Archive for the ‘open source’ Category

Towards a More Open Secure Element Chip

Tuesday, December 20th, 2022

Secure Element” (SE) chips have traditionally taken a very closed-source, NDA-heavy approach. Thus, it piqued my interest when an early-stage SE chip startup, Cramium (still in stealth mode), approached me to advise on open source strategy. This blog post explains my reasoning for agreeing to advise Cramium, and what I hope to accomplish in the future.

As an open source hardware activist, I have been very pleased at the progress made by the eFabless/Google partnership at creating an open-to-the-transistors physical design kit (PDK) for chips. This would be about as open as you can get from the design standpoint. However, the partnership currently supports only lower-complexity designs in the 90nm to 180nm technology nodes. Meanwhile, Cramium is planning to tape out their security chip in the 22nm node. A 22nm chip would be much more capable and cost-effective than one fabricated in 90nm (for reference, the RP2040 is fabricated in 40nm, while the Raspberry Pi 4’s CPU is fabricated in 28nm), but it would not be open-to-the-transistors.

Cramium indicated that they want to push the boundaries on what one can do with open source, within the four corners of the foundry NDAs. Ideally, a security chip would be fabricated in an open-PDK process, but I still feel it’s important to engage and help nudge them in the right direction because there is a genuine possibility that an open SDK (but still closed PDK) SE in a 22nm process could gain a lot of traction. If it’s not done right, it could establish poor de-facto standards, with lasting impacts on the open source ecosystem.

For example, when Cramium approached me, their original thought was to ship the chip with an ARM Cortex M7 CPU. Their reasoning is that developers prize a high-performance CPU, and the M7 is one of the best offerings in its class from that perspective. Who doesn’t love a processor with lots of MHz and a high IPC?

However, if Cramium’s chip were to gain traction and ship to millions of customers, it could effectively entrench the ARM instruction set — and more importantly — quirks such as the Memory Protection Unit (MPU) as the standard for open source SEs. We’ve seen the power of architectural lock-in as the x86 serially shredded the Alpha, Sparc, Itanium and MIPS architectures; so, I worry that every new market embracing ARM as a de-facto standard is also ground lost to fully open architectures such as RISC-V.

So, after some conversations, I accepted an advisory position at Cramium as the Ecosystem Engineer under the condition that they also include a RISC-V core on the chip. This is in addition to the Cortex M7. The good news is that a RISC-V core is royalty-free, and the silicon area necessary to add it at 22nm is basically a rounding error in cost, so it was a relatively easy sell. If I’m successful at integrating the RISC-V core, it will give software developers a choice between ARM and RISC-V.

So why is Cramium leaving the M7 core in? Quite frankly, it’s for risk mitigation. The project will cost upwards of $20 million to tape out. The ARM M7 core has been taped out and shipped in millions of products, and is supported by a billion-dollar company with deep silicon experience. The VexRiscv core that we’re planning to integrate, on the other hand, comes with no warranty of fitness, and it is not as performant as the Cortex M7. It’s just my word and sweat of brow that will ensure it hopefully works well enough to be usable. Thus, I find it understandable that the people writing the checks want a “plan B” that involves a battle-tested core, even if proprietary.

This will understandably ruffle the feathers of the open source purists who will only certify hardware as “Free” if and only if it contains solely libre components. I also sympathize with their position; however, our choices are either the open source community somehow provides a CPU core with a warranty of fitness, effectively underwriting a $20 million bill if there is a fatal bug in the core, or I walk away from the project for “not being libre enough”, and allow ARM to take the possibly soon-to-be-huge open source SE market without challenge.

In my view it’s better to compromise and have a seat at the table now, than to walk away from negotiations and simply cede green fields to proprietary technologies, hoping to retake lost ground only after the community has achieved consensus around a robust full-stack open source SE solution. So, instead of investing time arguing over politics before any work is done, I’m choosing to invest time building validation test suites. Once I have a solid suite of tests in hand, I’ll have a much stronger position to argue for the removal of any proprietary CPU cores.

On the Limit of Openness in a Proprietary Ecosystem

Advising on the CPU core is just one of many tasks ahead of me as their open source Ecosystem Engineer. Cramium’s background comes from the traditional chip world, where NDAs are the norm and open source is an exotic and potentially fatal novelty. Fatal, because most startups in this space exit through acquisition, and it’s much harder to negotiate a high acquisition price if prized IP is already available free-of-charge. Thus my goal is to not alienate their team with contumelious condescension about the obviousness and goodness of open source that is regrettably the cultural norm of our community. Instead, I am building bridges and reaching across the aisle, trying to understand their concerns, and explaining to them how and why open source can practically benefit a security chip.

To that end, trying to figure out where to draw the line for openness is a challenge. The crux of the situation is that the perceived fear/uncertainty/doubt (FUD) around a particular attack surface tends to have an inverse relation to the actual size of the attack surface. This illustrates the perceived FUD around a given layer of the security hierarchy:

Generally, the amount of FUD around an attack surface grows with how poorly understood the attack surface is: naturally we fear things we don’t understand well; likewise we have less fear of the familiar. Thus, “user error” doesn’t sound particularly scary, but “direct readout” with a focused ion beam of hardware security keys sounds downright leet and scary, the stuff of state actors and APTs, and also of factoids spouted over beers with peers to sound smart.

However, the actual size of the attack surface is quite the opposite:

In practice, “user error” – weak passwords, spearphishing, typosquatting, or straight-up fat fingering a poorly designed UX – is common and often remotely exploitable. Protocol errors – downgrade attacks, failures to check signatures, TOCTOUs – are likewise fairly common and remotely exploitable. Next in the order are just straight-up software bugs – buffer overruns, use after frees, and other logic bugs. Due to the sheer volume of code (and more significantly the rate of code turnover) involved in most security protocols, there are a lot of bugs, and a constant stream of newly minted bugs with each update.

Beneath this are the hardware bugs. These are logical errors in the implementation of a function of a piece of hardware, such as memory aliasing, open test access ports, and oversights such as partially mutable cryptographic material (such as an AES key that can’t be read out, but can be updated one byte at a time). Underneath logical hardware bugs are sidechannels – leakage of secret information through timing, power, and electromagnetic emissions that can occur even if the hardware is logically perfect. And finally, at the bottom layer is direct readout – someone with physical access to a chip directly inspecting its arrangement of atoms to read out secrets. While there is ultimately no defense against the direct readout of nonvolatile secrets short of zeroizing them on tamper detection, it’s an attack surface that is literally measured in microns and it requires unmitigated physical access to hardware – a far cry from the ubiquity of “user error” or even “software bugs”.

The current NDA-heavy status quo for SE chips creates an analytical barrier that prevents everyday users like us from determining how big the actual attack surface is. That analytical barrier actually extends slightly up the stack from hardware, into “software bugs”. This is because without intimate knowledge of how the hardware is supposed to function, there are important classes of software bugs we can’t analyze.

Furthermore, the inability of developers to freely write code and run it directly on SEs forces more functionality up into the protocol layer, creating an even larger attack surface.

