Chameth.com - posts like break-everything-fuzz-testing, docker-automatic-nginx-proxy, ipv6-docker-routing but not debugging-beyond-the-debugger, finding-an-awkward-bug-with-claude-code, migrating-from-github-to-forgejo, why-you-should-be-using-https

An interesting Tailscale + Docker gotcha

2026-01-29T00:00:00Z

As I’ve written about before, I use Tailscale for a lot of things. I thought I had it set up in a reasonably secure manner, but I recently noticed a problem.

I use Tailscale’s ACLs to limit what each node can access, based on the tags I apply to it. So an app node can’t access anything via Tailscale, while an integration or server node can access things tagged with either app or integration. This is expressed pretty simply in the Tailscale ACL JSON:

{
	"tagOwners": {
		// Servers that can be SSH'd into
		"tag:server": [],
		// Applications that are exposed on tailscale but never connect out
		"tag:app": [],
		// Things which talk to other services over tailscale (connecting to apps etc)
		"tag:integration": [],
	},
	"grants": [
		{
			// Users can access everything
			"src": ["autogroup:member"],
			"dst": ["*"],
			"ip":  ["*"],
		},
		{
			// Servers and integrations can access integrations and apps
			"src": ["tag:server", "tag:integration"],
			"dst": ["tag:integration", "tag:app"],
			"ip":  ["*"],
		},
	],
}

Over the past week I’ve been setting up a private Forgejo instance behind Tailscale, complete with an actions runner that runs things using a docker-in-docker container. I didn’t want the runner knowing anything about Tailscale, so I had it configured to speak to Forgejo direct over HTTP (forgejo:3000) instead of using full Tailscale HTTPS URL that I use when accessing it (http://git.example-net.ts.net/)¹.

Everything was going fine, until I forgot to do that translation… and it worked. My Forgejo action runners could access anything on my tailnet. I run several things on Tailscale that just have authentication turned off, on the basis that only authorised devices can access them. Things like the admin interface for this website. I definitely didn’t intend for any workflow I run on my git server to have access to edit my website!

This wasn’t just limited to Forgejo, either. Any docker container I was running could access the tailnet. In hindsight it’s fairly obvious why: the host is running Tailscale, connected as a node tagged with my server tag. That creates a tailscale0 interface, and automagically sets up iptables rules to route Tailscale traffic over the interface. Docker also automagically sets up iptables rules to bridge traffic, and apparently these two sets of rules interact in such a way that traffic from Docker containers is allowed to route via the tailscale0 interface.

I say it’s fairly obvious in hindsight — there’s no reason why Docker would special case any particular host interface after all — but it still feels pretty surprising. Because both bits of software inject their own iptables rules, I never really had a good mental model for how they interact. The host Tailscale node was a completely separate building block to Docker. It would be a pain to use either of them if they didn’t do these rules, but it’s also one of the reasons I don’t really like “magical” things².

There are lots of ways to fix this, but none of them feel particularly great. You can configure both Tailscale and Docker to not automatically fiddle with iptables and handle the rules yourself, but I really hate dealing with iptables³. As a stopgap I did hold my nose and add some iptables rules to drop traffic to the tailscale0 interface if it originated from the IP ranges that Docker was configured to use:

iptables -I DOCKER-USER -s 192.168.0.0/16 -o tailscale0 -j DROP
iptables -I DOCKER-USER -s 172.17.0.0/12 -o tailscale0 -j DROP

The DOCKER-USER chain is a nice little escape hatch; it comes before the main auto-generated DOCKER chain, and Docker leaves the rules in it alone.

So I added these rules, and felt pretty good about myself, and then… everything started breaking in weird ways. After some debugging I realised the problem was DNS⁴. The server’s DNS resolver is 100.100.100.100, a special Tailscale address. This is so that it can resolve tailnet hostnames via MagicDNS (and so I don’t have to configure my custom DNS servers manually on each device; Tailscale does it for me). My new iptables rules inadvertently dropped all the DNS packets coming from docker containers. D’oh.

Obviously the solution here is to double down and add MORE iptables rules:

iptables -I DOCKER-USER -s 192.168.0.0/16 -d 100.100.100.100 -o tailscale0 -j ACCEPT
iptables -I DOCKER-USER -s 172.17.0.0/12 -d 100.100.100.100 -o tailscale0 -j ACCEPT

This got DNS working again, but it all feels a bit gross. Not least because to actually make these persist you need to use iptables-save, and then you also get all the rules that both Docker and Tailscale have inserted. I came across a script to filter out the Docker ones, but… yuck⁵.

I realised a cleaner idea might just be to lock down what server tagged nodes could access within Tailscale itself. That way I can avoid touching iptables at all. It’s handy to be able to curl services from the host when debugging, but it’s not really necessary. So I removed that access, and… stuff broke again. I’m using my Forgejo instance as a registry for some of the docker images I run, so the docker daemon needs to be able to reach it. I ended up making a new tag for infrastructure, which can be accessed from server devices. This does still allow all the docker containers to reach Forgejo, but I already have it set up with appropriate access controls and public/private repository splits. Forgejo is a service designed to run publicly, so this seems a reasonable trade-off for convenience. I used the tests feature of Tailscale’s ACL config to make sure I’d got the rules right:

{
	"tests": [
		{
			// Servers can only access infrastructure
			"src":   "tag:server",
			"proto": "tcp",
			"allow": [
				"tag:infrastructure:8080",
			],
			"deny": [
				"100.84.16.43:8080",
				"me@example.com:8080",
				"tag:server:8080",
				"tag:app:8080",
				"tag:integration:8080",
			],
		},
	],
}

The whole situation still feels a bit messy. If I ever get around to switching to nftables I might loop back and manually craft some rules for routing traffic, instead of leaving Tailscale and Docker to do their own thing.

