file within it is timestamped as discussed above. Making an image involves a lot of copying of files around, so these timestamps invariably end up causing reproducibility issues.

Even worse than timestamps in the filesystem, the image format also contains some meta-data that includes the timestamp at which each layer was built. That means even if you go out of your way to set the timestamp of every single file in your image, the image itself will be different every time you rebuild it. There is no way to deal with this in Docker, which is a very sad state of affairs. Fortunately, Buildah provides a --timestamp flag for its build commands; this not only sets the layer timestamp but also the creation timestamp of any file within the layer.

The other major issue that affects Docker images is the pinning of packages pulled in by package managers. An awful lot of images are based on Alpine or Debian derivatives, and use apk or apt to install dependencies. These need to have a version specified as otherwise the package manager will just pull in the latest at the time of the build. But this isn’t quite enough: you also need to pin the version of any packages that they depend on, recursively. This means flattening the entire package hierarchy and installing all the packages explicitly and with pinned versions.

One more wrinkle in the package management space is that Alpine don’t keep old packages in their main repositories. If you have a Docker image with pinned alpine packages in, it will stop building if the package is updated. This isn’t necessarily fatal to making a reproducible build — as long as it’s reproducible for its useful lifetime, I don’t really see an issue.

Honestly, though, the biggest issue with making Docker images reproducible is getting people to care. Dockerfiles are a relatively new way of packaging software, and there’s no centralised organisation like you find with Linux distributions. There are enough challenges that most casual packagers aren’t going to bother, and no real incentive for them to. That won’t stop me trying, though!

Artisanal Docker images

2022-02-05T00:00:00Z

Artisanal containers…

I run a fair number of services as docker containers. Recently, I’ve been moving away from pre-built images pulled from Docker Hub in favour of those I’ve hand-crafted myself. If you’re thinking “that sounds like a lot of effort”, you’re right. It also comes with a number of advantages, though, and has been a fairly fun journey.

The problems with Docker Hub and its images

Rate limits

For the last few years, I’ve been getting increasingly unhappy with Docker Hub itself. Docker-the-technology is wonderful, but Docker-the-company has been making some rather large missteps. The biggest and most impactful of these has been introducing “pull rate” limits. At the time of writing, if you want to just pull a public image without logging in then you are limited to 100 pulls every 6 hours. If you log in then you’re limited to 200 pulls per 6 hours, but it’s account wide. This might seem like a big enough number, but I repeatedly hit it and there is no way to actually audit what is causing it. I have various containers that may all pull images at arbitrary times (e.g. continuous integration build agents), and the only information you get back from Docker Hub is the number of pulls remaining.

Obviously, I could start paying Docker Hub for a “Pro” plan. That gets you 5,000 pulls per day for $7/month. The downside is that every docker client would have to be authenticated, which presents a fair annoyance in terms of credential management. I also don’t really like how they positioned the service as a public utility with special treatment in the docker software, and then start tightening the ratchet to make money.

“Bad” images

I’m fairly opinionated about what a container image should look like: most importantly it should run just a single process, and only include the bare minimum dependencies required for that. Other people think differently, and it’s very hard to tell at a glance whether an image on Docker Hub contains just the application you want, or whether it also bundles MySQL, Redis, Elasticsearch, and a partridge in a pear tree. Some people want that kind of thing, but I really don’t. It’s also very hard to tell whether an image is officially endorsed by the upstream project, and where the source Dockerfile is. This used to be better because most projects used Docker Hub’s automatic builds, but they’re now a “pro” feature.

I quite often found that I’d be looking for an image for X, and there would be 5-10 images from different users. None of them looked official, some of them were out-of-date, some bundled the kitchen sink. Even when one looked good, it’s a bit of a gamble whether the author is going to keep it updated or not.

Doijanky

The rate limits and other problems were annoying, but they weren’t really annoying enough to force me to do anything about it. The straw that broke the camel’s back came later: I was looking at the official golang images, and noticed that all the tags were pushed by a random user account called “doijanky”:

An ‘official’ Docker Hub image pushed by user ‘doijanky’

I, perhaps naively, assumed that official images were built on Docker Hub’s own infrastructure. Why would all the Golang images be attributed to this user? Checking out their profile, they’re simply identified as a “Community User” like everyone else, with no repositories of their own. The only thing in the profile is their homepage, which is a link to a Jenkins dashboard: https://doi-janky.infosiftr.net/. It appears legitimate: “Infosiftr” are a container consultancy and the dashboard is linked to from the README in the official images git repository, but I find it baffling that they’re using third-party infrastructure and a normal user account (with a dubious name) to push these images. There doesn’t seem to be a good way to verify what you pull corresponds to the Dockerfile it came from; if infosiftr wanted to inject something into the build they could happily do so, and who knows how good their infosec posture is? If someone got access to the “doijanky” account, how long could they upload malicious images before someone noticed?