My hope is that working with Cramium will improve this situation. In the end, we won’t be able to entirely remove all analytical barriers, but hopefully we arrive at something closer to this:

Due to various NDAs, we won’t be able to release things such as the mask geometries, and there are some blocks less relevant to security such as the ADC and USB PHY that are proprietary. However, the goal is to have the critical sections responsible for the security logic, such as the cryptographic accelerators, the RISC-V CPU core, and other related blocks shared as open source RTL descriptions. This will allow us to have improved, although not perfect, visibility into a significant class of hardware bugs.

The biggest red flag in the overall scenario is that the on-chip interconnect matrix is slated to be a core generated using the ARM NIC-400 IP generator, so this logic will not be available for inspection. The reasoning behind this is, once again, risk mitigation of the tapeout. This is unfortunate, but this also means we just need to be a bit more clever about how we structure the open source blocks so that we have a toolbox to guard against potential misbehavior in the interconnect matrix.

My personal goal is to create a fully OSS-friendly FPGA model of the RISC-V core and their cryptographic accelerators using the LiteX framework, so that researchers and analysts can use this to model the behavior of the SE and create a battery of tests and fuzzers to confirm the correctness of construction of the rest of the chip.

In addition to the work advising Cramium’s engagement with the open source community, I’m also starting to look into non-destructive optical inspection techniques to verify chips in earnest, thanks to a grant I received from NLNet’s NGI0 Entrust fund. More on this later, but it’s my hope that I can find a synergy between the work I’m doing at Cramium and my silicon verification work to help narrow the remaining gaps in the trust model, despite refractory foundry and IP NDAs.

Counterpoint: The Utility of Secrecy in Security

Secrecy has utility in security. After all, every SE vendor runs with this approach, and for example, we trust the security of nuclear stockpiles to hardware that is presumably entirely closed source.

Secrecy makes a lot of sense when:

  • Even a small delay in discovering a secret can be a matter of life or death
  • Distribution and access to hardware is already strictly controlled
  • The secrets would rather be deleted than discovered

Military applications check all these boxes. The additional days, weeks or months delay incurred by an adversary analyzing around some obfuscation can be a critical tactical advantage in a hot war. Furthermore, military hardware has controlled distribution; every mission-critical box can be serialized and tracked. Although systems are designed assuming serial number 1 is delivered to the Kremlin, great efforts are still taken to ensure that is not the case (or that a decoy unit is delivered), since even a small delay or confusion can yield a tactical advantage. And finally, in many cases for military hardware, one would rather have the device self-destruct and wipe all of its secrets, rather than have its secrets extracted. Building in booby traps that wipe secrets can measurably raise the bar for any adversary contemplating a direct-readout attack.

On the other hand, SEs like those found in bank cards and phones are:

  • Widely distributed – often directly and intentionally to potentially adversarial parties
  • Protecting data at rest (value of secret is constant or may even grow with time)
  • Used as a trust root for complicated protocols that typically update over time
  • Protecting secrets where extraction is preferable to self-destruction. The legal system offers remedies for recourse and recovery of stolen assets; whereas self-destruction of the assets offers no recourse

In this case, the role of the anti-tamper countermeasures and side-channel minimization is to raise the investment necessary to recover data from “trivial” to somewhere around “there’s probably an easier and cheaper way to go about this…right?”. After all, for most complicated cryptosystems, the bigger risk is an algorithmic or protocol flaw that can be exploited without any circumvention of hardware countermeasures. If there is a protocol flaw, employing an SE to protect your data is like using a vault, but leaving the keys dangling on a hook next to the vault.

It is useful to contemplate who bears the greatest risk in the traditional SE model, where chips are typically distributed without any way to update their firmware. While an individual user may lose the contents of their bank account, a chip maker may bear a risk of many tens of millions of dollars in losses from recalls, replacement costs and legal damages if a flaw were traced to their design issue. In this game, the player with the most to lose is the chipmaker, not any individual user protected by the chip. Thus, a chipmaker has little incentive to disclose their design’s details.

A key difference between a traditional SE and Cramium’s is that Cramium’s firmware can be updated (assuming an updateable SKU is released; this was a surprisingly controversial suggestion when I brought it up). This is thanks in part to the extensive use of non-volatile ReRAM to store the firmware. This likewise shifts the calculus on what constitutes a recall event. The open source firmware model also means that the code on the device comes, per letter of the license, without warranty; the end customer is ultimately responsible for building, certifying and deploying their own applications. Thus, for a player like Cramium, the potential benefits of openness outweigh those of secrecy and obfuscation embraced by traditional SE vendors.

Summary

My role is to advise Cramium on how to shift the norms around SEs from NDAs to openness. Cramium is not attempting to forge an open-foundry model – they are producing parts using a relatively advanced (compared to your typical stand-alone SE) 22nm process. This process is protected by the highly restrictive foundry NDAs. However, Cramium plans to release much of their design under an open source license, to achieve the following goals:

  • Facilitate white-box inspection of cryptosystems implemented using their primitives
  • Speed up discovery of errors; and perhaps more importantly, improve the rate at which they are patched
  • Reduce the risk of protocol and algorithmic errors, so that hardware countermeasures could be the actual true path of least resistance
  • Build trust
  • Promote wide adoption and accelerate application development

Cramium is neither fully open hardware, nor is it fully closed. My goal is to steer it toward the more open side of the spectrum, but the reality is there are going to be elements that are too difficult to open source in the first generation of the chip.

The Cramium chip complements the eFabless/Google efforts to build open-to-the-transistors chips. Today, one can build chips that are open to the mask level using 90 – 180nm processes. Unfortunately, the level of integration achievable with their current technology isn’t quite sufficient for a single-chip Secure Element. There isn’t enough ROM or RAM available to hold the entire application stack on chip, thus requiring a multi-chip solution and negating the HSM-like benefits of custom silicon. The performance of older processes is also not sufficient for the latest cryptographic systems, such as Post Quantum algorithms or Multiparty Threshold ECDSA with Identifiable Aborts. On the upside, one could understand the design down to the transistor level using this process.

However, it’s important to remember that knowing the mask pattern does not mean you’ve solved the supply chain problem, and can trust the silicon in your hands. There are a lot of steps that silicon goes through to go from foundry to product, and at any of those steps the chip you thought you’re getting could be swapped out with a different one; this is particularly easy given the fact that all of the chips available through eFabless/Google’s process use a standardized package and pinout.

In the context of Cramium, I’m primarily concerned about the correctness of the RTL used to generate the chip, and the software that runs on it. Thus, my focus in guiding Cramium is to open sufficient portions of the design such that anyone can analyze the RTL for errors and weaknesses, and less on mitigating supply-chain level attacks.