I discovered later on that logging into the container registry at forgejo:3000 actually issued a redirect to https://git.example-net.ts.net/ so this was all basically for naught… ↩︎
I didn’t use Tailscale’s MagicDNS for a long time just because the word “magic” put me off. Only when I eventually got around to learning how it worked, and seeing that it wasn’t really that magical under the hood, did I change my mind. ↩︎
nftables seems far better in lots of ways, but I can’t really be bothered migrating. Maybe next time I reimage the server for whatever reason… ↩︎
It’s always DNS… ↩︎
Again, nftables would almost certainly help here. It actually has (gasp) configuration files. But again, I really didn’t want to spend the time migrating. ↩︎

Docker reverse proxying, redux

2022-12-06T00:00:00Z

Six years ago, I described my system for configuring a reverse proxy for docker containers. It involved six containers including a key-value store and a webserver. Nothing in that system has persisted to this day. Don’t get me wrong – it worked – but there were a lot of rough edges and areas for improvement.

Microservices and their limitations

My goal was to follow the UNIX philosophy of “do one thing and do it well”. Unfortunately, that doesn’t really work when applied to network services that have to interact with one and other. UNIX tools are built upon a common file system and simple data passed over STDIN. Microservices don’t have that shared foundation. You could make one: companies that use microservices in anger often have a team that deals with the “developer experience” of creating and using microservices. But as a solo developer that’s not something I wanted to spend my time doing.

This became very apparent when trying to debug issues. In the UNIX world, if your series of commands piped together does something strange you can simply echo the data at various stages. Not so much when that data is flying around on a network, going into and out of things like etcd. Trying to figure out why a certificate hadn’t been acquired was a case of searching through logs from four containers, none of which had particularly good logging. There are many ways to get insight into what’s happening with microservices but, again, that’s not something I wanted to do myself.

Over time, and with experience in other projects, I came to realise that microservices only really make sense if you’re unable to deploy a monolith. For tech companies this naturally happens when different teams contribute to the same service: splitting it out into smaller services that are wholly owned by individual teams makes sense. For solo developers, that never happens. You can still gain the other benefits of microservices – such as code separation and having clearly defined APIs – by sticking to certain coding standards.

Proxy inconveniences

As well as being unhappy with the microservice nature of the solution, I wasn’t pleased with nginx. If you requested an unknown domain, nginx would use the first server block in its config to serve a response, instead of sending an “unrecognised name” alert as I wanted. It was a minor issue, but it irked me.

So from nginx I switched to haproxy. It has a strict-sni option when configuring TLS connections which makes it behave properly. It also performs a lot better for this type of workload than nginx. All was well for a while, but then I started getting alerts that requests were occasionally failing. I couldn’t reproduce the issue, but my nightly jobs to build and push containers managed to hit it nearly every night, causing them to fail.

After some investigation, I found that the haproxy developers had refactored the header parsing code, and neglected to properly reset flags when multiple requests were sent over the same connection. There was a patch, but it wasn’t released. No problem, I thought, I’ll just cherry-pick it onto the last release… Except that haproxy use Git in the most convoluted manner I’ve ever seen – they have one repository per release. This makes it harder to patch, but it also made me question whether I trusted them to ship stable software: there were no tests for the header parsing code (which is both fundamental and finicky, the perfect target for tests), the source code management was weird, and they didn’t seem in any rush to patch this bug.

Not long after that issue, Greg managed to encounter another bug where haproxy returned a 500 error whenever the upstream server replied with a particular, perfectly valid, header. The die was cast – it was time to move to something else.

Not Invented Here syndrome

Looking for a new solution, there were many more options than back in 2016. I’m still convinced, however, that anything exposed to the Internet should not have access to run docker containers. It’s the modern equivalent of running a CGI script as root. That single requirement eliminates most off-the-shelf solutions. What do you do when nothing quite meets your specific requirements? You make something yourself! My new solution has two components: Dotege and Centauri.

Dotege is a replacement for the microservices that monitored containers and obtained certificates. It’s fundamentally a templating engine - whenever the containers change, it evaluates a template and saves the result to disk. The template has access to details about the containers, their labels, ports, and so on. Dotege can also obtain certificates from Let’s Encrypt, and raise a signal against another process whenever the template or certificates change. I used this to generate the configuration and certificates used by haproxy for a while, and more recently changed the template so that it works for Centauri.

Centauri is my own reverse proxy. It’s configured using a simple text file and can also obtain certificates from an ACME provider. It doesn’t serve static content, has no knowledge about docker, and avoids the other bells and whistles that adorn most reverse proxies. It also has good test coverage to ensure that I don’t, say, accidentally break header parsing.

