Author Archives: jejb

Measuring the Horizontal Attack Profile of Nabla Containers

One of the biggest problems with the current debate about Container vs Hypervisor security is that no-one has actually developed a way of measuring security, so the debate is all in qualitative terms (hypervisors “feel” more secure than containers because of the interface breadth) but no-one actually has done a quantitative comparison.  The purpose of this blog post is to move the debate forwards by suggesting a quantitative methodology for measuring the Horizontal Attack Profile (HAP).  For more details about Attack Profiles, see this blog post.  I don’t expect this will be the final word in the debate, but by describing how we did it I hope others can develop quantitative measurements as well.

Well begin by looking at the Nabla technology through the relatively uncontroversial metric of performance.  In most security debates, it’s acceptable that some performance is lost by securing the application.  As a rule of thumb, placing an application in a hypervisor loses anywhere between 10-30% of the native performance.  Our goal here is to show that, for a variety of web tasks, the Nabla containers mechanism has an acceptable performance penalty.

Performance Measurements

We took some standard benchmarks: redis-bench-set, redis-bench-get, python-tornado and node-express and in the latter two we loaded up the web servers with simple external transactional clients.  We then performed the same test for docker, gVisor, Kata Containers (as our benchmark for hypervisor containment) and nabla.  In all the figures, higher is better (meaning more throughput):

The red Docker measure is included to show the benchmark.  As expected, the Kata Containers measure is around 10-30% down on the docker one in each case because of the hypervisor penalty.  However, in each case the Nabla performance is the same or higher than the Kata one, showing we pay less performance overhead for our security.  A final note is that since the benchmarks are network ones, there’s somewhat of a penalty paid by userspace networking stacks (which nabla necessarily has) for plugging into docker network, so we show two values, one for the bridging plug in (nabla-containers) required to orchestrate nabla with kubernetes and one as a direct connection (nabla-raw) showing where the performance would be without the network penalty.

One final note is that, as expected, gVisor sucks because ptrace is a really inefficient way of connecting the syscalls to the sandbox.  However, it is more surprising that gVisor-kvm (where the sandbox connects to the system calls of the container using hypercalls instead) is also pretty lacking in performance.  I speculate this is likely because hypercalls exact their own penalty and hypervisors usually try to minimise them, which using them to replace system calls really doesn’t do.

HAP Measurement Methodology

The Quantitative approach to measuring the Horizontal Attack Profile (HAP) says that we take the bug density of the Linux Kernel code  and multiply it by the amount of unique code traversed by the running system after it has reached a steady state (meaning that it doesn’t appear to be traversing any new kernel paths). For the sake of this method, we assume the bug density to be uniform and thus the HAP is approximated by the amount of code traversed in the steady state.  Measuring this for a running system is another matter entirely, but, fortunately, the kernel has a mechanism called ftrace which can be used to provide a trace of all of the functions called by a given userspace process and thus gives a reasonable approximation of the number of lines of code traversed (note this is an approximation because we measure the total number of lines in the function taking no account of internal code flow, primarily because ftrace doesn’t give that much detail).  Additionally, this methodology works very well for containers where all of the control flow emanates from a well known group of processes via the system call information, but it works less well for hypervisors where, in addition to the direct hypercall interface, you also have to add traces from the back end daemons (like the kvm vhost kernel threads or dom0 in the case of Xen).

HAP Results

The results are for the same set of tests as the performance ones except that this time we measure the amount of code traversed in the host kernel:

As stated in our methodology, the height of the bar should be directly proportional to the HAP where lower is obviously better.  On these results we can say that in all cases the Nabla runtime tender actually has a better HAP than the hypervisor contained Kata technology, meaning that we’ve achieved a container system with better HAP (i.e. more secure) than hypervisors.

Some of the other results in this set also bear discussing.  For instance the Docker result certainly isn’t 10x the Kata result as a naive analysis would suggest.  In fact, the containment provided by docker looks to be only marginally worse than that provided by the hypervisor.  Given all the hoopla about hypervisors being much more secure than containers this result looks surprising but you have to consider what’s going on: what we’re measuring in the docker case is the system call penetration of normal execution of the systems.  Clearly anything malicious could explode this result by exercising all sorts of system calls that the application doesn’t normally use.  However, this does show clearly that a docker container with a well crafted seccomp profile (which blocks unexpected system calls) provides roughly equivalent security to a hypervisor.

The other surprising result is that, in spite of their claims to reduce the exposure to Linux System Calls, gVisor actually is either equivalent to the docker use case or, for the python tornado test, significantly worse than the docker case.  This too is explicable in terms of what’s going on under the covers: gVisor tries to improve containment by rewriting the Linux system call interface in Go.  However, no-one has paid any attention to the amount of system calls the Go runtime is actually using, which is what these results are really showing.  Thus, while current gVisor doesn’t currently achieve any containment improvement on this methodology, it’s not impossible to write a future version of the Go runtime that is much less profligate in the way it uses system calls by developing a Secure Go using the same methodology we used to develop Nabla.

Conclusions

On both tests, Nabla is far and away the best containment technology for secure workloads given that it sacrifices the least performance over docker to achieve the containment and, on the published results, is 2x more secure even than using hypervisor based containment.

Hopefully these results show that it is perfectly possible to have containers that are more secure than hypervisors and lays to rest, finally, the arguments about which is the more secure technology.  The next step, of course, is establishing the full extent of exposure to a malicious application and to do that, some type of fuzz testing needs to be employed.  Unfortunately, right at the moment, gVisor is simply crashing when subjected to fuzz testing, so it needs to become more robust before realistic measurements can be taken.

A New Method of Containment: IBM Nabla Containers

In the previous post about Containers and Cloud Security, I noted that most of the tenants of a Cloud Service Provider (CSP) could safely not worry about the Horizontal Attack Profile (HAP) and leave the CSP to manage the risk.  However, there is a small category of jobs (mostly in the financial and allied industries) where the damage done by a Horizontal Breach of the container cannot be adequately compensated by contractual remedies.  For these cases, a team at IBM research has been looking at ways of reducing the HAP with a view to making containers more secure than hypervisors.  For the impatient, the full open source release of the Nabla Containers technology is here and here, but for the more patient, let me explain what we did and why.  We’ll have a follow on post about the measurement methodology for the HAP and how we proved better containment than even hypervisor solutions.

The essence of the quest is a sandbox that emulates the interface between the runtime and the kernel (usually dubbed the syscall interface) with as little code as possible and a very narrow interface into the kernel itself.

The Basics: Looking for Better Containment

The HAP attack worry with standard containers is shown on the left: that a malicious application can breach the containment wall and attack an innocent application.  This attack is thought to be facilitated by the breadth of the syscall interface in standard containers so the guiding star in developing Nabla Containers was a methodology for measuring the reduction in the HAP (and hence the improvement in containment), but the initial impetus came from the observation that unikernel systems are nicely modular in the libOS approach, can be used to emulate systemcalls and, thanks to rumprun, have a wide set of support for modern web friendly languages (like python, node.js and go) with a fairly thin glue layer.  Additionally they have a fairly narrow set of hypercalls that are actually used in practice (meaning they can be made more secure than conventional hypervisors).  Code coverage measurements of standard unikernel based kvm images confirmed that they did indeed use a far narrower interface.

Replacing the Hypervisor Interface

One of the main elements of the hypervisor interface is the transition from a less privileged guest kernel to a more privileged host one via hypercalls and vmexits.  These CPU mediated events are actually quite expensive, certainly a lot more expensive than a simple system call, which merely involves changing address space and privilege level.  It turns out that the unikernel based kvm interface is really only nine hypercalls, all of which are capable of being rewritten as syscalls, so the approach to running this new sandbox as a container is to do this rewrite and seccomp restrict the interface to being only what the rewritten unikernel runtime actually needs (meaning that the seccomp profile is now CSP enforced).  This vision, by the way, of a broad runtime above being mediated to a narrow interface is where the name Nabla comes from: The symbol for Nabla is an inverted triangle (∇) which is broad at the top and narrows to a point at the base.

Using this formulation means that the nabla runtime (or nabla tender) can be run as a single process within a standard container and the narrowness of the interface to the host kernel prevents most of the attacks that a malicious application would be able to perform.

DevOps and the ParaVirt conundrum

Back at the dawn of virtualization, there were arguments between Xen and VMware over whether a hypervisor should be fully virtual (capable of running any system supported by the virtual hardware description) or paravirtual (the system had to be modified to run on the virtualization system and thus would be incapable of running on physical hardware).  Today, thanks in a large part to CPU support for virtualization primtives, fully paravirtual systems have long since gone the way of the dodo and everyone nowadays expects any OS running on a hypervisor to be capable of running on physical hardware1.  The death of paravirt also left the industry with an aversion to ever reviving it, which explains why most sandbox containment systems (gVisor, Kata) try to require no modifications to the image.