That being said, RTL-level transparency can still benefit efforts to close the supply chain gap. A trivial example would be using the RTL to fuzz blocks with garbage in simulation; any differences in measured hardware behavior versus simulated behavior could point to extra or hidden logic pathways added to the design. Extra backdoor circuitry injected into the chip would also add loading to internal nodes, impacting timing closure. Thus, we could also do non-destructive, in-situ experiments such as overclocking functional blocks to the point where they fail; with the help of the RTL we can determine the expected critical path and compare it against the observed failure modes. Strong outliers could indicate tampering with the design. While analysis like this cannot guarantee the absence of foundry-injected backdoors, it constrains the things one could do without being detected. Thus, the availability of design source opens up new avenues for verifying correctness and trustability in a way that would be much more difficult, if not impossible, to do without design source.

Finally, by opening as much of the chip as possible to programmers and developers, I’m hoping that we can get the open source SE chip ecosystem off on the right foot. This way, as more advance nodes shift toward open PDKs, we’ll be ready and waiting to create a full-stack open source solution that adequately addresses all the security needs of our modern technology ecosystem.

Book Review: Open Circuits

Wednesday, September 21st, 2022

There’s a profound beauty in well-crafted electronics.

Somehow, the laws of physics conspired with the evolution of human consciousness such that sound engineering solutions are also aesthetically appealing: from the ideal solder fillet, to the neat geometric arrangements of components on a circuit board, to the billowing clouds of standard cells laid down by the latest IC place-and-route tools, aesthetics both inspire and emerge from the construction of practical, everyday electronics.

Eric Schlaepfer (@TubeTimeUS) and Windell Oskay (co-founder of Evil Mad Scientist)’s latest book, Open Circuits, is a celebration of the electronic aesthetic, by literally opening circuits with mechanical cross-sections, accompanied by pithy explanations and illustrations. Their masterfully executed cross-sectioning process and meticulous photography blur the line between engineering and art, reminding us that any engineering task executed with soul and care results in something that can inspire feelings of awe (“wow!”) and reflection (“huh.”): that is art.

The pages of Open Circuits contain ample inspiration for both novices and grizzled veterans alike. Having been in electronics for four decades, I sometimes worry I’m becoming numb and cynical as I watch the world’s landfills brim with cheap electronics, built without care and purchased (and disposed of) with even less thought. However, as I thumb through the pages of Open Circuits, that excitement, that awe which I felt as a youth when I traced my fingers along the outlines of the resistors and capacitors of my first computer returns to me. Schlaepfer and Oskay render even the most mundane artifacts, such as the ceramic disc capacitor, in splendid detail – and in ways I’ve never seen before. Prior to now, I had no intuition for the dimensions of an actual capacitor’s dielectric material. I also didn’t realize that every thick film resistor bears the marks of lasers that trim it to its final value. Or just seeing the cross-section of a coaxial cable, as joined through a connector – all of a sudden, the telegrapher’s equations and the time domain reflectometry graphs take on a new and very tangible meaning to me. Ah, I think, so that’s the bump in the TDR graph at the connector interface!

Also breathtaking is the sheer scope of components addressed by Schlaepfer and Oskay. Nothing is too retro, nothing is too modern, nothing is too delicate: if you’ve ever wanted to see a vacuum tube cut in half, they managed to somehow slice straight through it without shattering the thin glass envelope; likewise, if you ever wondered what your smartphone motherboard might look like, they’ve gone and sliced clear through that as well.

One of my favorite tricks of the authors is when they slice through optoelectronic devices: somehow, they manage to cut through multiple LEDs and leave them in an operable state, leading to stunning images such as a 7-segment LED still displaying the number “5” yet revealed in cross-section. I really appreciate the effort that went into mounting that part onto a beautifully fabricated and polished (perhaps varnished?) copper-clad circuit board, so that not only are you treated to the spectacle of the still-functional cross sectioned device, you have the reflection of the device rippling off of a handsomely brushed copper surface. Like I said: any engineering executed with soul and care is also art.

In a true class act, Schlaepfer and Oskay conclude the book with an “Afterward” that shares the secrets of their cross-sectioning and photography techniques. Adhering to the principle of openness, this meta-chapter breaks down the fourth wall and gives you a peek into their atelier, showing you the tools and techniques used to generate the images within the book. Such sharing of hard-earned knowledge is a hallmark of true masters; while lesser authors would withold such trade secrets, fearing others may rise to compete with them, Schlaepfer and Oskay gain an even deeper respect from their fans by disclosing the effort and craft that went into creating the book. Sharing also plants the seeds for a broader community of circuit-openers, preserving the knowledge and techniques for new generations of electronics aficionados.

Even if you’re not a “hardware person”, or even if you’re “not into tech”, the images in Open Circuits are so captivating that they may just tempt you to learn a bit more about it. Or, perhaps more importantly, a wayward young mind may be influenced to realize that hardware isn’t scary: it’s okay to peel back the covers and discover that the fruits of engineering are not merely functional, but also deeply aesthetic as well. I know that a younger version of me would have carried a copy of this book everywhere I went, poring over its pages at every chance.

While I was only able to review an early access electronic copy of their book, I am excited to get the full-color, hard-cover edition of the book. Having published a couple books with No Starch Press myself, I know the passion with which its founder, Bill Pollock, conducts his trade. He does not scrimp on materials: for The Hardware Hacker, he sprung on silver ink for the endsheets and clear UV spot inks for the cover – extra costs that came out of his bottom line, but made the hardcover edition look and feel great. So, I’m excited to see these wonderful images rendered faithfully onto the pages of a coffee-table companion book that I will be proud to showcase for years to come.

If you’re also turned on to Open Circuits, pre-order it on No Starch Press’ website, with the discount code “BUNNIESTUDIOS25”, to receive 25% off (no affiliate code or trackback in that link – 100% goes to No Starch and the authors). The code expires Tuesday, October 4. Pre-orders will also receive exclusive phone and desktop wallpaper images that are not in the book!

Fully Oxidizing `ring`: Creating a Pure Rust TLS Stack Based on `rustls` + `ring`

Friday, September 16th, 2022

I really want to understand all the software that runs on my secure devices.

It’s a bit of a quixotic quest, but so far we’ve made pretty good progress towards this goal: I’ve been helping to write the Xous OS from the ground up in pure Rust – from the bootloader to the apps. Xous now has facilities like secure storage, a GUI toolkit, basic networking, and a password vault application that can handle U2F/FIDO, TOTP, and plaintext passwords.

One of the biggest challenges has been keeping our SBOM (software bill of materials) as small as possible. I consider components of the SBOM to be part of our threat model, so we very selectively re-write crates and libraries that are too bloated. This trades off the risk of introducing new bugs in our hand-rolled code versus the risk of latent, difficult-to-discover bugs buried in more popular but bloated libraries. A side benefit of this discipline is that to this day, Xous builds on multiple platforms with nothing more than a default Rust compiler – no other tooling necessary. It does mean we’re putting a lot of trust in the intractably complicated `rustc` codebase, but better than also including, for example, `gcc`, `nasm`, and `perl` codebases as security-critical SBOM components.

Unfortunately, more advanced networking based on TLS is a huge challenge. This is because the “go-to” Rust library for TLS, `rustls`, uses `ring` for its cryptography. `ring` is in large part an FFI (foreign function interface) wrapper around a whole lot of assembly and C code that is very platform specific and lifted out of BoringSSL. And it requires `gcc`, `nasm`, and `perl` to build, pulling all these complicated tools into our SBOM.