As a software engineer I enjoy writing software, but I also enjoy running simple, easy to understand software. That’s what I’ve achieved here: it’s very easy to identify where the problem is if anything goes wrong, both are small Go programs rather than vast sprawling C monstrosities, and their interaction is primarily through a file written to disk that can be inspected or edited as needed.

Adventures in IPv6 routing in Docker

2022-10-24T00:00:00Z

One of the biggest flaws in Docker’s design is that it wasn’t created with IPv6 in mind. Out of the box Docker assigns each container a private IPv4 address, and they won’t be able to reach IPv6-only services. While incoming connections might work, the containers won’t know the correct remote IP address which can cause problems for some applications. This situation is obviously suboptimal in the current day and age. It’s a bit like not supporting HTTPS on a website — you might not have any issues because of it immediately, but you’re fighting against the currents of progress and are making life worse for your users.

Thankfully, it’s now relatively easy to make Docker behave a lot nicer. The docker-ipv6nat project has been around since 2016, and uses an IPv6 overlay network and some iptables magic to route traffic to and from containers in a sensible fashion. It uses NAT to emulate the behaviour Docker employs for IPv4 traffic; while using NAT with IPv6 is an anathema, I think it makes sense for containers. You could give each container a publicly routable IPv6 address, but that brings with it a lot of headaches: you’re basically going to be forced to implement service discovery and some kind of DNS management to deal with the fact that your containers will be popping up on randomly assigned IP addresses. That is completely overkill for people running a small number of services on one or two physical boxes; and if it’s not overkill for you then you’re probably already looking at more complicated orchestration solutions like Kubernetes.

More recently, similar functionality has been built into the Docker daemon itself. You can now edit the config file to enable ipv6 and each container will be assigned an address in the range specified when it uses the default bridge network. This gives more-or-less the same functionality of docker-ipv6nat — you lose a little flexibility as you can’t disable IPv6 on the default bridge, but that’s a very worthy trade for having the functionality built-in.

So far this all seems very simple. Hardly worthy of being called an “adventure”. Enter stage left: the wicked witch of destination address selection…

Destination address selection and you

When a computer program tries to connect to an address such as google.com, it first resolves it in to an IP address. That’s DNS 101, but what happens if the address resolves to multiple IP addresses? For example, google.com resolves to both 142.250.74.206 and 2a00:1450:4001:82b::200e. You might just assume there’s a simple “prefer IPv4 / prefer IPv6” toggle somewhere that decides, but it’s actually a lot more complicated. With IPv6, devices are likely to have many addresses - a link-local address, a unique local address, a normal public address, various privacy addresses, and so on. To deal with this, a number of RFCs define a series of rules that most implementations follow. These are called the destination address selection rules. RFC6724 gives the rules as:

Avoid unusable destinations
Prefer matching scope
Avoid deprecated addresses
Prefer home addresses
Prefer matching label
Prefer higher precedence
Prefer native transport
Prefer smaller scope
Use the longest matching prefix
Otherwise, leave the order unchanged

Applying these rules will re-order the list of IP addresses such that (in theory) the most likely one to work will be first. Most of them are fairly niche rules; the ones that do the heavy lifting are rules 5 and 6, which rely on a policy table to make their decisions. The policy table ‘SHOULD’ be configurable by system administrators to allow them to tweak how traffic is routed. In the absence of an admin-provided policy table, the RFC gives the following defaults:

Prefix	Precedence	Label	Notes
`::1/128`	50	0	Loopback address
`::/0`	40	1	Any IPv6 address
`::ffff:0:0/96`	35	4	IPv4 addresses mapped as v6 addresses
`2002::/16`	30	2	6-to-4 gateways
`2001::/32`	5	5	Toredo tunnels
`fc00::/7`	3	13	Unique Local Addresses (ULAs)
`::/96`	1	3	IPv4 compatible addresses (deprecated)
`fec0::/10`	1	11	Site-local addresses (deprecated)
`3ffe::/16`	1	12	6bone (deprecated)

The policy table is a bit complex, but you can see that normal IPv6 addresses are preferred (have a higher precedence) over IPv4 addresses, which are preferred over the various tunnels, local addresses, and various deprecated ranges.

We can see the result of this when I run ping google.com on a box that has a native IPv6 connection as well as a native IPv4 connection:

PING google.com(fra07s29-in-x200e.1e100.net (2a00:1450:4001:802::200e)) 56 data bytes
64 bytes from fra24s01-in-x0e.1e100.net (2a00:1450:4001:802::200e): icmp_seq=1 ttl=119 time=5.11 ms
64 bytes from fra24s01-in-x0e.1e100.net (2a00:1450:4001:802::200e): icmp_seq=2 ttl=119 time=5.28 ms
64 bytes from fra07s29-in-x200e.1e100.net (2a00:1450:4001:802::200e): icmp_seq=3 ttl=119 time=5.31 ms
^C
--- google.com ping statistics ---
3 packets transmitted, 3 received, 0% packet loss, time 2003ms
rtt min/avg/max/mdev = 5.114/5.235/5.311/0.086 ms

The address selection in this instance has been a result of rule 6: the IPv6 address has a higher precedence (40) than the IPv4 address (35).