With DevOps, the requirement is that images be immutable and that to change an image you must take it through the full develop build, test, deploy cycle.  This development centric view means that, provided there’s no impact to the images you use as the basis for your development, you can easily craft your final image to suit the deployment environment, which means a step like linking with the nabla tender is very easy.  Essentially, this comes down to whether you take the Dev (we can rebuild to suit the environment) or the Ops (the deployment environment needs to accept arbitrary images) view.  However, most solutions take the Ops view because of the anti-paravirt bias.  For the Nabla tender, we take the Dev view, which is born out by the performance figures.

Conclusion

Like most sandbox models, the Nabla containers approach is an alternative to namespacing for containment, but it still requires cgroups for resource management.  The figures show that the containment HAP is actually better than that achieved with a hypervisor and the performance, while being marginally less than a namespaced container, is greater than that obtained by running a container inside a hypervisor.  Thus we conclude that for tenants who have a real need for HAP reduction, this is a viable technology.

Containers and Cloud Security

Introduction

The idea behind this blog post is to take a new look at how cloud security is measured and what its impact is on the various actors in the cloud ecosystem.  From the measurement point of view, we look at the vertical stack: all code that is traversed to provide a service all the way from input web request to database update to output response potentially contains bugs; the bug density is variable for the different components but the more code you traverse the higher your chance of exposure to exploitable vulnerabilities.  We’ll call this the Vertical Attack Profile (VAP) of the stack.  However, even this axis is too narrow because the primary actors are the cloud tenant and the cloud service provider (CSP).  In an IaaS cloud, part of the vertical profile belongs to the tenant (The guest kernel, guest OS and application) and part (the hypervisor and host OS) belong to the CSP.  However, the CSP vertical has the additional problem that any exploit in this piece of the stack can be used to jump into either the host itself or any of the other tenant virtual machines running on the host.  We’ll call this exploit causing a failure of containment the Horizontal Attack Profile (HAP).  We should also note that any Horizontal Security failure is a potentially business destroying event for the CSP, so they care deeply about preventing them.  Conversely any exploit occurring in the VAP owned by the Tenant can be seen by the CSP as a tenant only problem and one which the Tenant is responsible for locating and fixing.  We correlate size of profile with attack risk, so the large the profile the greater the probability of being exploited.

From the Tenant point of view, improving security can be done in one of two ways, the first (and mostly aspirational) is to improve the security and monitoring of the part of the Vertical the Tenant is responsible for and the second is to shift responsibility to the CSP, so make the CSP responsible for more of the Vertical.  Additionally, for most Tenants, a Horizontal failure mostly just means they lose trust in the CSP, unless the Tenant is trusting the CSP with sensitive data which can be exfiltrated by the Horizontal exploit.  In this latter case, the Tenant still cannot do anything to protect the CSP part of the Security Profile, so it’s mostly a contractual problem: SLAs and penalties for SLA failures.

Examples

To see how these interpretations apply to the various cloud environments, lets look at some of the Cloud (and pre-Cloud) models:

Physical Infrastructure

The left hand diagram shows a standard IaaS rented physical system.  Since the Tenant rents the hardware it is shown as red indicating CSP ownership and the the two Tenants are shown in green and yellow.  In this model, barring attacks from the actual hardware, the Tenant owns the entirety of the VAP.  The nice thing for the CSP is that hardware provides air gap security, so there is no HAP which means it is incredibly secure.

However, there is another (much older) model shown on the right, called the shared login model,  where the Tenant only rents a login on the physical system.  In this model, only the application belongs to the Tenant, so the CSP is responsible for much of the VAP (the expanded red area).  Here the total VAP is the same, but the Tenant’s VAP is much smaller: the CSP is responsible for maintaining and securing everything apart from the application.  From the Tenant point of view this is a much more secure system since they’re responsible for much less of the security.  From the CSP point of view there is now a  because a tenant compromising the kernel can control the entire system and jump to other tenant processes.  This actually has the worst HAP of all the systems considered in this blog.

Hypervisor based Virtual Infrastructure

In this model, the total VAP is unquestionably larger (worse) than the physical system above because there’s simply more code to traverse (a guest and a host kernel).  However, from the Tenant’s point of view, the VAP should be identical to that of unshared physical hardware because the CSP owns all the additional parts.  However, there is the possibility that the Tenant may be compromised by vulnerabilities in the Virtual Hardware Emulation.  This can be a worry because an exploit here doesn’t lead to a Horizontal security problem, so the CSP is apt to pay less attention to vulnerabilities in the Virtual Hardware simply because each guest has its own copy (even though that copy is wholly under the control of the CSP).

The HAP is definitely larger (worse) than the physical host because of the shared code in the Host Kernel/Hypervisor, but it has often been argued that because this is so deep in the Vertical stack that the chances of exploit are practically zero (although venom gave the lie to this hope: stack depth represents obscurity, not security).

However, there is another way of improving the VAP and that’s to reduce the number of vulnerabilities that can be hit.  One way that this can be done is to reduce the bug density (the argument for rewriting code in safer languages) but another is to restrict the amount of code which can be traversed by narrowing the interface (for example, see arguments in this hotcloud paper).  On this latter argument, the host kernel or hypervisor does have a much lower VAP than the guest kernel because the hypercall interface used for emulating the virtual hardware is very narrow (much narrower than the syscall interface).

The important takeaways here are firstly that simply transferring ownership of elements in the VAP doesn’t necessarily improve the Tenant VAP unless you have some assurance that the CSP is actively monitoring and fixing them.  Conversely, when the threat is great enough (Horizontal Exploit), you can trust to the natural preservation instincts of the CSP to ensure correct monitoring and remediation because a successful Horizontal attack can be a business destroying event for the CSP.

Container Based Virtual Infrastructure

The total VAP here is identical to that of physical infrastructure.  However, the Tenant component is much smaller (the kernel accounting for around 50% of all vulnerabilities).  It is this reduction in the Tenant VAP that makes containers so appealing: the CSP is now responsible for monitoring and remediating about half of the physical system VAP which is a great improvement for the Tenant.  Plus when the CSP remediates on the host, every container benefits at once, which is much better than having to crack open every virtual machine image to do it.  Best of all, the Tenant images don’t have to be modified to benefit from these fixes, simply running on an updated CSP host is enough.  However, the cost for this is that the HAP is the entire linux kernel syscall interface meaning the HAP is much larger than then hypervisor virtual infrastructure case because the latter benefits from interface narrowing to only the hypercalls (qualitatively, assuming the hypercall interface is ~30 calls and the syscall interface is ~300 calls, then the HAP is 10x larger in the container case than the hypervisor case); however, thanks to protections from the kernel namespace code, the HAP is less than the shared login server case.  Best of all, from the Tenant point of view, this entire HAP cost is borne by the CSP, which makes this an incredible deal: not only does the Tenant get a significant reduction in their VAP but the CSP is hugely motivated to keep on top of all vulnerabilities in their part of the VAP and remediate very fast because of the business implications of a successful horizontal attack.  The flip side of this is that a large number of the world’s CSPs are very unhappy about this potential risks and costs and actually try to shift responsibility (and risk) back to the Tenant by advocating nested virtualization solutions like running containers in hypervisors. So remember, you’re only benefiting from the CSP motivation to actively maintain their share of the VAP if your CSP runs bare metal containers because otherwise they’ve quietly palmed the problem back off on you.

Other Avenues for Controlling Attack Profiles

The assumption above was that defect density per component is roughly constant, so effectively the more code the more defects.  However, it is definitely true that different code bases have different defect densities, so one way of minimizing your VAP is to choose the code you rely on carefully and, of course, follow bug reduction techniques in the code you write.

Density Reduction

The simplest way of reducing defects is to find and fix the ones in the existing code base (while additionally being careful about introducing new ones).  This means it is important to know how actively defects are being searched for and how quickly they are being remediated.  In general, the greater the user base for the component, the greater the size of the defect searchers and the faster the speed of their remediation, which means that although the Linux Kernel is a big component in the VAP and HAP, a diligent patch routine is a reasonable line of defence because a fixed bug is not an exploitable bug.

Another way of reducing defect density is to write (or rewrite) the component in a language which is less prone to exploitable defects.  While this approach has many advocates, particularly among language partisans, it suffers from the defect decay issue: the idea that the maximum number of defects occurs in freshly minted code and the number goes down over time because the more time from release the more chance they’ve been found.  This means that a newly rewritten component, even in a shiny bug reducing language, can still contain more bugs than an older component written in a more exploitable language, simply because a significant number of bugs introduced on creation have been found in the latter.

Code Reduction (Minimization Techniques)

It also stands to reason that, for a complex component, simply reducing the amount of code that is accessible to the upper components reduces the VAP because it directly reduces the number of defects.  However, reducing the amount of code isn’t as simple as it sounds: it can only really be done by components that are configurable and then only if you’re not using the actual features you eliminate.  Elimination may be done in two ways, either physically, by actually removing the code from the component or virtually by blocking access using a guard (see below).