Notwithstanding our bespoke concerns, `ring` turns out to be the right solution for probably 90%+ of the deployments by CPU core count. It’s based on the highly-regarded, well-maintained and well-vetted BoringSSL codebase (“never roll your own crypto”!), and because of all the assembly and C, it is high performance. Secure, high-performance code, wrapped in Rust. What else could you ask for when writing code that potentially runs on some of the biggest cloud services on the Internet? I definitely can’t argue with the logic of the maintainers – in Open Source, sustainability often requires catering to deep-pocketed patrons.

The problem, of course, is that Open Source includes The Bazaar, with a huge diversity of architectures. The problem is well-stated in this comment from a RedHat maintainer:

…I’m not really speaking as a member of the Packaging Committee here, but as the person who is primary maintainer for 2000+ packages for Rust crates.

In Fedora Linux, our supported architectures are x86_64, i686, aarch64, powerpc64le, s390x, and, up to Fedora 36, armv7 (will no longer supported starting with Fedora 37). By default, all packages are built on all architectures, and architecture support is opt-out instead of opt-in. […]

On the other hand, this also makes it rather painful to deal with Rust crates which only have limited architecture support: Builds of packages for the affected crates and every other package of a Rust crate that depends on them need to opt-out of building on, in this case, powerpc64le and s390x architectures. This is manageable for the 2-3 packages that we have which depend on ring, but right now, I’m in the process of actually removing optional features that need rustls where I can, because that support is unused and hard to support.

However, the problem will get much worse once widely-used crates, like hyper (via h3 and quinn) start adding a (non-optional) dependency on rustls / ring. At that point, it would probably be easier to stop building Rust crates on the two unsupported architectures completely – but we cannot do that, because some new distribution-critical components have been introduced, which were either written from scratch in Rust, or were ported from C or Python to Rust, and many of them are network stack related, with many of them using hyper.

Long story short, if Redhat/Fedora can’t convince `ring` to support their needs, then the prognosis for getting our niche RISC-V + Xous combo supported in `ring` does not look good, which would mean that `rustls`, in turn, is not viable for Xous.

Fortunately, Ellen Poe (ellenhp) reached out to me in response to a post I made back in July, and informed me that she had introduced a patch which adds RISC-V support for ESP32 targets to `ring`, and that this is now being maintained by the community as `ring-compat`. Her community graciously tried another go at submitting a pull request to get this patch mainlined, but it seems to not have made much progress on being accepted.

At this point, the following options remained:

  • Use WolfSSL with FFI bindings, through the wolfssl-sys crate.
  • Write our own crappy pure-Rust TLS implementation
  • Patch over all the `ring` FFI code with pure Rust versions

WolfSSL is appealing as it is a well-supported TLS implementation precisely targeted toward light-weight clients that fit our CPU profile: I was confident it could meet our space and performance metrics if we could only figure out how to integrate the package. Unfortunately, it is both license and language incompatible with Xous, which would require turning it into a stand-alone binary for integration. This also reduced efficiency of the code, because we would have to wrap every SSL operation into an inter-process call, as the WolfSSL code would be sandboxed into its own virtual memory space. Furthermore, it introduces a C compiler into our SBOM, something we had endeavoured to avoid from the very beginning.

Writing our own crappy TLS implementation is just a patently bad idea for so many reasons, but, when doing a clean-sheet architecture like ours, all options have to stay on the table.

This left us with one clear path: trying to patch over the `ring` FFI code with pure Rust versions.

The first waypoint on this journey was to figure out how `ring-compat` managed to get RISC-V support into `ring`. It turns out their trick only works for `ring` version 0.17.0 – which is an unreleased, as-of-yet still in development version.

Unfortunately, `rustls` depends on `ring` version 0.16.20; `ring` version 0.16.20 uses C code derived from BoringSSL that seems to be hand-coded, but carefully reviewed. So, even if we could get `ring-compat` to work for our platform, it still would not work with `rustls`, because 0.17.0 != 0.16.20.

Foiled!

…or are we?

I took a closer look at the major differences between `ring` 0.17.0 and 0.16.20. There were enough API-level differences that I would have to fork `rustls` to use `ring` 0.17.0.

However, if I pushed one layer deeper, within `ring` itself, one of the biggest changes is that ring’s “fipsmodule” code changes from the original, hand-coded version, to a machine-generated version that is derived from ciphers from the fiat-crypto project (NB: “Fiat Crypto” has nothing to do with cryptocurrency, and they’ve been at it for about as long as Bitcoin has been in existence. As they say, “crypto means cryptography”: fiat cryptography utilizes formal methods to create cryptographic ciphers that are guaranteed to be correct. While provably correct ciphers are incredibly important and have a huge positive benefit, they don’t have a “get rich quick” story attached to them and thus they have been on the losing end of the publicity-namespace battle for the words “fiat” and “crypto”). Because their code is machine-generated from formal proofs, they can more easily support a wide variety of back-ends; in particular, in 0.17.0, there was a vanilla C version of the code made available for every architecture, which was key to enabling targets such as WASM and RISC-V.

This was great news for me. I proceeded to isolate the fipsmodule changes and layer them into a 0.16.20 base (with Ellen’s patch applied); this was straightforward in part because cryptography APIs have very little reason to change (and in fact, changing them can have disastrous unintended consequences).

Now, I had a `rustls` API-compatible version of `ring` that also uses machine-generated, formally verified pure C code (that is: no more bespoke assembly targets!) with a number of pathways to achieve a pure Rust translation.

Perhaps the most “correct” method would have been to learn the entire Fiat Crypto framework and generate Rust back-ends from scratch, but that does not address the thin layer of remnant C code in `ring` still required to glue everything together.

Instead, Xobs suggested that we use `c2rust` to translate the existing C code into Rust. I was initially skeptical: transpilation is a very tricky proposition; but Xobs whipped together a framework in an afternoon that could at least drive the scripts and get us to a structure that we could rapidly iterate around. The transpiled code generated literally thousands of warnings, but because we’re transpiling machine-generated code, the warning mechanisms were very predictable and easy to patch using various regex substitutions.

Over the next couple of days, I kept plucking away at the warnings emitted by `rustc`, writing fix-up patches that could be automatically applied to the generated Rust code through a Python script, until I had a transpilation script that could take the original C code and spit out warning-free Rust code that integrates seamlessly into `ring`. The trickiest part of the whole process was convincing `c2rust`, which was running on a 64-bit x86 host, to generate 32-bit code; initially all our TLS tests were failing because the bignum arithmetic assumed a 64-bit target. But once I figured out that the `-m32` flag was needed in the C options, everything basically just worked! (hurray for `rustc`’s incredibly meticulous compiler warnings!)

The upshot is now we have a fork of `ring` in `ring-xous` that is both API-compatible with the current `rustls` version, and uses pure Rust, so we can compile TLS for Xous without need of gcc, clang, nasm, or perl.