However, if I run the same command in an IPv6-enabled Ubuntu container then it seems to prefer the IPv4 address:

PING google.com (172.217.16.206) 56(84) bytes of data.
64 bytes from fra16s08-in-f206.1e100.net (172.217.16.206): icmp_seq=1 ttl=59 time=4.86 ms
64 bytes from fra16s08-in-f14.1e100.net (172.217.16.206): icmp_seq=2 ttl=59 time=4.86 ms
64 bytes from fra16s08-in-f14.1e100.net (172.217.16.206): icmp_seq=3 ttl=59 time=5.03 ms
^C
--- google.com ping statistics ---
3 packets transmitted, 3 received, 0% packet loss, time 2003ms
rtt min/avg/max/mdev = 4.860/4.916/5.027/0.078 ms

If I force ping to use IPv6 with the -6 flag then it works the same as on the host, but when left to its own devices it prefers IPv4. What’s going on there? The key difference between the Docker container and the host is that the host’s network interfaces have public addresses, but the container has a private IPv4 address (172.19.0.7) and a private IPv6 address (fd00:dead:beef::7). My first reaction to this was to think “Ah yes, fc00::/7 has a lower precedence than native IPv4, that makes sense”, but that’s not quite right. These are the destination address selection rules; rule 6 doesn’t care about the source addresses. This is actually rule 5 at work: the label of source IPv6 address is 13, but the label of the destination address is 1; meanwhile both the source and destination IPv4 addresses are 4. This sorts the IPv4 address before the IPv6 one, and rule 6 becomes irrelevant.

Adjusting the policy table

Thankfully, the RFC says the policy table should be configurable by system administrators, so those of us who are doing unorthodox things like NAT’ing IPv6 can customise the behaviour to fit our weird environments. The configuration is done via the /etc/gai.conf file (‘gai’ standing for getaddrinfo, the function in the standard library responsible for dealing with all these rules). The gai.conf file by default (if it exists) will likely just contain comments and examples; if there are no uncommented “label” or “precedence” lines then the library will use its built-in defaults based on the RFC requirements.

To make our container happy, we need to cause fd00::/8 to have the same label as a public IPv6 address. To do this we can uncomment the default labels in gai.conf and then add a single extra line, like so:

 label ::1/128       0
 label ::/0          1
 label 2002::/16     2
 label ::/96         3
 label ::ffff:0:0/96 4
 label fec0::/10     5
 label fc00::/7      6
 label 2001:0::/32   7
+label fd00::/8      1

Because fd00::/8 has a longer prefix than fc00::/7 it will match our addresses and give them a label of 1, the same as a public IPv6 address would get. This makes rule 5 leave it alone, and the default precedence table used by the standard library will put the IPv6 address above the IPv4 address. (They don’t bother giving fc00::/7 a separate precedence as listed in the RFC because rule 5 would have already de-prioritised those addresses, as we discovered.)

Making this small change to the gai.conf file in our Ubuntu container makes it start preferring the IPv6 address for google.com:

PING google.com(fra16s65-in-x0e.1e100.net (2a00:1450:4001:806::200e)) 56 data bytes
64 bytes from fra15s29-in-x0e.1e100.net (2a00:1450:4001:806::200e): icmp_seq=1 ttl=118 time=5.00 ms
64 bytes from fra15s29-in-x0e.1e100.net (2a00:1450:4001:806::200e): icmp_seq=2 ttl=118 time=5.10 ms
64 bytes from fra16s65-in-x0e.1e100.net (2a00:1450:4001:806::200e): icmp_seq=3 ttl=118 time=5.08 ms
^C
--- google.com ping statistics ---
3 packets transmitted, 3 received, 0% packet loss, time 2004ms
rtt min/avg/max/mdev = 5.002/5.058/5.098/0.040 ms

A fairly complicated problem, but a simple fix!

But wait, there’s more…

Unfortunately, this fix won’t work on containers that use alpine. And that is a lot of containers. Alpine uses musl as its standard library, rather than the much more common glibc. There is very little documentation on the subject, but if you browse the source code for musl you will see that it doesn’t implement any way at all to configure the policy tables. In network/lookup_name.c we can see the sorting logic:

	/* The following implements a subset of RFC 3484/6724 destination
	 * address selection by generating a single 31-bit sort key for
	 * each address. Rules 3, 4, and 7 are omitted for having
	 * excessive runtime and code size cost and dubious benefit.
	 * So far the label/precedence table cannot be customized. */
	for (i=0; i<cnt; i++) {
		int family = buf[i].family;
		int key = 0;
		struct sockaddr_in6 sa6 = { 0 }, da6 = {
			.sin6_family = AF_INET6,
			.sin6_scope_id = buf[i].scopeid,
			.sin6_port = 65535
		};
		struct sockaddr_in sa4 = { 0 }, da4 = {
			.sin_family = AF_INET,
			.sin_port = 65535
		};
		void *sa, *da;
		socklen_t salen, dalen;
		if (family == AF_INET6) {
			memcpy(da6.sin6_addr.s6_addr, buf[i].addr, 16);
			da = &da6; dalen = sizeof da6;
			sa = &sa6; salen = sizeof sa6;
		} else {
			memcpy(sa6.sin6_addr.s6_addr,
				"\0\0\0\0\0\0\0\0\0\0\xff\xff", 12);
			memcpy(da6.sin6_addr.s6_addr+12, buf[i].addr, 4);
			memcpy(da6.sin6_addr.s6_addr,
				"\0\0\0\0\0\0\0\0\0\0\xff\xff", 12);
			memcpy(da6.sin6_addr.s6_addr+12, buf[i].addr, 4);
			memcpy(&da4.sin_addr, buf[i].addr, 4);
			da = &da4; dalen = sizeof da4;
			sa = &sa4; salen = sizeof sa4;
		}
		const struct policy *dpolicy = policyof(&da6.sin6_addr);
		int dscope = scopeof(&da6.sin6_addr);
		int dlabel = dpolicy->label;
		int dprec = dpolicy->prec;
		int prefixlen = 0;
		int fd = socket(family, SOCK_DGRAM|SOCK_CLOEXEC, IPPROTO_UDP);
		if (fd >= 0) {
			if (!connect(fd, da, dalen)) {
				key |= DAS_USABLE;
				if (!getsockname(fd, sa, &salen)) {
					if (family == AF_INET) memcpy(
						sa6.sin6_addr.s6_addr+12,
						&sa4.sin_addr, 4);
					if (dscope == scopeof(&sa6.sin6_addr))
						key |= DAS_MATCHINGSCOPE;
					if (dlabel == labelof(&sa6.sin6_addr))
						key |= DAS_MATCHINGLABEL;
					prefixlen = prefixmatch(&sa6.sin6_addr,
						&da6.sin6_addr);
				}
			}
			close(fd);
		}
		key |= dprec << DAS_PREC_SHIFT;
		key |= (15-dscope) << DAS_SCOPE_SHIFT;
		key |= prefixlen << DAS_PREFIX_SHIFT;
		key |= (MAXADDRS-i) << DAS_ORDER_SHIFT;
		buf[i].sortkey = key;
	}
	qsort(buf, cnt, sizeof *buf, addrcmp);

We can see if the address labels match, the sort key is adjusted using the DAS_MATCHINGLABEL constant. But where do the labels come from? For that we need to investigate the policyof func:

static const struct policy {
	unsigned char addr[16];
	unsigned char len, mask;
	unsigned char prec, label;
} defpolicy[] = {
	{ "\0\0\0\0\0\0\0\0\0\0\0\0\0\0\0\1", 15, 0xff, 50, 0 },
	{ "\0\0\0\0\0\0\0\0\0\0\xff\xff", 11, 0xff, 35, 4 },
	{ "\x20\2", 1, 0xff, 30, 2 },
	{ "\x20\1", 3, 0xff, 5, 5 },
	{ "\xfc", 0, 0xfe, 3, 13 },
#if 0
	/* These are deprecated and/or returned to the address
	 * pool, so despite the RFC, treating them as special
	 * is probably wrong. */
	{ "", 11, 0xff, 1, 3 },
	{ "\xfe\xc0", 1, 0xc0, 1, 11 },
	{ "\x3f\xfe", 1, 0xff, 1, 12 },
#endif
	/* Last rule must match all addresses to stop loop. */
	{ "", 0, 0, 40, 1 },
};

static const struct policy *policyof(const struct in6_addr *a)
{
	int i;
	for (i=0; ; i++) {
		if (memcmp(a->s6_addr, defpolicy[i].addr, defpolicy[i].len))
			continue;
		if ((a->s6_addr[defpolicy[i].len] & defpolicy[i].mask)
		    != defpolicy[i].addr[defpolicy[i].len])
			continue;
		return defpolicy+i;
	}
}

So the policyof func simply uses the defpolicy array to find the policy that applies to an address. This defpolicy array contains a number of hardcoded entries which correspond exactly to the ones provided in the RFC. The one causing us problems is { "\xfc", 0, 0xfe, 3, 13 } which matches fc00::/7 addresses.

Unfortunately, without recompiling musl from source there’s not much we can do to address this directly. The only way we can make the containers work as desired is to give them a different address range. Ideally this would be a range that you control and that is otherwise not used, but there’s a good chance you won’t have such a range. One possible alternative is the 2001:db8::/32 range which is reserved for documentation purposes. This doesn’t feature in any of the policy tables, so is treated like a normal public IPv6 address. It doesn’t feel great to misuse a reserved range like that, but it’s probably the least of all evils, at least until musl allows configuring the policy table.

How to break everything by fuzz testing

2020-04-26T00:00:00Z

Fuzz testing is a bit like the infinite monkey theorem, but instead of Shakespeare you get crashes.

Fuzz testing, if you’re not aware, is a form of testing that uses procedurally generated random inputs to see how a program behaves. For instance, if you were fuzz testing a web page renderer you might generate a bunch of HTML - some valid, and some not - and make sure the rendering process didn’t unexpectedly crash.

Fuzz testing doesn’t readily lend itself to all types of software, but it particularly shines in cases where some kind of complex user input is accepted and processed in some way - like the aforementioned web page renderer. I was recently adding a library to parse EXIF data to images to an Internet-facing service and realised it was a perfect opportunity to do some fuzz testing. Even if I didn’t find any issues, I’d improve my confidence that the library was safe enough to expose to the Internet.

Breaking my EXIF library

I wrote a quick harness to run go-fuzz on the library, and gave it some pre-existing demo files as sample input. The way go-fuzz works is that it instruments your code and then mutates the inputs to try to improve the coverage. For example, if I had some sample data that had an EXIF tag with a value of 1 then go-fuzz might change it to a 2 and see if the code follows a different path. In most cases it won’t but when it does, they tend to be very interesting cases.