This little roller-coaster ride from “are all the official images compromised?!” to “oh, no, they’re not, it’s all just awful” finally convinced me to look at building my own images from scratch.

The implementation: templating with contempt

One of the big issues I needed to tackle was how to deal with updates. I didn’t want to have to go and edit a file every time some minor release was made of some software, or every time there was a security vulnerability in a common library. The official images use a shell-scripting based system to check for updates and generate Dockerfiles, I decided to do something similar but with Go templates. The result is a tool called contempt. It takes a template like:

FROM {{image "golang"}} AS build

ARG TAG="{{github_tag "example/project"}}"

RUN apk add --no-cache \
        {{range $key, $value := alpine_packages "git" -}}
        {{$key}}={{$value}}\
        {{end}}; \
# ...

Contempt has support for getting information from a variety of sources. In this case, it’s getting the latest digest of another Docker image, the latest tag from a Git repository, and the latest version of an alpine package and all its dependencies. The resulting Dockerfile looks something like this:

# Generated from https://github.com/csmith/dockerfiles/blob/master/miniflux/Dockerfile.gotpl
# BOM: {"apk:brotli-libs":"1.0.9-r5","apk:busybox":"1.34.1-r4", <snip> }

FROM reg.c5h.io/golang@sha256:ac8fa5f4078b0a697796b5d741... AS build

ARG TAG="2.0.35"

RUN apk add --no-cache \
        brotli-libs=1.0.9-r5\
        busybox=1.34.1-r4\
        # <snip>
        pcre2=10.39-r0\
        zlib=1.2.11-r3\
        ; \
# ...

I’ve cut out the longer parts for readability. You can see that it pins all versions of the alpine packages in use, as well as the base image. This ensures that if you build from the same Dockerfile at a later time it will build the same image (or will fail entirely, as Alpine doesn’t keep their old packages around indefinitely). It also produces a “bill of materials” as a really long JSON-encoded comment. If you let contempt commit the Dockerfile it uses the BOM to generate useful commit messages like: [project] apk:busybox: 1.34.1-r3->1.34.1-r4, so you can see exactly what changed.

Contempt also has support for building and pushing images whenever it changes the Dockerfile. I use it in a GitHub action that runs daily to check all my images are up-to-date and push those that aren’t. It understands the dependencies between images (by pre-analysing the templates) so it will always check and build base images before ones that require them. This means an update to, say, the “alpine” base image will cause anything that depends on it to get updated at the same time, ensuring security updates are rolled out promptly.

The result

You can see my collection of lovingly hand-crafted Dockerfiles in my dockerfiles repository.

There are a number of advantages to handwriting all the images I use. The obvious one is that they’re all built how I want: there are no extraneous dependencies, they’re all based on the same small set of base images (rather than pulling around 10 different versions of debian), nothing tries to also run a DBMS in its container, etc.

This level of customisation goes further, though. Because I’m packaging the software myself, I can tweak how it’s built to fit my needs. A couple of things I run need their own TLS certificates separate from my normal HTTPS setup, so I bake my certwrapper tool in to manage those; I can even set the build flags on certwrapper to only enable the particular DNS provider I personally need (thus avoiding dragging in clients for AWS, GCP, etc). Some software like Hashicorp Vault has an optional web interface that I don’t need, so I simply don’t enable it in the build. These changes save build time, reduce image sizes, in some cases improve runtime performance, and generally reduce the attack surface of what’s running in the container.

It’s also been a great way to learn more about how software is distributed. Writing a Dockerfile is not that distant from writing a PKGBUILD file for an Arch Linux package, or the equivalent for other distributions. In a couple of instances I’ve googled how to solve a particular issue, and found an Arch or Void linux maintainer asking the upstream project about the exact same issue.