But Is it Constant Time?

One note of caution is that the cryptographic primitives used in TLS are riddled with tricky timing side channels that can lead to the disclosure of private keys and session keys. The good news is that a manual inspection of the transpiled code reveals that most of the constant-time tricks made it through the transpilation process cleanly, assuming that I interpreted the barrier instruction correctly as the Rust `compiler_fence` primitive. Just to be sure, I built a low-overhead, cycle-accurate hardware profiling framework called perfcounter. With about 2 cycles of overhead, I’m able to snapshot a timestamp that can be used to calculate the runtime of any API call.

Inspired by DJB’s Cache-timing attacks on AES paper, I created a graphical representation of the runtimes of both our hardware AES block (which uses a hard-wired S-box for lookups, and is “very” constant-time) and the transpiled `ring` AES code (which uses program code that can leak key-dependent timing information due to variations in execution speed) to convince myself that the constant-time properties made it through the transpilation process.

Each graphic above shows a plot of runtime versus 256 keys (horizontal axis) versus 128 data values (vertical axis) (similar to figure 8.1 in the above-cited paper). In the top row, brightness corresponds to runtime; the bright spots correspond to periodic OS interrupts that hit in the middle of the AES processing routine. These bright spots are not correlated to the AES computation, and would average out over multiple runs. The next lower row is the exact same image, but with a random color palette, so that small differences in runtime are accentuated. Underneath the upper 2×2 grid of images is another 2×2 grid that corresponds to the same parameters, but averaged over 8 runs.

Here we can see that for the AES with hardware S-boxes, there is a tiny bit of texture, which represents a variability of about ±20 CPU cycles out of a typical time of 4168 cycles to encrypt a block; this variability is not strongly correlated with key or data bit patterns. For AES with transpiled ring code, we see a lot more texture, representing about ±500 cycles variability out of a typical time of 12,446 cycles to encrypt a block. It’s not as constant time as the hardware S-boxes, but more importantly the variance also does not seem to be strongly correlated with a particular key or data pattern over multiple runs.

Above is a histogram of the same data sets; on the left are the hardware S-boxes, and the right is the software S-box used in the `ring` transpilation; and across the top are results from a single run, and across the bottom are the average of 8 runs. Here we can see how on a single run, the data tends to bin into a couple of bands, which I interpret as timing differences based upon how “warm” the cache is (in particular, the I-cache). The banding patterns are easily disturbed: they do not replicate well from run-to-run, they tend to “average out” over more runs, and they only manifest when the profiling is very carefully instrumented (for example, introducing some debug counters in the profiling routines disrupts the banding pattern). I interpret this as an indicator that the banding patterns are more an artifact of external influences on the runtime measurement, rather than a pattern exploitable in the AES code itself.

More work is necessary to thoroughly characterize this, but it’s good enough for a first cut; and this points to perhaps optimizing `ring-xous` to use our hardware AES block for both better performance and more robust constant-time properties, should we be sticking with this for the long haul.

Given that Precursor is primarily a client and not a server for TLS, leakage of the session key is probably the biggest concern, so I made checking the AES implementation a priority. However, I also have reason to believe that the ECDSA and RSA implementation’s constant time hardening should have also made it through the transpilation process.

That being said, I’d welcome help from anyone who can recommend a robust and succinct way to test for constant time ECDSA and/or RSA operation. Our processor is fairly slow, so at 100MHz simply generating gobs of random keys and signing them may not give us enough coverage to gain confidence in face of some of the very targeted timing attacks that exist against the algorithm. Another alternative could be to pluck out every routine annotated with “constant time” in the source code and benchmark them; it’s a thing we could do but first I’m still not sure this would encompass everything we should be worried about, and second it would be a lot of effort given the number of routines with this annotation. The ideal situation would be a Wycheproof-style set of test vectors for constant time validation, but unfortunately the Wycheproof docs simply say “TBD” under Timing Attacks for DSA.

Summary

`ring-xous` is a fork of `ring` that is compatible with `rustls` (that is, it uses the 0.16.20 API), and is pure Rust. I am also optimistic that our transpilation technique preserved many of the constant-time properties, so while it may not be the most performant implementation, it should at least be usable; but I would welcome the review and input of someone who knows much more about constant-time code to confirm my hunch.

We’re able to use it as a drop-in replacement for `ring`, giving us TLS on Xous via `rustls` with a simple `Cargo.toml` patch in our workspace:

[patch.crates-io.ring]
git="https://github.com/betrusted-io/ring-xous"
branch="0.16.20-cleanup"

We’ve also confirmed this works with the `tungstenite` websockets framework for Rust, paving the way towards implementing higher-level secure messaging protocols.

This leads to the obvious question of “What now?” — we’ve got this fork of `ring`, will we maintain it? Will we try to get things upstreamed? I think the idea is to maintain a fork for now, and to drop it once something better comes along. At the very least, this particular fork will be deprecated once `ring` reaches full 0.17.0 and `rustls` is updated to use this new version of `ring`. So for now, this is a best-effort port for the time being that is good enough to get us moving again on application development. If you think this fork can also help your project get un-stuck, you may be able to get `ring-xous` to work with your OS/arch with some minor tweaks of the `cfg` directives sprinkled throughout; feel free to submit a PR if you’d like to share your tweaks with others!

The Plausibly Deniable DataBase (PDDB): It’s Real Now!

Thursday, July 28th, 2022

Earlier I described the Plausibly Deniable DataBase (PDDB). It’s a filesystem (like FAT or ext4), combined with plausibly deniable full disk encryption (similar to LUKS or VeraCrypt) in a “batteries included” fashion. Plausible deniability aims to make it difficult to prove “beyond a reasonable doubt” that additional secrets exist on the disk, even in the face of forensic evidence.

Since then, I’ve implemented, deployed, and documented the PDDB. Perhaps of most interest for the general reader is the extensive documentation now available in the Xous Book. Here you can find discussions about the core data structures, key derivations, native & std APIs, testing, backups, and issues affecting security and deniability.

Rust: A Critical Retrospective

Thursday, May 19th, 2022

Since I was unable to travel for a couple of years during the pandemic, I decided to take my new-found time and really lean into Rust. After writing over 100k lines of Rust code, I think I am starting to get a feel for the language and like every cranky engineer I have developed opinions and because this is the Internet I’m going to share them.

The reason I learned Rust was to flesh out parts of the Xous OS written by Xobs. Xous is a microkernel message-passing OS written in pure Rust. Its closest relative is probably QNX. Xous is written for lightweight (IoT/embedded scale) security-first platforms like Precursor that support an MMU for hardware-enforced, page-level memory protection.

In the past year, we’ve managed to add a lot of features to the OS: networking (TCP/UDP/DNS), middleware graphics abstractions for modals and multi-lingual text, storage (in the form of an encrypted, plausibly deniable database called the PDDB), trusted boot, and a key management library with self-provisioning and sealing properties.