One of the first issues that go-fuzz found was that some values in a maker note field would cause the library to panic (i.e., crash). This happened because there was a check to see if the first six characters were “Nikon” and a null byte, without first checking to see if there were actually six characters available. This is a kind of bug that doesn’t happen much with “real” data - as the field is either not present or completed correctly - but could easily be exploited once this code is exposed to the Internet.

Another interesting bug that go-fuzz found was that if a tag had a particularly large count, the library would try to allocate an obscene amount of memory and die. There was already a check in the code that was meant to avoid this exact scenario, but go-fuzz managed to find a way around it. Each tag has a size (for example an integer tag takes a fixed number of bytes) and a count; the existing check multiplied the two together and made sure that the result wasn’t too large. For most cases this was fine but go-fuzz found a case where the count was so large that when multiplied by the size of the tag it overflowed the integer and became negative, thus passing the sanity check but then subsequently failing when it came around to actually allocating the memory.

The final bug of note that go-fuzz found was the most interesting. EXIF data is stored in IFDs (“Image File Directories”), and each IFD provides what is effectively a pointer (a byte offset) to the next one. The EXIF library already had a check to make sure that these didn’t loop, but it only checked the immediately preceding IFD - so if IFD 1 linked to IFD 2, it would catch IFD 2 linking back to IFD 1 and break the loop with an error. Go-fuzz found that having three interlinked IFDs had the same issue, though, and the guard code wasn’t triggered. This created an infinite loop, maxing out a CPU core until the process was eventually killed - one of the worst kind of bugs you could have in an Internet-facing service which doesn’t deal with private data! The fix for this was fairly straightforward - I just made the library keep a record of the previously visited IFDs and bail out if it found a loop.

Breaking my IDE

When go-fuzz detects an issue it outputs not only the details of the problem (the stack trace, error message, and so forth) but also the input that generated the problem. This is useful for reproducing and making sure the issue is fixed, but it also makes it really easy to write a test to ensure that the behaviour never regresses in the future.

As I was working through fixing the bugs that go-fuzz found, I dutifully added new tests where needed. After adding the sample input with looping IFDs to the project, I switched to IDEA to write a test to use it. I clicked on the input file to copy the file name, and then the entire IDE hung and had to be restarted. Uh oh! When I restarted IDEA, it immediately began indexing the project and again hung. It turns out IDEA parses EXIF data (presumably, even if it does nothing else with the data, to get the rotation property for images), and the library they use - an independent one written on Java - had the same bug as the Go library I was using.

In order to stop IDEA from indexing the file and becoming unusable I renamed it from a ‘.tif’ extension to ‘.dat’, and everything went back to normal. I thought I’d best report the bug to JetBrains, though, so they could put a proper fix in.

Breaking YouTrack

JetBrains use their own issue tracker called YouTrack for reporting bugs in IDEA. I dutifully went over and described the problem, attaching the log files from the IDE, a description of how the file was malformed, and carefully selected the .dat version of the file to upload so that it wouldn’t cause anyone else the same immediate problem.

After trying to upload the file I got a strange error back. Uh oh! I submitted the IDEA issue as it stood, unable to see if the attachments had even uploaded, and went and wrote up an issue for YouTrack itself about the error message. While I was doing that, YouTrack seemed to slow down and become really annoying to use. I had a sinking feeling the exact same thing was probably happening as with IDEA and my library - but this time YouTrack had content-sniffed the file instead of relying on the file extension. In hindsight, I should’ve put the file in a passworded archive to ensure no automated tools got hold of it. I marked the issue as a security problem as in a service like YouTrack it presents a denial-of-service opportunity¹ (remember when I said it was one of the worst kinds of bugs you could have in an Internet-facing service?…)

Shortly after I raised my YouTrack ticket, a notice appeared at the top of the page saying they were investigating the current performance issues. Uh oh! I was holding out hope that this was unrelated to me uploading the buggy dat file, but the timing all seemed a bit suspect. I shot support an e-mail saying I think I might be the root cause for their performance issues and linked to the ticket. In the time it took me to e-mail them, the entire site had been put into maintenance mode. I got an e-mail back a few hours later confirming the outage was in fact all my fault, as I’d feared. Within the space of days the JetBrains security team had fixed the issue in YouTrack, which was a pretty nice turnaround.

So if you were trying to access YouTrack at the start of March and couldn’t - I’m sorry, I didn’t mean to! Also, if you’re building an Internet-facing service that takes user input you should really consider running a fuzz tester against it!

“We have a problem”. “Remember, there are no such things as problems, only opportunities”. “Well then we have a DDoS opportunity.” – @J4vv4d ↩︎

An introduction to containers

2020-03-01T00:00:00Z

So. Many. Containers.

I’m a huge fan of (software) containers. Most people I know fall in to one of two camps: either they also use, and are fans of, containers, or they haven’t yet really figured them out and view them as some kind of voodoo that they don’t really want or need.

I’m writing this short guide to explain a little how containers work - and how running something in a container isn’t really that much different to running it normally - to hopefully enable more people in that second group to give them a try. It’s aimed at people who have a fairly good grasp of how Linux works.