Guarding and Sandboxing

Guarding is mostly used to do virtual code elimination by blocking access to certain code paths that the upper layers do not use.  For instance, seccomp  in the Linux Kernel can be used to block access to system calls you know the application doesn’t use, meaning it also blocks any attempt to exploit code that would be in those system calls, thus reducing the VAP (and also reducing the HAP if the kernel is shared).

The deficiencies in the above are obvious: if the application needs to use a system call, you cannot block it although you can filter it, which leads to huge and ever more complex seccomp policies.  The solution for the system call an application has to use problem can sometimes be guarding emulation.  In this mode the guard code actually emulates all the effects of the system call without actually making the actual system call into the kernel.  This approach, often called sandboxing, is certainly effective at reducing the HAP since the guards usually run in their own address space which cannot be used to launch a horizontal attack.  However, the sandbox may or may not reduce the VAP depending on the bugs in the emulation code vs the bugs in the original.  One of the biggest potential disadvantages to watch out for with sandboxing is the fact that the address space the sandbox runs in is often that of the tenant, often meaning the CSP has quietly switched ownership of that component back to the tenant as well.

Conclusions

First and foremost: security is hard.  As a cloud Tenant, you really want to offload as much of it as possible to people who are much more motivated to actually do it than you are (i.e. the Cloud Service Provider).

The complete Vertical Attack Profile of a container bare metal system in the cloud is identical to a physical system and better than a Hypervisor based system; plus the tenant owned portion is roughly 50% of the total VAP meaning that Containers are by far the most secure virtualization technology available today from the Tenant perspective.

The increased Horizontal Attack profile that containers bring should all rightly belong to the Cloud Service Provider.  However, CSPs are apt to shirk this responsibility and try to find creative ways to shift responsibility back to the tenant including spreading misinformation about the container Attack profiles to try to make Tenants demand nested solutions.

Before you, as a Tenant, start worrying about the CSP owned Horizontal Attack Profile, make sure that contractual remedies (like SLAs or Reputational damage to the CSP) would be insufficient to cover the consequences of any data loss that might result from a containment breach.  Also remember that unless you, as the tenant, are under external compliance obligations like HIPPA or PCI, contractual remedies for a containment failure are likely sufficient and you should keep responsibility for the HAP where it belongs: with the CSP.

Why Microsoft is a good steward for GitHub

There seems to be a lot of hysteria going on in various communities that depend on GitHub for their project hosting around the Microsoft acquisition (just look in the comments here and here).  Obviously a lot of social media ink will be expended on this, so I’d just like to explain why as a committed open source developer, I think this will actually be a good thing.

Firstly, it’s very important to remember that git may be open source, but GitHub isn’t: none of the scripts that run the service have much published source code at all.  It may be a closed source hosting infrastructure that a lot of open source projects rely on but that doesn’t make it open source itself.  So why is GitHub not open source?  Well, it all goes back to the business model.  Notwithstanding fantastic market valuations there are lots of companies that play in the open source ecosystem, like GitHub, which struggle to find a sustainable business model (or even revenue).  This leads to a lot of open closed/open type models like GitHub (the reason GitHub keeps the code closed is so they can sell it to other companies for internal source management) or Docker Enterprise.

Secondly, even if GitHub were fully open source, as I’ve argued in my essays about the GPL, to trust a corporate player in the ecosystem, you need to be able to understand fully its business motivation for being there and verify the business goals align with the community ones.  As long as the business motivation is transparent and aligned with the community, you know you can trust it.  However, most of the new supposedly “open source” companies don’t have clear business models at all, which means their business motivation is anything but transparent.  Paradoxically this means that most of the new corporate idols in the open source ecosystem are remarkably untrustworthy because their business model changes from week to week as they struggle to please their venture capitalist overlords.  There’s no way you can get the transparency necessary for open source trust if the company itself doesn’t know what its business model will be next week.

Finally, this means that companies with well established open source business models and motivations that don’t depend on the whims of VCs are much more trustworthy in open source in the long term.  Although it’s a fairly recent convert, Microsoft is now among these because it’s clearly visible how its conversion from desktop to cloud both requires open source and requires Microsoft to play nicely with open source.  The fact that it has a trust deficit from past actions is a bonus because from the corporate point of view it has to be extra vigilant in maintaining its open source credentials.  The clinching factor is that GitHub is now ancillary to Microsoft’s open source strategy, not its sole means of revenue, so lots of previous less community oriented decisions, like keeping the GitHub code closed source, can be revisited in time as Microsoft seeks to gain community trust.

For the record, I should point out that although I have a github account, I host all my code on kernel.org mostly because the GitHub workflow really annoys me, having spent a lot of time trying to deduce commit motivations in a sparse git commit messages which then require delving into github issues and pull requests only to work out that most of the necessary details are in some private slack back channel well away from public view.  Regardless of who owns GitHub, I don’t see this workflow problem changing any time soon, so I’ll be sticking to my current hosting setup.

GPL as the Best Licence – Governance and Philosophy

In the first part I discussed the balancing mechanisms the GPL provides for enabling corporate contributions, giving users a voice and the right one for mutually competing corporations to collaborate on equal terms.  In this part I’ll look at how the legal elements of the GPL licences make it pretty much the perfect one for supporting a community of developers co-operating with corporations and users.

As far as a summary of my talk goes, this series is complete.  However, I’ve been asked to add some elaboration on the legal structure of GPL+DCO contrasted to other CLAs and also include AGPL, so I’ll likely do some other one off posts in the Legal category about this.

Free Software vs Open Source

There have been many definitions of both of these.  Rather than review them, in the spirit of Humpty Dumpty, I’ll give you mine: Free Software, to me, means espousing a set of underlying beliefs about the code (for instance the four freedoms of the FSF).  While this isn’t problematic for many developers (code freedom, of course, is what enables developer driven communities) it is an anathema to most corporations and in particular their lawyers because, generally applied, it would require the release of all software based intellectual property.  Open Source on the other hand, to me, means that you follow all the rules of the project (usually licensing and contribution requirements) but don’t necessarily sign up to the philosophy underlying the project (if there is one; most Open Source projects won’t have one).

Open Source projects are compatible with Corporations because, provided they have some commonality in goals, even a corporation seeking to exploit a market can march a long way with a developer driven community before the goals diverge.  This period of marching together can be extremely beneficial for both the project and the corporation and if corporate priorities change, the corporation can simply stop contributing.  As I have stated before, Community Managers serve an essential purpose in keeping this goal alignment by making the necessary internal business adjustments within a corporation and by explaining the alignment externally.

The irony of the above is that collaborating within the framework of the project, as Open Source encourages, could work just as well for a Free Software project, provided the philosophical differences could be overcome (or overlooked).  In fact, one could go a stage further and theorize that the four freedoms as well as being input axioms to Free Software are, in fact, the generated end points of corporate pursuit of Open Source, so if the Open Source model wins in business, there won’t actually be a discernible difference between Open Source and Free Software.

Licences and Philosophy

It has often been said that the licence embodies the philosophy of the project (I’ve said it myself on more than one occasion, for which I’d now like to apologize).  However, it is an extremely reckless statement because it’s manifestly untrue in the case of GPL.  Neither v2 nor v3 does anything to require that adopters also espouse the four freedoms, although it could be said that the Tivoization Clause of v3, to which the kernel developers objected, goes slightly further down the road of trying to embed philosophy in the licence.  The reason for avoiding this statement is that it’s very easy for an inexperienced corporation (or pretty much any corporate legal counsel with lack of Open Source familiarity) to take this statement at face value and assume adopting the code or the licence will force some sort of viral adoption of a philosophy which is incompatible with their current business model and thus reject the use of reciprocal licences altogether.  Whenever any corporation debates using or contributing to Open Source, there’s inevitably an internal debate and this licence embeds philosophy argument is a powerful weapon for the Open Source opponents.

Equity in Contribution Models

Some licensing models, like those pioneered by Apache, are thought to require a foundation to pass the rights through under the licence: developers (or their corporations) sign a Contributor Licence Agreement (CLA) which basically grants the foundation redistributable licences to both copyrights and patents in the code and then the the Foundation licenses the contribution to the Project under Apache-2.  The net result is the outbound rights (what consumers of the project gets) are Apache-2 but the inbound rights (what contributors are required to give away) are considerably more.  The danger in this model is that control of the foundation gives control of the inbound rights, so who controls the foundation and how control may be transferred forms an important part of the analysis of what happens to contributor rights.  Note that this model is also the basis of open core, with a corporation taking the place of the foundation.

Inequity in the inbound versus the outbound rights creates an imbalance of power within the project between those who possess the inbound rights and everyone else (who only possess the outbound rights) and can damage developer driven communities by creating an alternate power structure (the one which controls the IP rights).  Further, the IP rights tend to be a focus for corporations, so simply joining the controlling entity (or taking a licence from it) instead of actually contributing to the project can become an end goal, thus weakening the technical contributions to the project and breaking the link with end users.