One of the reasons why we decided to write our own OS instead of using an existing implementation such as SeL4, Tock, QNX, or Linux, was we wanted to really understand what every line of code was doing in our device. For Linux in particular, its source code base is so huge and so dynamic that even though it is open source, you can’t possibly audit every line in the kernel. Code changes are happening at a pace faster than any individual can audit. Thus, in addition to being home-grown, Xous is also very narrowly scoped to support just our platform, to keep as much unnecessary complexity out of the kernel as possible.

Being narrowly scoped means we could also take full advantage of having our CPU run in an FPGA. Thus, Xous targets an unusual RV32-IMAC configuration: one with an MMU + AES extensions. It’s 2022 after all, and transistors are cheap: why don’t all our microcontrollers feature page-level memory protection like their desktop counterparts? Being an FPGA also means we have the ability to fix API bugs at the hardware level, leaving the kernel more streamlined and simplified. This was especially relevant in working through abstraction-busting processes like suspend and resume from RAM. But that’s all for another post: this one is about Rust itself, and how it served as a systems programming language for Xous.

Rust: What Was Sold To Me

Back when we started Xous, we had a look at a broad number of systems programming languages and Rust stood out. Even though its `no-std` support was then-nascent, it was a strongly-typed, memory-safe language with good tooling and a burgeoning ecosystem. I’m personally a huge fan of strongly typed languages, and memory safety is good not just for systems programming, it enables optimizers to do a better job of generating code, plus it makes concurrency less scary. I actually wished for Precursor to have a CPU that had hardware support for tagged pointers and memory capabilities, similar to what was done on CHERI, but after some discussions with the team doing CHERI it was apparent they were very focused on making C better and didn’t have the bandwidth to support Rust (although that may be changing). In the grand scheme of things, C needed CHERI much more than Rust needed CHERI, so that’s a fair prioritization of resources. However, I’m a fan of belt-and-suspenders for security, so I’m still hopeful that someday hardware-enforced fat pointers will make their way into Rust.

That being said, I wasn’t going to go back to the C camp simply to kick the tires on a hardware retrofit that backfills just one poor aspect of C. The glossy brochure for Rust also advertised its ability to prevent bugs before they happened through its strict “borrow checker”. Furthermore, its release philosophy is supposed to avoid what I call “the problem with Python”: your code stops working if you don’t actively keep up with the latest version of the language. Also unlike Python, Rust is not inherently unhygienic, in that the advertised way to install packages is not also the wrong way to install packages. Contrast to Python, where the official docs on packages lead you to add them to system environment, only to be scolded by Python elders with a “but of course you should be using a venv/virtualenv/conda/pipenv/…, everyone knows that”. My experience with Python would have been so much better if this detail was not relegated to Chapter 12 of 16 in the official tutorial. Rust is also supposed to be better than e.g. Node at avoiding the “oops I deleted the Internet” problem when someone unpublishes a popular package, at least if you use fully specified semantic versions for your packages.

In the long term, the philosophy behind Xous is that eventually it should “get good enough”, at which point we should stop futzing with it. I believe it is the mission of engineers to eventually engineer themselves out of a job: systems should get stable and solid enough that it “just works”, with no caveats. Any additional engineering beyond that point only adds bugs or bloat. Rust’s philosophy of “stable is forever” and promising to never break backward-compatibility is very well-aligned from the point of view of getting Xous so polished that I’m no longer needed as an engineer, thus enabling me to spend more of my time and focus supporting users and their applications.

The Rough Edges of Rust

There’s already a plethora of love letters to Rust on the Internet, so I’m going to start by enumerating some of the shortcomings I’ve encountered.

“Line Noise” Syntax

This is a superficial complaint, but I found Rust syntax to be dense, heavy, and difficult to read, like trying to read the output of a UART with line noise:

Trying::to_read::<&'a heavy>(syntax, |like| { this. can_be( maddening ) }).map(|_| ())?;

In more plain terms, the line above does something like invoke a method called “to_read” on the object (actually `struct`) “Trying” with a type annotation of “&heavy” and a lifetime of ‘a with the parameters of “syntax” and a closure taking a generic argument of “like” calling the can_be() method on another instance of a structure named “this” with the parameter “maddening” with any non-error return values mapped to the Rust unit type “()” and errors unwrapped and kicked back up to the caller’s scope.

Deep breath. Surely, I got some of this wrong, but you get the idea of how dense this syntax can be.

And then on top of that you can layer macros and directives which don’t have to follow other Rust syntax rules. For example, if you want to have conditionally compiled code, you use a directive like

#[cfg(all(not(baremetal), any(feature = “hazmat”, feature = “debug_print”)))]

Which says if either the feature “hazmat” or “debug_print” is enabled and you’re not running on bare metal, use the block of code below (and I surely got this wrong too). The most confusing part of about this syntax to me is the use of a single “=” to denote equivalence and not assignment, because, stuff in config directives aren’t Rust code. It’s like a whole separate meta-language with a dictionary of key/value pairs that you query.

I’m not even going to get into the unreadability of Rust macros – even after having written a few Rust macros myself, I have to admit that I feel like they “just barely work” and probably thar be dragons somewhere in them. This isn’t how you’re supposed to feel in a language that bills itself to be reliable. Yes, it is my fault for not being smart enough to parse the language’s syntax, but also, I do have other things to do with my life, like build hardware.

Anyways, this is a superficial complaint. As time passed I eventually got over the learning curve and became more comfortable with it, but it was a hard, steep curve to climb. This is in part because all the Rust documentation is either written in eli5 style (good luck figuring out “feature”s from that example), or you’re greeted with a formal syntax definition (technically, everything you need to know to define a “feature” is in there, but nowhere is it summarized in plain English), and nothing in between.

To be clear, I have a lot of sympathy for how hard it is to write good documentation, so this is not a dig at the people who worked so hard to write so much excellent documentation on the language. I genuinely appreciate the general quality and fecundity of the documentation ecosystem.

Rust just has a steep learning curve in terms of syntax (at least for me).

Rust Is Powerful, but It Is Not Simple

Rust is powerful. I appreciate that it has a standard library which features HashMaps, Vecs, and Threads. These data structures are delicious and addictive. Once we got `std` support in Xous, there was no going back. Coming from a background of C and assembly, Rust’s standard library feels rich and usable — I have read some criticisms that it lacks features, but for my purposes it really hits a sweet spot.

That being said, my addiction to the Rust `std` library has not done any favors in terms of building an auditable code base. One of the criticisms I used to leverage at Linux is like “holy cow, the kernel source includes things like an implementation for red black trees, how is anyone going to audit that”.

Now, having written an OS, I have a deep appreciation for how essential these rich, dynamic data structures are. However, the fact that Xous doesn’t include an implementation of HashMap within its repository doesn’t mean that we are any simpler than Linux: indeed, we have just swept a huge pile of code under the rug; just the `collection`s portion of the standard library represents about 10k+ SLOC at a very high complexity.

So, while Rust’s `std` library allows the Xous code base to focus on being a kernel and not also be its own standard library, from the standpoint of building a minimum attack-surface, “fully-auditable by one human” codebase, I think our reliance on Rust’s `std` library means we fail on that objective, especially so long as we continue to track the latest release of Rust (and I’ll get into why we have to in the next section).