Containers are often mentioned in the same breath as VMs, which is not a helpful comparison or analogy. Think of containers as standard units of software, much like how Intermodal containers are standard units of freight transport across the world. When a company internationally ships goods in volume there isn’t a question about how they’re packaged - they go in an intermodal container. The same container can be deployed on a freight train, a lorry, or a ship. The haulage company doesn’t need to care what’s in the container because they’re completely standardised. Likewise, with software containers you don’t really need to care about what’s inside: the software you’re deploying could be written in Go, Python2, Python3, Bash, PHP, LOLCODE, or anything¹.

What does a running container look like?

When you run a container, you’re just running a process. In a lot of ways it’s not very different to what happens if you were to start the same process on the host computer.

For example I have a container that just runs cat(1). With no arguments, cat will read from stdin until it receives an EOF, so it’s handy to test with. If I run ps a on my computer, I can see the cat process in amongst everything else I’m currently running:

    PID TTY      STAT   TIME COMMAND
   7199 pts/1    Ss     0:01 /usr/bin/zsh
 323806 pts/0    Ss+    0:00 /bin/cat
 324120 pts/4    R+     0:00 ps a

The /bin/cat process is in a container, and the ps a underneath it is just running like normal on my desktop. They look very similar, right? If I look under /proc/323806 I can see all the usual attributes, the same as any other process running.

If I run ps in a container, though, it’s a different story:

    PID TTY      STAT   TIME COMMAND
      1 pts/0    Rs+    0:00 ps a

So inside the container it looks like there’s only one process running. It can’t see anything running “outside” on my desktop. The secret here is that this isn’t a special container trick: this is just a feature of the Linux kernel called namespacing.

If we go back to procfs and look at the ns/pid node we can see the process in the container is in a separate PID (process ID) namespace to the one on my desktop:

# readlink /proc/323806/ns/pid  
pid:[4026534564]
# readlink /proc/7199/ns/pid
pid:[4026531836]

Almost all² processes running ’normally’ on my desktop have the same PID namespace, whereas each container gets their own by default. PID namespaces are hierarchical: a new process is assigned a PID in its own namespace, and the parent namespace, and the grandparent namespace, and so on. That’s why I can see the process running in the container from my normal shell - the container’s namespace is a child of the main namespace all of my desktop software is running in.

Linux supports - and container software makes use of - a bunch of other namespaces too: mount points, network, UTS³, cgroups, and more. These all play a part in isolating a container from the system it is running on.

You can manually run a process with unshare(1) to “unshare” some namespaces from the parent process. For example if I run unshare -fp --mount-proc ps a, it looks very similar to running ps instead the container:

    PID TTY      STAT   TIME COMMAND
      1 pts/4    R+     0:00 ps a

So: a process running inside a container is just a heavily namespaced process running otherwise normally in the operating system. No voodoo magic here!

What about the filesystem? What are ‘images’?

Containers run in their own mount namespace meaning mount points can be different inside the container to those on the host. This means the container can have a different / mounted to the host, effectively giving it its own filesystem.

The root filesystem of the container is defined in the container’s image. If I use Docker to run a container using the Ubuntu image (docker run ubuntu), the root filesystem inside that container will resemble a minimal ubuntu install. Note that this is just the filesystem: the container doesn’t have its own kernel.

You might be thinking that sounds pretty inefficient. Downloading Ubuntu is definitely not instant, and doing it for every application you run would be insane! Quite. Containers solve this by using filesystem layers. These are stacked on top of one another to create the final filesystem. Each layer can be retrieved and cached independently of all others.

Say (for simplicity) that the Ubuntu image is a single layer. If I run one container with that image, then the layer will be downloaded and cached once. If I run three hundred containers with that image then the layer will be downloaded and cached once. Even better, if I use another image that’s based on Ubuntu but adds some software on top, only the “on top” layer will be downloaded if I already have the relevant “Ubuntu” layer cached.

If all the layers are cached, what happens when you change a file? This is dealt with using the copy-on-write technique: when you modify a file it is copied from the source layer and the changes are only made in a new layer. This is handled by the OverlayFS filesystem which is part of the mainline kernel.

When a container is running, changes made to its filesystem are temporary, and do not persist across container restarts. To persist data - or introduce new data to a container - you can mount volumes. How this works varies depending on how you’re running your container, but at the basic level it is pretty much the same as bind-mounting (mount -o bind).

You may be familiar with using chroots to change the apparent root directory of processes, perhaps with full-blown “jails” built on top. Containers offer much better isolation thanks to the use of namespaces. Instead of being constrained to a portion of the host’s filesystem, they don’t even have it mounted! Containers also get to specify their environment - if they expect in certain places, for example - instead of the sysadmin having to manually set up the chroot. Finally, containers offer much more fine-grained control over what processes can do (if you want it), and allow much more advanced use-cases such as inter-container networking.

Images and filesystems employ a little magic to ensure that layers are reusable and cacheable, but again there’s nothing terribly special about them: a container has a filesystem that appears to it to work the same way as a filesystem on the host, and it’s using a standard filesystem shipped with the kernel.

How about networking?

Again, networking is namespaced, so a container has its own network stack, its own virtual network interface, its own IP address and so on. How that network interacts with your real network depends on how you’re running the container. Docker, for example, can add iptables rules to NAT traffic between containers' networks and the outside world.