Creating equity in the licensing framework is thus a key to preserving the developer driven nature of a community.  This equity can be preserved by using the Inbound = Outbound principle, first pioneered by Richard Fontana, the essential element being that contributors should only give away exactly the rights that downstream recipients require under the licence.  This structure means there is no need for a formal CLA and instead a model like the Developer Certificate of Origin (DCO) can be used whereby the contributor simply places a statement in the source control of the project itself attesting to giving away exactly the rights required by the licence.  In this model, there’s no requirement to store non-electronic copies of the the contribution attestation (which inevitably seem to get lost), because the source control system used by the project does this.  Additionally, the source browsing functions of the source control system can trace a single line of code back exactly to all the contributor attestations thus allowing fully transparent inspection and independent verification of all the inbound contribution grants.

The Dangers of Foundations

Foundations which have no special inbound contribution rights can still present a threat to the project by becoming an alternate power structure.  In the worst case, the alternate power structure is cemented by the Foundation having a direct control link with the project, usually via some Technical Oversight Committee (TOC).  In this case, the natural Developer Driven nature of the project is sapped by the TOC creating uncertainty over whether a contribution should be accepted or not, so now the object isn’t to enthuse fellow developers, it’s to please the TOC.  The control confusion created by this type of foundation directly atrophies the project.

Even if a Foundation specifically doesn’t create any form of control link with the project, there’s still the danger that a corporation’s marketing department sees joining the Foundation as a way of linking itself with the project without having to engage the engineering department, and thus still causing a weakening in both potential contributions and the link between the project and its end users.

There are specific reasons why projects need foundations (anything requiring financial resources like conferences or grants requires some entity to hold the cash) but they should be driven by the need of the community for a service and not by the need of corporations for an entity.

GPL+DCO as the Perfect Licence and Contribution Framework

Reciprocity is the key to this: the requirement to give back the modifications levels the playing field for corporations by ensuring that they each see what the others are doing.  Since there’s little benefit (and often considerable down side) to hiding modifications and doing a dump at release time, it actively encourages collaboration between competitors on shared features.  Reciprocity also contains patent leakage as we saw in Part 1.  Coupled with the DCO using the Inbound = Outbound principle, means that the Licence and DCO process are everything you need to form an effective and equal community.

Equality enforced by licensing coupled with reciprocity also provides a level playing field for corporate contributors as we saw in part 1, so equality before the community ensures equity among all participants.  Since this is analogous to the equity principles that underlie a lot of the world’s legal systems, it should be no real surprise that it generates the best contribution framework for the project.  Best of all, the model works simply and effectively for a group of contributors without necessity for any more formal body.

Contributions and Commits

Although GPL+DCO can ensure equity in contribution, some human agency is still required to go from contribution to commit.  The application of this agency is one of the most important aspects to the vibrancy of the project and the community.  The agency can be exercised by an individual or a group; however, the composition of the agency doesn’t much matter, what does is that the commit decisions of the agency essentially (and impartially) judge the technical merit of the contribution in relation to the project.

A bad commit agency can be even more atrophying to a community than a Foundation because it directly saps the confidence the community has in the ability of good (or interesting) code to get into the tree.  Conversely, a good agency is simply required to make sound technical decisions about the contribution, which directly preserves the confidence of the community that good code gets into the tree.   As such, the role requires leadership, impartiality and sound judgment rather than any particular structure.

Governance and Enforcement

Governance seems to have many meanings depending on context, so lets narrow it to the rules by which the project is run (this necessarily includes gathering the IP contribution rights) and how they get followed. In a GPL+DCO framework, the only additional governance component required is the commit agency.

However, having rules isn’t sufficient unless you also follow them; in other words you need some sort of enforcement mechanism.  In a non-GPL+DCO system, this usually involves having an elaborate set of sanctions and some sort of adjudication system, which, if not set up correctly, can also be a source of inequity and project atrophy.  In a GPL+DCO system, most of the adjudication system and sanctions can be replaced by copyright law (this was the design of the licence, after all), which means licence enforcement (or at least the threat of it) becomes the enforcement mechanism.  The only aspect of governance this doesn’t cover is the commit agency.  However, with no other formal mechanisms to support its authority, the commit agency depends on the trust of the community to function and could easily be replaced by that community simply forking the tree and trusting a new commit agency.

The two essential corollaries of the above is that enforcement does serve an essential governance purpose in a GPL+DCO ecosystem and lack of a formal power structure keeps the commit agency honest because the community could replace it.

The final thing worth noting is that too many formal rules can also seriously weaken a project by encouraging infighting over rule interpretations, how exactly they should be followed and who did or did not dot the i’s and cross the t’s.  This makes the very lack of formality and lack of a formalised power structure which the GPL+DCO encourages a key strength of the model.

Conclusions

In the first part I concluded that the GPL fostered the best ecosystem between developers, corporations and users by virtue of the essential ecosystem fairness it engenders.  In this part I conclude that formal control structures are actually detrimental to a developer driven community and thus the best structural mechanism is pure GPL+DCO with no additional formality.  Finally I conclude that this lack of ecosystem control is no bar to strong governance, since that can be enforced by any contributor through the copyright mechanism, and the very lack of control is what keeps the commit agency correctly serving the community.

GPL as the best licence – Community, Code and Licensing

This article is the first of  a set supporting the conclusion that the GPL family of copy left licences are the best ones for maintaining a healthy development pace while providing a framework for corporations and users to influence the code base.  It is based on an expansion of the thoughts behind the presentation GPL: The Best Business Licence for Corporate Code at the Compliance Summit 2017 in Yokohama.

A Community of Developers

The standard definition of any group of people building some form of open source software is usually that they’re developers (people with the necessary technical skills to create or contribute to the project).  In pretty much every developer driven community, they’re doing it because they get something out of the project itself (this is the scratch your own itch idea in the Cathedral and the Bazaar): usually because they use the project in some form, but sometimes because they’re fascinated by the ideas it embodies (this latter is really how the Linux Kernel got started because ordinarily a kernel on its own isn’t particularly useful but, for a lot of the developers, the ideas that went into creating unix were enormously fascinating and implementations were completely inaccessible in Europe thanks to the USL vs BSDi lawsuit).

The reason for discussing developer driven communities is very simple: they’re the predominant type of community in open source (Think Linux Kernel, Gnome, KDE etc) which implies that they’re the natural type of community that forms around shared code collaboration.  In this model of interaction, community and code are interlinked: Caring for the code means you also care for the community.  The health of this type of developer community is very easily checked: ask how many contributors would still contribute to the project if they weren’t paid to do it (reduction in patch volume doesn’t matter, just the desire to continue sending patches).  If fewer than 50% of the core contributors would cease contributing if they weren’t paid then the community is unhealthy.

Developer driven communities suffer from three specific drawbacks:

  1. They’re fractious: people who care about stuff tend to spend a lot of time arguing about it.  Usually some form of self organising leadership fixes a significant part of this, but it’s not guaranteed.
  2. Since the code is built by developers for developers (which is why they care about it) there’s no room for users who aren’t also developers in this model.
  3. The community is informal so there’s no organisation for corporations to have a peer relationship with, plus developers don’t tend to trust corporate motives anyway making it very difficult for corporations to join the community.

Trusting Corporations and Involving Users

Developer communities often distrust the motives of corporations because they think corporations don’t care about the code in the same way as developers do.  This is actually completely true: developers care about code for its own sake but corporations care about code only as far as it furthers their business interests.  However, this business interest motivation does provide the basis for trust within the community: as long as the developer community can see and understand the business motivation, they can trust the Corporation to do the right thing; within limits, of course, for instance code quality requirements of developers often conflict with time to release requirements for market opportunity.  This shared interest in the code base becomes the framework for limited trust.

Enter the community manager:  A community manager’s job, when executed properly, is twofold: one is to take corporate business plans and realign them so that some of the corporate goals align with those of useful open source communities and the second is to explain this goal alignment to the relevant communities.  This means that a good community manager never touts corporate “community credentials” but instead explains in terms developers can understand the business reasons why the community and the corporation should work together.  Once the goals are visible and aligned, the developer community will usually welcome the addition of paid corporate developers to work on the code.  Paying for contributions is the most effective path for Corporations to exert significant influence on the community and assurance of goal alignment is how the community understands how this influence benefits the community.

Involving users is another benefit corporations can add to the developer ecosystem.  Users who aren’t developers don’t have the technical skills necessary to make their voices and opinions heard within the developer driven community but corporations, which usually have paying users in some form consuming the packaged code, can respond to user input and could act as a proxy between the user base and the developer community.  For some corporations responding to user feedback which enhances uptake of the product is a natural business goal.  For others, it could be a goal the community manager pushes for within the corporation as a useful goal that would help business and which could be aligned with the developer community.  In either case, as long as the motives and goals are clearly understood, the corporation can exert influence in the community directly on behalf of users.