Finally, all the images I build I push to my own registry so there are obviously no rate limiting issues. Standing up a service (assuming the Dockerfile has been written!) is amazingly quick because the base layers are all shared and cached, and the registry is a lot physically closer than Docker Hub. Bootstrapping this whole thing becomes an interesting problem because the image for the registry is stored on the registry, but I’ll leave that discussion for another post…

Obfuscating Kotlin code with ProGuard

2019-10-21T00:00:00Z

Kotlin + Proguard = fun

Obfuscating code is the process of modifying source code or build output in order to make it harder for humans to understand. It’s often employed as a tactic to deter reverse engineering of commercial applications or libraries when you have no choice but to ship binaries or byte code. For Android apps, ProGuard is part of the default toolchain and obfuscation is usually only a config switch away.

I was recently working on an Android library written in Kotlin that my client wanted obfuscated to try and protect some of their trade secrets that were included. Not a problem, I thought: it’s just a few lines of ProGuard config and we’re away. Four hours and lots of hair pulling later I finally got it working…

If at first you don’t succeed…

At first it seemed like ProGuard was refusing to obfuscate any class with a keep rule. With a simple test class:

class Test {

    private val secret = 123
    var name = "Chris"

    fun greet() {
        println("Hi $name! Enter a number: ")
        readLine()?.let { guess(it.toInt()) }
    }

    private fun guess(attempt: Int) = println(
        if (attempt == secret) {
            "Correct!"
        } else {
            "Nope!"
        }
    )

}

And a ProGuard rule of:

-keep public class Test {
    public void greet();
}

I expected that the Test class and the greet method would remain, but both fields and the guess method would be obfuscated. When I built the project and opened the class from Android Studio’s APK inspector I was disappointed:

public final class Test public constructor() {
    public final var name: kotlin.String /* compiled code */

    private final val secret: kotlin.Int /* compiled code */

    public final fun greet(): kotlin.Unit { /* compiled code */ }

    private final fun guess(attempt: kotlin.Int): kotlin.Unit { /* compiled code */ }
}

Having your secret sauce in a field labelled “secret” isn’t exactly the level of obfuscation I was hoping for. ProGuard has lots of knobs that you can twist to affect what it keeps and what it renames, but all the incantations of -keep, -keepmembernames, -allowobfuscation, and so on, that I could come up with either resulted in the class completely vanishing (because it wasn’t kept) or showing up with all its symbols intact.

There are lots of useful Stack Overflow posts describing how to obfuscate a single method, or keep a single method and obfuscate the rest, but nothing I tried seemed to make a difference. I’m not the biggest fan of ProGuard, but I’ve used it enough before to know that it’s not usually that hard to make it submit to your demands. Obviously something else was going on.

… Maybe you’re solving the wrong problem

My next thought was that perhaps Android Studio was doing something clever like reading the ProGuard mapping file and automatically deobfuscating the output for me. Looking at the mapping file it seems that ProGuard has indeed decided to rename some things:

Test -> Test:
    int secret -> a
    java.lang.String name -> b
    void greet() -> greet
    void <init>() -> <init>

The obvious solution is to look at the class file in something less smart than Android Studio. A couple of unzips later and I could do a quick test to see if the original names were still present:

$ strings Test.class | grep secret
secret

The problem is evidently not with Android Studio, as secret shouldn’t end up in the class file at all: it should have been entirely replaced with a like the mapping file says. The output from javap -p doesn’t show any hint of the original names, however:

$ javap -p Test
public final class Test {
  private final int a;
  private java.lang.String b;
  public final void greet();
  public Test();
}

But, given the names show up in strings, they must be kicking around somewhere. None of the various outputs from javap helped until I hit -verbose. Right at the end of the class is:

RuntimeVisibleAnnotations:
  0: #58(#84=[I#2,I#2,I#4],#69=[I#2,I#1,I#3],#81=I#2,#70=[s#40],#71=[s#55,s#39,s#43,s#85,s#39,s#72,s#42,s#91,s#47,s#90,s#39,s#73,s#39,s#74,s#67,s#83])
    kotlin.Metadata(
      mv=[1,1,15]
      bv=[1,0,3]
      k=1
      d1=["\u0000\"\n\u0002\u0018\u0002\n\u0002\u0010\u0000\n\u0002\b\u0002\n\u0002\u0010\u000e\n\u0002\b\u0005\n\u0002\u0010\b\n\u0000\n\u0002\u0010\u0002\n\u0002\b\u0003\u0018\u00002\u00020\u0001B\u0005¢\u0006\u0002\u0010\u0002J\u0006\u0010\u000b\u001a\u00020\fJ\u0010\u0010\r\u001a\u00020\f2\u0006\u0010\u000e\u001a\u00020\nH\u0002R\u001a\u0010\u0003\u001a\u00020\u0004X\u0086\u000e¢\u0006\u000e\n\u0000\u001a\u0004\b\u0005\u0010\u0006\"\u0004\b\u0007\u0010\bR\u000e\u0010\t\u001a\u00020\nX\u0082D¢\u0006\u0002\n\u0000¨\u0006\u000f"]
      d2=["LTest;","","()V","name","","getName","()Ljava/lang/String;","setName","(Ljava/lang/String;)V","secret","","greet","","guess","attempt","lib_release"]
    )

There’s a Kotlin annotation containing all of the symbols we were trying to obfuscate away! Kotlin apparently uses this annotation for reflection and for keeping track of various language features that don’t have a direct mapping in Java bytecode (such as members with internal access). Sure enough, switching back to Android Studio and making it “decompile” the Kotlin code into Java shows the annotation:

@Metadata(
   mv = {1, 1, 15},
   bv = {1, 0, 3},
   k = 1,
   d1 = {"\u0000\"\n\u0002\u0018\u0002\n\u0002\u0010\u0000\n\u0002\b\u0002\n\u0002\u0010\u000e\n\u0002\b\u0005\n\u0002\u0010\b\n\u0000\n\u0002\u0010\u0002\n\u0002\b\u0003\u0018\u00002\u00020\u0001B\u0005¢\u0006\u0002\u0010\u0002J\u0006\u0010\u000b\u001a\u00020\fJ\u0010\u0010\r\u001a\u00020\f2\u0006\u0010\u000e\u001a\u00020\nH\u0002R\u001a\u0010\u0003\u001a\u00020\u0004X\u0086\u000e¢\u0006\u000e\n\u0000\u001a\u0004\b\u0005\u0010\u0006\"\u0004\b\u0007\u0010\bR\u000e\u0010\t\u001a\u00020\nX\u0082D¢\u0006\u0002\n\u0000¨\u0006\u000f"},
   d2 = {"LTest;", "", "()V", "name", "", "getName", "()Ljava/lang/String;", "setName", "(Ljava/lang/String;)V", "secret", "", "greet", "", "guess", "attempt", "lib_release"}
)
public final class Test {

Solving the right problem

Now I had an idea of what was happening I was able to find a couple of other reports of people having the same issue. There’s an open feature request for ProGuard to support Kotlin’s metadata annotation but it’s not yet supported.

As this was a library with a fairly straight forward interface I reasoned I could probably get ProGuard to strip out the metadata annotation. If I stopped Kotlin reflection working in the process then that would actually be a small bonus. Unfortunately other people with the same idea had reported back that they were unsuccessful: even when not using the default ProGuard config, somehow the annotations are kept.

Adding a -printconfiguration instruction to my configuration lets me see the full configuration being passed to ProGuard, and the reason for keeping quickly becomes obvious:

-keepattributes *Annotation*,*Annotation*

This appears to be added by the Android build plugin before it invokes ProGuard, and there’s no obvious way to disable it. ProGuard doesn’t offer a way to reverse this instruction, either, but fortunately the build plugin seems to concatenate all of the -keepattribute values together and puts our user-supplied ones first. Adding a negative filter:

-keepattributes !*Annotation*

Results in the following in the printed configuration:

-keepattributes !*Annotation*,*Annotation*,*Annotation*

The negative filter prevents any subsequent filters from matching. Recompiling and looking at the class file again looks a lot more sensible:

import kotlin.io.ConsoleKt;

public final class Test {
   private final int a = 123;
   private String b = "Chris";

   public final void greet() {
      String var1 = "Hi " + this.b + "! Enter a number: ";
      System.out.println(var1);
      String var10000 = ConsoleKt.readLine();
      if (var10000 != null) {
         var1 = var10000;
         int var3 = Integer.parseInt(var1);
         var1 = var3 == 123 ? "Correct!" : "Nope!";
         System.out.println(var1);
      }
   }
}

And that’s the story of how I spent half a day making a one line change.