Ideally, at some point, things “settle down” enough that we can stick a fork in it and call it done by well, forking the Rust repo, and saying “this is our attack surface, and we’re not going to change it”. Even then, the Rust `std` repo dwarfs the Xous repo by several multiples in size, and that’s not counting the complexity of the compiler itself.

Rust Isn’t Finished

This next point dovetails into why Rust is not yet suitable for a fully auditable kernel: the language isn’t finished. For example, while we were coding Xous, a thing called `const generic` was introduced. Before this, Rust had no native ability to deal with arrays bigger than 32 elements! This limitation is a bit maddening, and even today there are shortcomings such as the `Default` trait being unable to initialize arrays larger than 32 elements. This friction led us to put limits on many things at 32 elements: for example, when we pass the results of an SSID scan between processes, the structure only reserves space for up to 32 results, because the friction of going to a larger, more generic structure just isn’t worth it. That’s a language-level limitation directly driving a user-facing feature.

Also over the course of writing Xous, things like in-line assembly and workspaces finally reached maturity, which means we need to go back a revisit some unholy things we did to make those critical few lines of initial boot code, written in assembly, integrated into our build system.

I often ask myself “when is the point we’ll get off the Rust release train”, and the answer I think is when they finally make “alloc” no longer a nightly API. At the moment, `no-std` targets have no access to the heap, unless they hop on the “nightly” train, in which case you’re back into the Python-esque nightmare of your code routinely breaking with language releases.

We definitely gave writing an OS in `no-std` + stable a fair shake. The first year of Xous development was all done using `no-std`, at a cost in memory space and complexity. It’s possible to write an OS with nothing but pre-allocated, statically sized data structures, but we had to accommodate the worst-case number of elements in all situations, leading to bloat. Plus, we had to roll a lot of our own core data structures.

About a year ago, that all changed when Xobs ported Rust’s `std` library to Xous. This means we are able to access the heap in stable Rust, but it comes at a price: now Xous is tied to a particular version of Rust, because each version of Rust has its own unique version of `std` packaged with it. This version tie is for a good reason: `std` is where the sausage gets made of turning fundamentally `unsafe` hardware constructions such as memory allocation and thread creation into “safe” Rust structures. (Also fun fact I recently learned: Rust doesn’t have a native allocater for most targets – it simply punts to the native libc `malloc()` and `free()` functions!) In other words, Rust is able to make a strong guarantee about the stable release train not breaking old features in part because of all the loose ends swept into `std`.

I have to keep reminding myself that having `std` doesn’t eliminate the risk of severe security bugs in critical code – it merely shuffles a lot of critical code out of sight, into a standard library. Yes, it is maintained by a talented group of dedicated programmers who are smarter than me, but in the end, we are all only human, and we are all fair targets for software supply chain exploits.

Rust has a clockwork release schedule – every six weeks, it pushes a new version. And because our fork of `std` is tied to a particular version of Rust, it means every six weeks, Xobs has the thankless task of updating our fork and building a new `std` release for it (we’re not a first-class platform in Rust, which means we have to maintain our own `std` library). This means we likewise force all Xous developers to run `rustup update` on their toolchains so we can retain compatibility with the language.

This probably isn’t sustainable. Eventually, we need to lock down the code base, but I don’t have a clear exit strategy for this. Maybe the next point at which we can consider going back to `nostd` is when we can get the stable `alloc` feature, which allows us to have access to the heap again. We could then decouple Xous from the Rust release train, but we’d still need to backfill features such as Vec, HashMap, Thread, and Arc/Mutex/Rc/RefCell/Box constructs that enable Xous to be efficiently coded.

Unfortunately, the `alloc` crate is very hard, and has been in development for many years now. That being said, I really appreciate the transparency of Rust behind the development of this feature, and the hard work and thoughtfulness that is being put into stabilizing this feature.

Rust Has A Limited View of Supply Chain Security

I think this position is summarized well by the installation method recommended by the rustup.rs installation page:

`curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh`

“Hi, run this shell script from a random server on your machine.”

To be fair, you can download the script and inspect it before you run it, which is much better than e.g. the Windows .MSI installers for vscode. However, this practice pervades the entire build ecosystem: a stub of code called `build.rs` is potentially compiled and executed whenever you pull in a new crate from crates.io. This, along with “loose” version pinning (you can specify a version to be, for example, simply “2” which means you’ll grab whatever the latest version published is with a major rev of 2), makes me uneasy about the possibility of software supply chain attacks launched through the crates.io ecosystem.

Crates.io is also subject to a kind of typo-squatting, where it’s hard to determine which crates are “good” or “bad”; some crates that are named exactly what you want turn out to just be old or abandoned early attempts at giving you the functionality you wanted, and the more popular, actively-maintained crates have to take on less intuitive names, sometimes differing by just a character or two from others (to be fair, this is not a problem unique to Rust’s package management system).

There’s also the fact that dependencies are chained – when you pull in one thing from crates.io, you also pull in all of that crate’s subordinate dependencies, along with all their build.rs scripts that will eventually get run on your machine. Thus, it is not sufficient to simply audit the crates explicitly specified within your Cargo.toml file — you must also audit all of the dependent crates for potential supply chain attacks as well.

Fortunately, Rust does allow you to pin a crate at a particular version using the `Cargo.lock` file, and you can fully specify a dependent crate down to the minor revision. We try to mitigate this in Xous by having a policy of publishing our Cargo.lock file and specifying all of our first-order dependent crates to the minor revision. We have also vendored in or forked certain crates that would otherwise grow our dependency tree without much benefit.

That being said, much of our debug and test framework relies on some rather fancy and complicated crates that pull in a huge number of dependencies, and much to my chagrin even when I try to run a build just for our target hardware, the dependent crates for running simulations on the host computer are still pulled in and the build.rs scripts are at least built, if not run.

In response to this, I wrote a small tool called `crate-scraper` which downloads the source package for every source specified in our Cargo.toml file, and stores them locally so we can have a snapshot of the code used to build a Xous release. It also runs a quick “analysis” in that it searches for files called build.rs and collates them into a single file so I can more quickly grep through to look for obvious problems. Of course, manual review isn’t a practical way to detect cleverly disguised malware embedded within the build.rs files, but it at least gives me a sense of the scale of the attack surface we’re dealing with — and it is breathtaking, about 5700 lines of code from various third parties that manipulates files, directories, and environment variables, and runs other programs on my machine every time I do a build.

I’m not sure if there is even a good solution to this problem, but, if you are super-paranoid and your goal is to be able to build trustable firmware, be wary of Rust’s expansive software supply chain attack surface!

You Can’t Reproduce Someone Else’s Rust Build

A final nit I have about Rust is that builds are not reproducible between different computers (they are at least reproducible between builds on the same machine if we disable the embedded timestamp that I put into Xous for $reasons).