Corporate Fear around Community Code

All corporations have a significant worry about investing in something which they don’t control. However, these worries become definite fears around community code because not only might it take a significant investment to exert the needed influence, there’s also the possibility that the investment might enable a competitor to secure market advantage.

Another big potential fear is loss of intellectual property in the form of patent grants.  Specifically, permissive licences with patent grants allow any other entity to take the code on which the patent reads, incorporate it into a proprietary code base and then claim the benefit of the patent grant under the licence.  This problem, essentially, means that, unless it doesn’t care about IP leakage (or the benefit gained outweighs the problem caused), no corporation should contribute code to which they own patents under a permissive licence with a patent grant.

Both of these fears are significant drivers of “privatisation”, the behaviour whereby a corporation takes community code but does all of its enhancements and modifications in secret and never contributes them back to the community, under the assumption that bearing the forking cost of doing this as less onerous than the problems above.

GPL is the Key to Allaying these Fears

The IP leak fear is easily allayed: whether the version of GPL that includes an explicit or implicit patent licence, the IP can only leak as far as the code can go and the code cannot be included in a proprietary product because of the reciprocal code release requirements, thus the Corporation always has visibility into how far the IP rights might leak by following licence mandated code releases.

GPL cannot entirely allay the fear of being out competed with your own code but it can, at least, ensure that if a competitor is using a modification of your code, you know about it (as do your competition), so everyone has a level playing field.  Most customers tend to prefer active participants in open code bases, so to be competitive in the market places, corporations using the same code base tend to be trying to contribute actively.  The reciprocal requirements of GPL provide assurance that no-one can go to market with a secret modification of the code base that they haven’t shared with others.  Therefore, although corporations would prefer dominance and control, they’re prepared to settle for a fully level playing field, which the GPL provides.

Finally, from the community’s point of view, reciprocal licences prevent code privatisation (you can still work from a fork, but you must publish it) and thus encourage code sharing which tends to be a key community requirement.

Conclusions

In this first part, I conclude that the GPL, by ensuring fairness between mutually distrustful contributors and stemming IP leaks, can act as a guarantor of a workable code ecosystem for both developers and corporations and, by using the natural desire of corporations to appeal to customers, can use corporations to bridge the gap between user requirements and the developer community.

In the second part of this series, I’ll look at philosophy and governance and why GPL creates self regulating ecosystems which give corporations and users a useful function while not constraining the natural desire of developers to contribute and contrast this with other possible ecosystem models.

Using Elliptic Curve Cryptography with TPM2

One of the most significant advances going from TPM1.2 to TPM2 was the addition of algorithm agility: The ability of TPM2 to work with arbitrary symmetric and asymmetric encryption schemes.  In practice, in spite of this much vaunted agile encryption capability, most actual TPM2 chips I’ve seen only support a small number of asymmetric encryption schemes, usually RSA2048 and a couple of Elliptic Curves.  However, the ability to support any Elliptic Curve at all is a step up from TPM1.2.  This blog post will detail how elliptic curve schemes can be integrated into existing cryptographic systems using TPM2.  However, before we start on the practice, we need at least a tiny swing through the theory of Elliptic Curves.

What is an Elliptic Curve?

An Elliptic Curve (EC) is simply the set of points that lie on the curve in the two dimensional plane (x,y) defined by the equation

y2 = x3 + ax + b

which means that every elliptic curve can be parametrised by two constants a and b.  The set of all points lying on the curve plus a point at infinity is combined with an addition operation to produce an abelian (commutative) group.  The addition property is defined by drawing straight lines between two points and seeing where they intersect the curve (or picking the infinity point if they don’t intersect).  Wikipedia has a nice diagrammatic description of this here.  The infinity point acts as the identity of the addition rule and the whole group is denoted E.

The utility for cryptography is that you can define an integer multiplier operation which is simply the element added to itself n times, so for P ∈ E, you can always find Q ∈ E such that

Q = P + P + P … = n × P

And, since it’s a simple multiplication like operation, it’s very easy to compute Q.  However, given P and Q it is computationally very difficult to get back to n.  In fact, it can be demonstrated mathematically that trying to compute n is equivalent to the discrete logarithm problem which is the mathematical basis for the cryptographic security of RSA.  This also means that EC keys suffer the same (actually more so) problems as RSA keys: they’re not Quantum Computing secure (vulnerable to the Quantum Shor’s algorithm) and they would be instantly compromised if the discrete logarithm problem were ever solved.

Therefore, for any elliptic curve, E, you can choose a known point G ∈ E, select a large integer d and you can compute a point P = d × G.  You can then publish (P, G, E) as your public key knowing it’s computationally infeasible for anyone to derive your private key d.

For instance, Diffie-Hellman key exchange can be done by agreeing (E, G) and getting Alice and Bob to select private keys dA, dB.  Then knowing Bob’s public key PB, Alice can select a random integer r, which she publishes, and compute a key agreement as a secret point on the Elliptic Curve (r dA) × PB.  Bob can derive the same Elliptic Curve point because

(r dA) × PB = (r dA)dB × G = (r dB) dA × G = (r dB) × PA

The agreement is a point on the curve, but you can use an agreed hashing or other mechanism to get from the point to a symmetric key.

Seems simple, but the problem for computing is that we really want to use integers and right at the moment the elliptic curve is defined over all the real numbers, meaning E is of infinite size and involves floating point computations (of rather large precision).

Elliptic Curves over Finite Fields

Fortunately there is a mathematical theory of finite fields, called Galois Theory, which allows us to take the Galois Field over prime number p, which is denoted GF(p), and compute Elliptic Curve points over this field.  This derivation, which is mathematically rather complicated, is denoted E(GF(p)), where every point (x,y) is represented by a pair of integers between 0 and p-1.  There is another theory that says the number of elements in E(GF(p))

n = |E(GF(p))|

is roughly the same size as p, meaning if you choose a 32 bit prime p, you likely have a field over roughly 2^32 elements.  For every point P in E(GF(p)) it is also mathematically proveable that n × P = 0. where 0 is the zero point (which was the infinity point in the real elliptic curve).

This means that you can take any point, G,  in E(GF(p)) and compute a subgroup based on it:

EG = { ∀m ∈ Zn : m × G }

If you’re lucky |EG| = |E(GF(p))| and G is the generator of the entire group.  However, G may only generate a subgroup and you will find |EG| = h|E(GF(p))| where integer h is called the cofactor.  In general you want the cofactor to be small (preferably less than four) for EG to be cryptographically useful.

For a computer’s purposes, EG is the elliptic curve group used for integer arithmetic in the cryptographic algorithms.  The Curve and Generator is then defined by (p, a, b, Gx, Gy, n, h) which are the published parameters of the key (Gx, Gy represent the x and y elements of point G).  You select a random number d as your private key and your public key P = d × G exactly as above, except now P is easy to compute with integer operations.

Problems with Elliptic Curves

Although I stated above that solving P = d × G is equivalent in difficulty to the discrete logarithm problem, that’s not generally true.  If the discrete logarithm problem were solved, then we’d easily be able to compute d for every generator and curve, but it is possible to pick curves for which d can be easily computed without solving the discrete logarithm problem. This is the reason why you should never pick your own curve parameters (even if you think you know what you’re doing) because it’s very easy to choose a compromised curve.  As a demonstration of the difficulty of the problem: each of the major nation state actors, Russia, China and the US, publishes their own curve parameters for use in their own cryptographic EC implementations and each of them thinks the parameters published by the others is compromised in a way that allows the respective national security agencies to derive private keys.  So if nation state actors can’t tell if a curve is compromised or not, you surely won’t be able to either.

Therefore, to be secure in EC cryptography, you pick and existing curve which has been vetted and select some random Generator Point on it.  Of course, if you’re paranoid, that means you won’t be using any of the nation state supplied curves …

Using the TPM2 with Elliptic Curves in Cryptosystems

The initial target for this work was the openssl cryptosystem whose libraries are widely used for deriving other uses (like https in apache or openssh). Originally, when I did the initial TPM2 enabling of openssl as described in this blog post, I added TPM2 as a patch to the existing TPM 1.2 openssl_tpm_engine.  Unfortunately, openssl_tpm_engine seems to be pretty much defunct at this point, so I started my own openssl_tpm2_engine as a separate git tree to begin experimenting with Elliptic Curve keys (if you don’t use git, you can download the tar file here). One of the benefits to running my own source tree is that I can now add a testing infrastructure that makes use of the IBM TPM emulator to make sure that the basic cryptographic operations all work which means that make check functions even when a TPM2 isn’t available.  The current key creation and import algorithms use secured connections to the TPM (to avoid eavesdropping) which means it’s only really possible to construct them using the IBM TSS. To make all of this easier, I’ve set up an openSUSE Build Service repository which is building for all major architectures and the openSUSE and Fedora distributions (ignore the failures, they’re currently induced because the TPM emulator only currently works on 64 bit little endian systems, so make check is failing, but the TPM people at IBM are working on this, so eventually the builds should be complete).