Containers can generally be connected into networks, and can communicate amongst themselves without the traffic actually leaving the host machine. This allows you to, for example, run a SQL database and connect it to a web application without ever exposing the database to the outside world. Moreover, as well as being isolated from the outside world, it’s isolated from other containers in other networks. If one of your applications has a crazy bug or is compromised, this significantly limits the damage it can do.

You have to explicitly opt in to “publishing” ports from a container, which exposes them to the outside world (either directly, or via a load balancer or some other middle-man, depending on how you’re running the container). This means you can pick and chose how the outside world sees the app you’re deploying: if it’s a web service that listens on both port 443 and port 80, you can chose to only expose the encrypted port.

If you run some containers and create some networks, you can see the interfaces and bridges on the host:

$ ip l
...
6: br-2405a8cc0445: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc noqueue state UP mode DEFAULT group default 
    link/ether 02:42:3e:fa:23:62 brd ff:ff:ff:ff:ff:ff
8: veth8ed0735@if7: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc noqueue master br-2405a8cc0445 state UP mode DEFAULT group default 
    link/ether b2:c1:5d:55:26:9b brd ff:ff:ff:ff:ff:ff link-netnsid 2
10: veth541d84b@if9: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc noqueue master br-9d7bc4024c1a state UP mode DEFAULT group default 
    link/ether 86:aa:5f:ee:da:1a brd ff:ff:ff:ff:ff:ff link-netnsid 1

This shows a bridge and two virtual NICs, just the same as if you’d manually created them (albeit with seemingly random names). So: as before, nothing special here.

Docker? Compose? K8s? Floccinaucinihilipilification?

(OK, Floccinaucinihilipilification isn’t actually a container technology, as far as I’m aware.)

Docker is the most popular container engine - that is, the bit of software that actually runs containers. It’s responsible for setting up all those namespaces we found, downloading and caching image layers, and actually starting and stopping the processes. Docker runs as a system-wide daemon - when you run a command like docker run ubuntu it actually just instructs the daemon to do the work.

There are several alternatives to Docker for running containers; one interesting one is Podman which runs containers without a daemon. Container engines have all standardised around the same image format looked after by the Open Container Initiative, so an image you build in Docker can be used in Podman, or pretty much any other engine.

Docker compose is a tool for defining and running multiple-container applications. I mentioned earlier running a database alongside a webapp - in practice to do this you’re going to have to configure a network for them, configure a mount point for the database to persist its data on, pass credentials in to both the database and the application, and so on. Doing all that by hand is tedious and error prone.

Docker compose lets you write “compose files”, which are simple yaml descriptions of the containers you wish to run, their properties, and details about any volumes or networks you may want. Out of the box, docker-compose will create a default network for each compose file you run so the containers within it can communicate.

Kubernetes is a container orchestrator, designed to automate deployment and management of large numbers of containers. It works with Docker under the hood, but provides a huge amount of tooling on top to allow you to deploy applications and manage their dependencies. It runs across multiple physical (or virtual) machines (while still allowing containers to communicate privately), and can support massive workloads by scaling out services (running multiple copies of a container on different hosts) and load balancing. Kubernetes is sometimes shorted to k8s (as in K - 8 elided letters - s) because computer people don’t like long words.

OK, they make sense now. But why bother?

Hopefully if you’ve read this far you’ve already picked up on some of the potential benefits, but this is my personal list:

Isolation. If I run some software in a container, there is very little it can do to upset me. It’s not going to leave bits of itself all over my filesystem, it can’t steal all of the secrets in my home directory, I can even limit its CPU and memory resources if I want. If I decide to stop running it, I just delete the container and it is completely gone: no trace remains.

Ease of use. If you give me a container image I have a very good idea of how to run it already. I might need to do some minor configuration to expose ports or mount volumes, but there’s no question about how to run it, how to make it automatically start, and there’s no “installation” procedure. If I want to then swap it with an alternative (say, move from MySQL to MariaDB), it’s potentially just a case of changing the name of the image I pull.

Dependencies included. Containers just run. Python 2 software includes Python 2 and just work. Python 3 software includes Python 3 and just work. I don’t have a massive headache trying to run both at the same time, because they take care of their own messes. Similarly I’m not going to have to install npm or cargo or composer to pull in dependencies for an application: that’s going to have been done in the build process.

Reproducibility. As a fallout from having dependencies included and being isolated from everything else, containers give you amazing reproducibility. If it “works on your machine” in a container, it’ll almost certainly work in production because it’s the exact same environment.

Standardisation. At the start of this article I called containers standard units of software. One of my favourite advantages of containers is that you basically get an API to list all the software you’re running. Most container engines let you supply labels attached to containers as well, so you can add your own annotations. I use this to annotate services which expose HTTP endpoints, and I have a tool that automatically generates SSL certificates for them and configures haproxy to route traffic to them. I can’t imagine how I’d do this without containers - I imagine it’d involve a lot of manual work.

Hopefully this has helped demystify containers a little. If you feel like I’ve missed something important out, or I’ve left you more confused than when you started, feel free to drop me a note using the feedback form below.

OK, maybe you should care if you’re deploying something written in crazy languages like PHP. ↩︎
Some multi-process apps, such as web browsers, are starting to use namespaces to enhance security, as do certain package systems like Flatpak ↩︎
“Unix timesharing system”; in practice this means having a separate hostname ↩︎