I think this is primarily because Rust pulls in the full path to the source code as part of the panic and debug strings that are built into the binary. This has lead to uncomfortable situations where we have had builds that worked on Windows, but failed under Linux, because our path names are very different lengths on the two and it would cause some memory objects to be shifted around in target memory. To be fair, those failures were all due to bugs we had in Xous, which have since been fixed. But, it just doesn’t feel good to know that we’re eventually going to have users who report bugs to us that we can’t reproduce because they have a different path on their build system compared to ours. It’s also a problem for users who want to audit our releases by building their own version and comparing the hashes against ours.

There’s some bugs open with the Rust maintainers to address reproducible builds, but with the number of issues they have to deal with in the language, I am not optimistic that this problem will be resolved anytime soon. Assuming the only driver of the unreproducibility is the inclusion of OS paths in the binary, one fix to this would be to re-configure our build system to run in some sort of a chroot environment or a virtual machine that fixes the paths in a way that almost anyone else could reproduce. I say “almost anyone else” because this fix would be OS-dependent, so we’d be able to get reproducible builds under, for example, Linux, but it would not help Windows users where chroot environments are not a thing.

Where Rust Exceeded Expectations

Despite all the gripes laid out here, I think if I had to do it all over again, Rust would still be a very strong contender for the language I’d use for Xous. I’ve done major projects in C, Python, and Java, and all of them eventually suffer from “creeping technical debt” (there’s probably a software engineer term for this, I just don’t know it). The problem often starts with some data structure that I couldn’t quite get right on the first pass, because I didn’t yet know how the system would come together; so in order to figure out how the system comes together, I’d cobble together some code using a half-baked data structure.

Thus begins the descent into chaos: once I get an idea of how things work, I go back and revise the data structure, but now something breaks elsewhere that was unsuspected and subtle. Maybe it’s an off-by-one problem, or the polarity of a sign seems reversed. Maybe it’s a slight race condition that’s hard to tease out. Nevermind, I can patch over this by changing a <= to a <, or fixing the sign, or adding a lock: I’m still fleshing out the system and getting an idea of the entire structure. Eventually, these little hacks tend to metastasize into a cancer that reaches into every dependent module because the whole reason things even worked was because of the “cheat”; when I go back to excise the hack, I eventually conclude it’s not worth the effort and so the next best option is to burn the whole thing down and rewrite it…but unfortunately, we’re already behind schedule and over budget so the re-write never happens, and the hack lives on.

Rust is a difficult language for authoring code because it makes these “cheats” hard – as long as you have the discipline of not using “unsafe” constructions to make cheats easy. However, really hard does not mean impossible – there were definitely some cheats that got swept under the rug during the construction of Xous.

This is where Rust really exceeded expectations for me. The language’s structure and tooling was very good at hunting down these cheats and refactoring the code base, thus curing the cancer without killing the patient, so to speak. This is the point at which Rust’s very strict typing and borrow checker converts from a productivity liability into a productivity asset.

I liken it to replacing a cable in a complicated bundle of cables that runs across a building. In Rust, it’s guaranteed that every strand of wire in a cable chase, no matter how complicated and awful the bundle becomes, is separable and clearly labeled on both ends. Thus, you can always “pull on one end” and see where the other ends are by changing the type of an element in a structure, or the return type of a method. In less strictly typed languages, you don’t get this property; the cables are allowed to merge and affect each other somewhere inside the cable chase, so you’re left “buzzing out” each cable with manual tests after making a change. Even then, you’re never quite sure if the thing you replaced is going to lead to the coffee maker switching off when someone turns on the bathroom lights.

Here’s a direct example of Rust’s refactoring abilities in action in the context of Xous. I had a problem in the way trust levels are handled inside our graphics subsystem, which I call the GAM (Graphical Abstraction Manager). Each Canvas in the system gets a `u8` assigned to it that is a trust level. When I started writing the GAM, I just knew that I wanted some notion of trustability of a Canvas, so I added the variable, but wasn’t quite sure exactly how it would be used. Months later, the system grew the notion of Contexts with Layouts, which are multi-Canvas constructions that define a particular type of interaction. Now, you can have multiple trust levels associated with a single Context, but I had forgotten about the trust variable I had previously put in the Canvas structure – and added another trust level number to the Context structure as well. You can see where this is going: everything kind of worked as long as I had simple test cases, but as we started to get modals popping up over applications and then menus on top of modals and so forth, crazy behavior started manifesting, because I had confused myself over where the trust values were being stored. Sometimes I was updating the value in the Context, sometimes I was updating the one in the Canvas. It would manifest itself sometimes as an off-by-one bug, other times as a concurrency error.

This was always a skeleton in the closet that bothered me while the GAM grew into a 5k-line monstrosity of code with many moving parts. Finally, I decided something had to be done about it, and I was really not looking forward to it. I was assuming that I messed up something terribly, and this investigation was going to conclude with a rewrite of the whole module.

Fortunately, Rust left me a tiny string to pull on. Clippy, the cheerfully named “linter” built into Rust, was throwing a warning that the trust level variable was not being used at a point where I thought it should be – I was storing it in the Context after it was created, but nobody every referred to it after then. That’s strange – it should be necessary for every redraw of the Context! So, I started by removing the variable, and seeing what broke. This rapidly led me to recall that I was also storing the trust level inside the Canvases within the Context when they were being created, which is why I had this dangling reference. Once I had that clue, I was able to refactor the trust computations to refer only to that one source of ground truth. This also led me to discover other bugs that had been lurking because in fact I was never exercising some code paths that I thought I was using on a routine basis. After just a couple hours of poking around, I had a clear-headed view of how this was all working, and I had refactored the trust computation system with tidy APIs that were simple and easier to understand, without having to toss the entire code base.

This is just one of many positive experiences I’ve had with Rust in maintaining the Xous code base. It’s one of the first times I’ve walked into a big release with my head up and a positive attitude, because for the first time ever, I feel like maybe I have a chance of being able deal with hard bugs in an honest fashion. I’m spending less time making excuses in my head to justify why things were done this way and why we can’t take that pull request, and more time thinking about all the ways things can get better, because I know Clippy has my back.

Caveat Coder

Anyways, that’s a lot of ranting about software for a hardware guy. Software people are quick to remind me that first and foremost, I make circuits and aluminum cases, not code, therefore I have no place ranting about software. They’re right – I actually have no “formal” training to write code “the right way”. When I was in college, I learned Maxwell’s equations, not algorithms. I could never be a professional programmer, because I couldn’t pass even the simplest coding interview. Don’t ask me to write a linked list: I already know that I don’t know how to do it correctly; you don’t need to prove that to me. This is because whenever I find myself writing a linked list (or any other foundational data structure for that matter), I immediately stop myself and question all the life choices that brought me to that point: isn’t this what libraries are for? Do I really need to be re-inventing the wheel? If there is any correlation between doing well in a coding interview and actual coding ability, then you should definitely take my opinions with the grain of salt.

Still, after spending a couple years in the foxhole with Rust and reading countless glowing articles about the language, I felt like maybe a post that shared some critical perspectives about the language would be a refreshing change of pace.