TPM2 itself also has some annoying restrictions.  The biggest of which is that it doesn’t allow you to pass in arbitrary elliptic curve parameters; you may only use elliptic curves which the TPM itself knows.  This will be annoying if you have an existing EC key you’re trying to import because the TPM may reject it as an unknown algorithm.  For instance, openssl can actually compute with arbitrary EC parameters, but has 39 current elliptic curves parametrised by name. By contrast, my Nuvoton TPM2 inside my Dell XPS 13 knows precisely two curves:

jejb@jarvis:~> create_tpm2_key --list-curves
prime256v1
bnp256

However, assuming you’ve picked a compatible curve for your EC private key (and you’ve defined a parent key for the storage hierarchy) you can simply import it to a TPM bound key:

create_tpm2_key -p 81000001 -w key.priv key.tpm

The tool will report an error if it can’t convert the curve parameters to a named elliptic curve known to the TPM

jejb@jarvis:~> openssl genpkey -algorithm EC -pkeyopt ec_paramgen_curve:brainpoolP256r1 > key.priv
jejb@jarvis:~> create_tpm2_key -p 81000001 -w key.priv key.tpm
TPM does not support the curve in this EC key
openssl_to_tpm_public failed with 166
TPM_RC_CURVE - curve not supported Handle number unspecified

You can also create TPM resident private keys simply by specifying the algorithm

create_tpm2_key -p 81000001 --ecc bnp256 key.tpm

Once you have your TPM based EC keys, you can use them to create public keys and certificates.  For instance, you create a self-signed X509 certificate based on the tpm key by

openssl req -new -x509 -sha256  -key key.tpm -engine tpm2 -keyform engine -out my.crt

Why you should use EC keys with the TPM

The initial attraction is the same as for RSA keys: making it impossible to extract your private key from the system.  However, the mathematical calculations for EC keys are much simpler than for RSA keys and don’t involve finding strong primes, so it’s much simpler for the TPM (being a fairly weak calculation machine) to derive private and public EC keys.  For instance the times taken to derive a RSA key from the primary seed and an EC key differ dramatically

jejb@jarvis:~> time tsscreateprimary -hi o -ecc bnp256 -st
Handle 80ffffff

real 0m0.111s
user 0m0.000s
sys 0m0.014s

jejb@jarvis:~> time tsscreateprimary -hi o -rsa -st
Handle 80ffffff

real 0m20.473s
user 0m0.015s
sys 0m0.084s

so for a slow system like the TPM, using EC keys is a significant speed advantage.  Additionally, there are other advantages.  The standard EC Key signature algorithm is a modification of the NIST Digital Signature Algorithm called ECDSA.  However DSA and ECDSA require a cryptographically strong (and secret) random number as Sony found out to their cost in the EC Key compromise of Playstation 3.  The TPM is a good source of cryptographically strong random numbers and if it generates the signature internally, you can be absolutely sure of keeping the input random number secret.

Why you might want to avoid EC keys altogether

In spite of the many advantages described above, EC keys suffer one additional disadvantage over RSA keys in that Elliptic Curves in general are very hot fields of mathematical research so even if the curve you use today is genuinely not compromised, it’s not impossible that a mathematical advance tomorrow will make the curve you chose (and thus all the private keys you generated) vulnerable.  Of course, the same goes for RSA if anyone ever cracks the discrete logarithm problem, but solving that problem would likely be fully published to world acclaim and recognition as a significant contribution to the advancement of number theory.  Discovering an attack on a currently used elliptic curve on the other hand might be better remunerated by offering to sell it privately to one of the national security agencies …

Using letsencrypt certificates with DANE

If, like me, you run your own cloud server, at some point you need TLS certificates to export secure services.  Like a lot of people I object to paying a so called X.509 authority a yearly fee just to get a certificate, so I’ve been using a free startcom one for a while.  With web browsers delisting startcom, I’m unable to get a new usable certificate from them, so I’ve been investigating letsencrypt instead (if you’re in to fun ironies, by the way, you can observe that currently the letsencrypt site isn’t using a letsencrypt certificate, perhaps indicating the administrative difficulty of doing so).

The problem with letsencrypt certificates is that they currently have a 90 day expiry, which means you really need to use an automated tool to keep your TLS certificate current.  Fortunately the EFF has developed such a tool: certbot (they use a letsencrypt certificate for their site, indicating you can have trust that they do know what they’re doing).  However, one of the problems with certbot is that, by default, it generates a new key each time the certificate is renewed.  This isn’t a problem for most people, but if you use DANE records, it causes significant issues.

Why use both DANE and letsencrypt?

The beauty of DANE, as I’ve written before, is that it gives you a much more secure way of identifying your TLS certificate (provided you run DNSSEC).  People verifying your certificate may use DANE as the only verification mechanism (perhaps because they also distrust the X.509 authorities) which means the best practice is to publish a DANE TLSA record for each service and also use an X.509 authority rooted certificate.  That way your site just works for everyone.

The problem here is that being DNS based, DANE records can be cached for a while, so it can take a few days for DANE certificate updates to propagate through the DNS infrastructure. DANE records have an answer for this: they have a mode where the record identifies only the hash of the public key used by the website, not the certificate itself, which means you can change your certificate as much as you want provided you keep the same public/private key pair.  And here’s the rub: if certbot is going to try to give you a new key on each renewal, this isn’t going to work.

The internet society also has posts about this.

Making certbot work with DANE

Fortunately, there is a solution: the certbot manual mode (certonly) takes a –csr flag which allows you to construct your own certificate request to send to letsencrypt, meaning you can keep a fixed key … at the cost of not using most of the certbot automation.  So, how do you construct a correct csr for letsencrypt?  Like most free certificates, letsencrypt will only allow you to specify the certificate commonName, which must be a DNS pointer to the actual website.  If you need a certificate that covers multiple sites, all the other sites must be enumerated in the x509 v3 extensions field subjectAltName.  Let’s look at how openssl can generate such a certificate request.  One of the slight problems is that openssl, being a cranky tool, does not allow you to specify a subjectAltName on the command line, so you have to construct a special configuration file for it.  I called mine letsencrypt.conf

[req]
prompt = no
distinguished_name = req_dn
req_extensions = req_ext

[req_dn]
commonName = bedivere.hansenpartnership.com

[req_ext]
subjectAltName=@alt_names

[alt_names]
DNS.1=bedivere.hansenpartnership.com
DNS.2=www.hansenpartnership.com
DNS.3=hansenpartnership.com
DNS.4=blog.hansenpartnership.com

As you can see, I’ve given my canonical server (bedivere) as the common name and then four other subject alt names.  Once you have this config file tailored to your needs, you merely run

openssl req -new -key <mykey.key> -config letsencrypt.conf -out letsencrypt.csr

Where mykey.key is the path to your private key (you need a private key because even though the CSR only contains the public key, it is also signed).  However, once you’ve produced this letsencrypt.csr, you no longer need the private key and, because it’s undated, it will now work forever, meaning the infrastructure you put into place with certbot doesn’t need to be privileged enough to access your private key.  Once this is done, you make sure you have TLSA 3 1 1 records pointing to the hash of your public key (here’s a handy website to generate them for you) and you never need to alter your DANE record again.  Note, by the way that letsencrypt certificates can be used for non-web purposes (I use mine for encrypted SMTP as well), so you’ll need one DANE record for every service you use them for.

Putting it all together

Now that you have your certificate request, depending on what version of certbot you have, you may need it in DER format

openssl req -in letsencrypt.csr -out letsencrypt.der -outform DER

And you’re ready to run the following script from cron

#!/bin/bash
date=$(date +%Y-%m-%d)
dir=/etc/ssl/certs
cert="${dir}/letsencrypt-${date}.crt"
fullchain="${dir}/letsencrypt-${date}.pem"
chain="${dir}/letsencrypt-chain-${date}.pem"
csr=/etc/ssl/letsencrypt.der
out=/tmp/certbot.out

##
# certbot handling
#
# first it cannot replace certs, so ensure new locations (date suffix)
# each time mean the certificate is unique each time.  Next, it's
# really chatty, so the only way to tell if there was a failure is to
# check whether the certificates got updated and then get cron to
# email the log
##

certbot certonly --webroot --csr ${csr} --preferred-challenges http-01 -w /var/www --fullchain-path ${fullchain} --chain-path ${chain} --cert-path ${cert} > ${out} 2>&1

if [ ! -f ${fullchain} -o ! -f ${chain} -o ! -f ${cert} ]; then
    cat ${out}
    exit 1;
fi

# link into place

# cert only (apache needs)
ln -sf ${cert} ${dir}/letsencrypt.crt
# cert with chain (stunnel needs)
ln -sf ${fullchain} ${dir}/letsencrypt.pem
# chain only (apache needs)
ln -sf ${chain} ${dir}/letsencrypt-chain.pem

# reload the services
sudo systemctl reload apache2
sudo systemctl restart stunnel4
sudo systemctl reload postfix

Note that this script needs the ability to write files and create links in /etc/ssl/certs (can be done by group permission) and the systemctl reloads need the following in /etc/sudoers

%LimitedAdmins ALL=NOPASSWD: /bin/systemctl reload apache2
%LimitedAdmins ALL=NOPASSWD: /bin/systemctl reload postfix
%LimitedAdmins ALL=NOPASSWD: /bin/systemctl restart stunnel4

And finally you can run this as a cron script under whichever user you’ve chosen to have sufficient privilege to write the certificates.  I run this every month, so I know I if anything goes wrong I have at least two months to fix it.

Oh, and just in case you need proof that I got this all working, here you are!

TPM2 and Linux

Recently Microsoft started mandating TPM2 as a hardware requirement for all platforms running recent versions of windows.  This means that eventually all shipping systems (starting with laptops first) will have a TPM2 chip.  The reason this impacts Linux is that TPM2 is radically different from its predecessor TPM1.2; so different, in fact, that none of the existing TPM1.2 software on Linux (trousers, the libtpm.so plug in for openssl, even my gnome keyring enhancements) will work with TPM2.  The purpose of this blog is to explore the differences and how we can make ready for the transition.

What are the Main 1.2 vs 2.0 Differences?

The big one is termed Algorithm Agility.  TPM1.2 had SHA1 and RSA2048 only.  TPM2 is designed to have many possible algorithms, including support for elliptic curve and a host of government mandated (Russian and Chinese) crypto systems.  There’s no requirement for any shipping TPM2 to support any particular algorithms, so you actually have to ask your TPM what it supports.  The bedrock for TPM2 in the West seems to be RSA1024-2048, ECC and AES for crypto and SHA1 and SHA256 for hashes1.

What algorithm agility means is that you can no longer have root keys (EK and SRK see here for details) like TPM1.2 did, because a key requires a specific crypto algorithm.  Instead TPM2 has primary “seeds” and a Key Derivation Function (KDF).  The way this works is that a seed is simply a long string of random numbers, but it is used as input to the KDF along with the key parameters and the algorithm and out pops a real key based on the seed.  The KDF is deterministic, so if you input the same algorithm and the same parameters you get the same key again.  There are four primary seeds in the TPM2: Three permanent ones which only change when the TPM2 is cleared: endorsement (EPS), Platform (PPS) and Storage (SPS).  There’s also a Null seed, which is used for ephemeral keys and changes every reboot.  A key derived from the SPS can be regarded as the SRK and a key derived from the EPS can be regarded as the EK. Objects descending from these keys are called members of hierarchies2. One of the interesting aspects of the TPM is that the root of a hierarchy is a key not a seed (because you need to exchange secret information with the TPM), and that there can be multiple of these roots with different key algorithms and parameters.

Additionally, the mechanism for making use of keys has changed slightly.  In TPM 1.2 to import a secret key you wrapped it asymmetrically to the SRK and then called LoadKeyByBlob to get a use handle.  In TPM2 this is a two stage operation, firstly you import a wrapped (or otherwise protected) private key with TPM2_Import, but that returns a private key structure encrypted with the parent key’s internal symmetric key.  This symmetrically encrypted key is then loaded (using TPM2_Load) to obtain a use handle whenever needed.  The philosophical change is from online keys in TPM 1.2 (keys which were resident inside the TPM) to offline keys in TPM2 (keys which now can be loaded when needed).  This philosophy has been reinforced by reducing the space available to keep keys loaded in TPM2 (see later).

Playing with TPM2

If you have a recent laptop, chances are you either have or can software upgrade to a TPM2.  I have a dell XPS13 the skylake version which comes with a software upgradeable Nuvoton TPM.  Dell kindly provides a 1.2->2 switching program here, which seems to work under Freedos (odin boot) so I have a physical TPM2 based system.  For those of you who aren’t so lucky, you can still play along, but you need a TPM2 emulator.  The best one is here; simply download and untar it then type make in the src directory and run it as ./tpm_server.  It listens on two TCP ports, 2321 and 2322, for TPM commands so there’s no need to install it anywhere, it can be run directly from the source directory.

After that, you need the interface software called tss2.  The source is here, but Fedora 25 and recent Ubuntu already package it.  I’ve also built openSUSE packages here.  The configuration of tss2 is controlled by environment variables.  The most important one is TPM_INTERFACE_TYPE which tells it how to connect to the TPM2.  If you’re using a simulator, you set this to “socsim” and if you have a real TPM2 device you set it to “dev”.  One final thing about direct device connection: in tss2 there’s no daemon like trousers had to broker the connection, all your users connect directly to the TPM2 device /dev/tpm0.  To do this, the device has to support read and write by arbitrary users, so its permissions need to be 0666.  I’ve got a udev script to achieve this

# tpm 2 devices need to be world readable
SUBSYSTEM=="tpm", ACTION=="add", MODE="0666"

Which goes in /etc/udev/rules.d/80-tpm-2.rules on openSUSE.  The next thing you need to do, if you’re running the simulator, is power it on and start it up (for a real device, this is done by the bios):

tsspowerup
tssstartup

The simulator will now create a NVChip file wherever you started it to store NV ram based objects, which it will read on next start up.  The first thing you need to do is create an SRK and store it in NV memory.  Microsoft uses the well known key handle 81000001 for this, so we’ll do the same.  The reason for doing this is that a real TPM takes ages to run the KDF for RSA keys because it has to look for prime numbers:

jejb@jarvis:~> TPM_INTERFACE_TYPE=socsim time tsscreateprimary -hi o -st -rsa
Handle 80000000
0.03 user 0.00 system 0:00.06 elapsed

jejb@jarvis:~> TPM_INTERFACE_TYPE=dev time tsscreateprimary -hi o -st -rsa
Handle 80000000
0.04 user 0.00 system 0:20.51 elapsed

As you can see: the simulator created a primary storage key (the SRK) in a few milliseconds, but it took my real TPM2 20 seconds to do it3 … not something you want to wait for, hence the need to store this permanently under a well known key handle and get rid of the temporary copy

tssevictcontrol -hi o -ho 80000000 -hp 81000001
tssflushcontext -ha 80000000

tssevictcontrol tells the TPM to copy the key at transient handle 800000004  to permanent NV handle 81000001 and tssflushcontext erases the transient key.  Flushing transient objects is very important, because TPM2 has a lot less transient storage space than TPM1.2 did; usually only about three handles worth.  You can tell how much you have by doing

tssgetcapability -cap 6|grep -i transient
TPM_PT 0000010e value 00000003 TPM_PT_HR_TRANSIENT_MIN - the minimum number of transient objects that can be held in TPM RAM

Where the value (00000003) tells me that the TPM can store at least 3 transient objects.  After that you’ll start getting out of space errors from it.

The final step in taking ownership of a TPM2 is to set the authorization passwords.  Each of the four hierarchies (Null, Owner, Endorsement, Platform) and the Lockout has a possible authority password.  The Platform authority is cleared on startup, so there’s not much point setting it (it’s used by the BIOS or Firmware to perform TPM functions).  Of the other four, you really only need to set Owner, Endorsement and Lockout (I use the same password for all of them).

tsshierarchychangeauth -hi l -pwdn <your password>
tsshierarchychangeauth -hi e -pwdn <your password>
tsshierarchychangeauth -hi o -pwdn <your password>

After this is done, you’re all set.  Note that as well as these authorizations, each object can have its own authorization (or even policy), so the SRK you created earlier still has no password, allowing it to be used by anyone.  Note also that the owner authorization controls access to the NV memory, so you’ll need to supply it now to make other objects persistent.

An Aside about Real TPM2 devices and the Resource Manager

Although I’m using the code below to store my keys in the TPM2, there’s a couple of practical limitations which means it won’t work for you if you have multiple TPM2 using applications without a kernel update.  The two problems are

  1. The Linux Kernel TPM2 device /dev/tpm0 only allows one user at once.  If a second application tries to open the device it will get an EBUSY which causes TSS_Create() to fail.
  2. Because most applications make use of transient key slots and most TPM2s have only a couple of these, simultaneous users can end up running out of these and getting unexpected out of space errors.

The solution to both of these is something called a Resource Manager (RM).  What the RM does is effectively swap transient objects in and out of the TPM as needed to prevent it from running out of space.  Linux has an issue in that both the kernel and userspace are potential users of TPM keys so the resource manager has to live inside the kernel.  Jarkko Sakkinen has preliminary resource manager patches here, and they will likely make it into kernel 4.11 or 4.12.  I’m currently running my laptop with the RM patches applied, so multiple applications work for me, but since these are preliminary patches, I wouldn’t currently advise others to do this.  The way these patches work is that once you declare to the kernel via an ioctl that you want to use the RM, every time you send a command to the TPM, your context gets swapped in, the command is executed, the context is swapped out and the response sent meaning that no other user of the TPM sees your transient objects.  The moment you send the ioctl, the TPM device allows another user to open it as well.

Using TPM2 as a keystore

Once the implementation is sorted out, openssl and gnome-keyring patches can be produced for TPM2.  The only slight wrinkle is that for create_tpm2_key you require a parent key to exist in the NV storage (at the 81000001 handle we talked about previously).  So to convert from a password protected openssh RSA key to a TPM2 based one, you do

create_tpm2_key -a -p 81000001 -w id_rsa id_rsa.tpm
mv id_rsa.tpm id_rsa

And then gnome keyring manager will work nicely (make sure you keep a copy of your original private key in case you want to move to a new laptop or reseed the TPM2).  If you use the same TPM2 password as your original key password, you won’t even need to update the gnome loginkeyring for the new password.

Conclusions

Because of the lack of an in-kernel Resource Manager, TPM2 is ready for experimentation in Linux but definitely not ready for prime time yet (unless you’re willing to patch your kernel).  Hopefully this will change in the 4.11 or 4.12 kernel when the Resource Manager finally goes upstream5.

Looking forwards to the new stack, the lack of a central daemon is really a nice feature: tcsd crashing used to kill all of my TPM key based applications, but with tss2 having no central daemon, everything has just worked(tm) so far.  A kernel based RM also means that the kernel can happily use the TPM (for its trusted keys and disk encryption) without interfering with whatever userspace is doing.

TPM enabling gnome-keyring

One of the questions about the previous post on using your TPM as a secure key store was “could the TPM be used to protect ssh keys?”  The answer is yes, because openssh uses openssl (so you can simply convert an openssh private key to a TPM private key) but the ssh-agent wouldn’t work because ssh-add passes in the private keys by their component primes, which is not possible when the private key is guarded by the TPM.  However,  that made me actually look at gnome-keyring to figure out how it worked and whether it could be used with the TPM.  The answer is yes, but there are also some interesting side effects of TPM enabling gnome-keyring.

Gnome-keyring Architecture

Gnome-keyring consists of essentially three components: a pluggable store backend, a secure passphrase holder (which is implemented as a backend store) and an agent frontend.  The frontend and backend talk to each other using the pkcs11 protocol.  This also means that the backend can serve anything that also speaks pkcs11, which most encryption systems do.  The stores consist of a variety of file or directory backed keys (for instance the ssh-store simply loads all the ssh keys in your $HOME/.ssh; the secret-store uses the gnome default keyring store, $HOME/.local/shared/keyring/,  to store collections of passwords)  The frontends act as bridges between a variety of external protocols and the keyring daemon.  They take whatever external protocol they’re speaking in, convert the request to pkcs11 and query the backends for the information.  The most important frontend is the login one which is called by gnome-keyring-daemon at start of day to unlock the secret-store which contains all your other key passwords using your login password as the key.  The ssh-agent frontend speaks the ssh agent protocol and converts key and signing requests to pkcs11 which is mostly served by the ssh-store.  The gpg-agent store speaks a very cut down version of the gpg agent protocol: basically all it does is allow you to store gpg key passwords in the secret-store; it doesn’t do any cryptographic operations.

Pkcs11 Essentials

Pkcs11 is a highly complex protocol designed for opening sessions which query or operate on tokens, which roughly speaking represent a bundle of objects (If you have a USB crypto key, that’s usually represented as a token and your stored keys as objects).  There is some intermediate stuff about slots, but that mostly applies to tokens which may be offline and need insertion, which isn’t relevant to the gnome keyring.  The objects may have several levels of visibility, but the most common is public (always visible) and private (must be logged in to the token to see them).  Objects themselves have a variety of attributes, some of which depend on what type of object they are and some of which are universal (like id and label).  For instance, an object representing an RSA public key would have the public exponent and the public modulus as queryable attributes.

The pkcs11 protocol also has a reasonably comprehensive object finding protocol.  An arbitrary list of attributes and values can be passed to the query and it will return all objects that fully match.  The token identifier is a query attribute which may be present but doesn’t have to be, so if you omit it, you end up searching over every token the pkcs11 subsystem knows about.

The other operation that pkcs11 understands is logging into a token with a pin (which is pkcs11 speak for a passphrase).  The pin doesn’t have to be supplied by the entity performing the login, it may be supplied to the token via an out of band mechanism (for instance the little button on the yukikey, or even a real keypad).  The important thing for gnome keyring here is that logging into a token may be as simple as sending the login command and letting the token sort out the authorization, which is the way gnome keyring operates.

You also need to understand is conventions about searching for keys.  The pkcs11 standard recommends (but does not require) that public and private key pairs, which exist as two separate objects, should have the same id attribute.  This means that if you want to find an rsa private key, the way you do this is by searching the public objects for the exponent and modulus.  Once this is returned, you log into the token and retrieve the private key object by the public key id.

The final thing to understand is that once you have the private key object, it merely acts as an entitlement to have the token perform certain private key operations for you (like encryption, decryption or signing); it doesn’t mean you have access to the private key itself.

Gnome Keyring handling of ssh keys

Once the ssh-agent side of gnome keyring receives a challenge, it must respond by returning the private key signature of the challenge.  To do this, it searches the pkcs11 for the key used for the challenge (for RSA keys, it searches by modulus and exponent, for DSA keys it searches by signature primes, etc).  One interesting point to note is the search isn’t limited to the gnome keyring ssh token store, so if the key is found anywhere, in any pkcs11 token, it will be used.  The expectation is that they key id attribute will be the ssh fingerprint and the key label attribute will be the ssh comment, but these aren’t used in the actual search.  Once the public key is found, the agent logs into the token, retrieves the private key by label and id and proceeds to get the private key to sign the challenge.

Adding TPM key handling to the ssh store

Gnome keyring is based on GNU libgcrypt which handles all cryptographic objects via s-expressions (which are basically lisp like strings).  Gcrypt itself doesn’t seem to have an s-expression for a token, so the actual signing will be done inside the keyring code.  The s-expression I chose to represent a TPM based private key is

(private-key
  (rsa
    (tpm
      (blob <binary key blob>)
      (auth <authoriztion>))))

The rsa is necessary for it to be recognised for RSA signatures.  Now the ssh-store is modified to recognise the PEM guards for TPM keys, load the key blob up into the s-expression and ask the secret-store for the authorization passphrase which is also loaded.  In theory, it might be safer to ask the secret store for the authorization at key use time, but this method mirrors what happens to private keys, which are decrypted at load time and stored as s-expressions containing the component primes.

Now the RSA signing code is hooked to check for TPM s-expressions and divert the signature to the TPM if they’re found.  Once this is done, gnome keyring is fully enabled for TPM based ssh keys.  The initial email thread about this is here, and an openSUSE build repository of the modified code is here.

One important design constraint for this is that people (well, OK me) have a lot of ssh keys, sometimes more than could be effectively stored by the TPM in its internal shielded memory (plus if you’re like me, you’re using the TPM for other things like VPN), so the design of the keyring TPM additions is not to burden that memory further, thus TPM keys are only loaded into the TPM whenever they’re needed for an operation and are unloaded otherwise.  This means the TPM can scale to hundreds of keys, but at the expense of taking longer: Instead of simply asking the TPM to sign something, you have to first ask the TPM to load and unwrap (which is an RSA operation) the key, then use it to sign.  Effectively it’s two expensive TPM operations per real cryptographic function.

Using TPM based ssh keys

Once you’ve installed the modified gnome-keyring package, you’re ready actually to make use of it.  Since we’re going to replace all your ssh private keys with TPM equivalents, which are keyed to a given storage root key (SRK)1 which changes every time the TPM is cleared, it is prudent to take a backup of all your ssh keys on some offline encrypted USB stick, just in case you ever need to restore them (or transfer them to a new laptop2).

cd ~/.ssh/
for pub in *rsa*.pub; do
    priv=$(basename $pub .pub)
    echo $priv
    create_tpm_key -m -a -w $priv ${priv}.tpm
    mv ${priv}.tpm $priv
done

You’ll be prompted first to create a TPM authorization password for the key, then to verify it, then to give the PEM password for the ssh key (for each key).  Note, this only transfers RSA keys (the only algorithm the TPM can handle) and also note the script above is overwriting the PEM private key, so make sure you have a backup.  The create_tpm_key command comes from the openssl_tpm_engine package, which I’ve patched here to support random migration authority and well known SRK authority.

All you have to do now is log out and back in (to restart the gnome-keyring daemon with the new ssh keystore) and you’re using TPM based keys for all your ssh operations.  I’ve noticed that this adds a couple of hundred milliseconds per login, so if you batch stuff over ssh, this is why your scripts are slower.