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

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. ↩︎

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 ↩︎

Debugging beyond the debugger

2019-05-08T00:00:00Z

Real-life debugging tools

Most programming — and sysadmin — problems can be debugged in a fairly straight forward manner using logs, print statements, educated guesses, or an actual debugger. Sometimes, though, the problem is more elusive. There’s a wider box of tricks that can be employed in these cases but I’ve not managed to find a nice overview of them, so here’s mine. I’m mainly focusing on Linux and similar systems, but there tend to be alternatives available for other Operating Systems or VMs if you seek them out.

Networking

tcpdump

tcpdump prints out descriptions of packets on a network interface. You can apply filters to limit which packets are displayed, chose to dump the entire content of the packet, and so forth.

Typical usage might look something like:

# tcpdump -nSi eth0 port 80
tcpdump: verbose output suppressed, use -v or -vv for full protocol decode
listening on eth0, link-type EN10MB (Ethernet), capture size 262144 bytes
16:03:35.577781 IP6 2001:db8::1.54742 > 2001:db8::2.80: Flags [S], seq 2815779044, win 64800, options [mss 1440,sackOK,TS val 2378811665 ecr 0,nop,wscale 7], length 0
16:03:35.586853 IP6 2001:db8::2.80 > 2001:db8::1.54742: Flags [S.], seq 1522609102, ack 2815779045, win 28560, options [mss 1440,sackOK,TS val 3063610173 ecr 2378811665,nop,wscale 7], length 0
16:03:35.586877 IP6 2001:db8::1.54742 > 2001:db8::2.80: Flags [.], ack 1522609103, win 507, options [nop,nop,TS val 2378811674 ecr 3063610173], length 0
16:03:35.620678 IP6 2001:db8::1.54742 > 2001:db8::2.80: Flags [P.], seq 2815779045:2815779399, ack 1522609103, win 507, options [nop,nop,TS val 2378811708 ecr 3063610173], length 354: HTTP: GET / HTTP/1.1

Here you can see the start of a plaintext HTTP request: the three-way handshake as the TCP connection is established followed by a GET request. Even if the data is encrypted as it will be in most cases, it’s often useful to see the “shape” of the transmissions: did the client start sending data when it connected, did the server ever respond, etc.

Daniel Miessler has a good tutorial on tcpdump if you’re not familiar with it and don’t want to jump straight into the man page.

… with Docker

Docker sets up separate network namespaces for each container. To see the traffic across the interfaces of a single container you can nsenter the container’s network namespace:

# nsenter -t $(docker inspect --format '{{.State.Pid}}' my_container) -n tcpdump -nS port 80

This retrieves the PID for the container, and tells nsenter to enter the network (-n) namespace from the given target (-t) PID, and then run the given command (in this case tcpdump ...).

openssl s_client / s_server

When a connection is using TLS it’s often useful to try connecting to the server and see what certificate it presents, algorithms it negotiates, and so forth. OpenSSL offers two useful subcommands which can help with this: s_client for connecting as a client, and s_server for listening to connections.

For example, using s_client to connect to google.com on the standard HTTPS port shows us details about the server cert and its verification status:

$ openssl s_client -connect google.com:443
CONNECTED(00000003)
depth=2 OU = GlobalSign Root CA - R2, O = GlobalSign, CN = GlobalSign
verify return:1
depth=1 C = US, O = Google Trust Services, CN = Google Internet Authority G3
verify return:1
depth=0 C = US, ST = California, L = Mountain View, O = Google LLC, CN = *.google.com
verify return:1
---
Certificate chain
 0 s:C = US, ST = California, L = Mountain View, O = Google LLC, CN = *.google.com
   i:C = US, O = Google Trust Services, CN = Google Internet Authority G3
 1 s:C = US, O = Google Trust Services, CN = Google Internet Authority G3
   i:OU = GlobalSign Root CA - R2, O = GlobalSign, CN = GlobalSign
---
# ...

Whereas connecting to my webserver and providing an unknown host in the SNI field results in an SSL alert 112 (“The server name sent was not recognized”) and no server certificate is sent:

$ openssl s_client -connect chameth.com:443 -servername example.com
CONNECTED(00000003)
140384831313024:error:14094458:SSL routines:ssl3_read_bytes:tlsv1 unrecognized name:../ssl/record/rec_layer_s3.c:1536:SSL alert number 112
---
no peer certificate available
---
# ...

Often if you hit this kind of alert in an application the exact error will be lost somewhere in the many layers between the SSL library and the logs, so being able to directly connect and test can help diagnose a lot of issues.

Once a connection is established you can read and write plain text and it will be encrypted and decrypted automatically.

Java apps

If a Java app is involved in the connection, you can enable a lot of built-in debugging with a simple JVM property: javax.net.debug. You can tweak what exactly gets logged, but the easiest thing to do is just set the property to all and you’ll see information about certificate chains, verification, and packet dumps:

$ java -Djavax.net.debug=all -jar ....
# ...
found key for : duke
chain [0] = [
[
  Version: V1
  Subject: CN=Duke, OU=Java Software, O="Sun Microsystems, Inc.",
  L=Cupertino, ST=CA, C=US
# ...

More information about Java’s debugging options is available on docs.oracle.com.

Thread and core dumps

Higher-level languages frequently provide an interactive way to dump the current execution state of all of their threads (a “thread dump”). This is useful to spot deadlocks, some types of race conditions, and as a quick and dirty method of investigating hangs or excessive CPU usage.

With both Java and Go applications you can send a QUIT signal to have a thread dump printed out; Go applications will quit after doing so, Java ones will carry on running. At most terminals you can hit Ctrl and \ to send a QUIT signal.

For Java you can also use the jstack tool from the JDK to dump threads by PID; this can be useful if the application is running in the background or has redirected sysout:

$ jstack 8321
Attaching to process ID 8321, please wait...
Debugger attached successfully.
Client compiler detected.

Thread t@5: (state = BLOCKED)
 - java.lang.Object.wait(long) @bci=-1107318896 (Interpreted frame)
 - java.lang.Object.wait(long) @bci=0 (Interpreted frame)
 - java.lang.ref.ReferenceQueue.remove(long) @bci=44, line=116 (Interpreted frame)
 - java.lang.ref.ReferenceQueue.remove() @bci=2, line=132 (Interpreted frame)
 - java.lang.ref.Finalizer$FinalizerThread.run() @bci=3, line=159 (Interpreted frame)

# ...

A core dump provides more complete information about the state of a process, but is often more complex to interpret. The gcore utility from GDB will create a core dump of a process with a given PID. You can then generally load the core file using your normal debugger, depending on the language in question.

System calls

strace is the swiss army knife for seeing what a process is doing. It details each system call made by a program (you can filter them down, of course). For example:

$ strace -e read curl https://google.com/
read(3, "\177ELF\2\1\1\0\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0 \236\0\0\0\0\0\0"..., 832) = 832
read(3, "\177ELF\2\1\1\0\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0P!\0\0\0\0\0\0"..., 832) = 832
read(3, "\177ELF\2\1\1\3\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0\200l\2\0\0\0\0\0"..., 832) = 832
read(3, "\177ELF\2\1\1\0\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0\20Q\0\0\0\0\0\0"..., 832) = 832
# ...
read(3, "\0\0\0\0\0\0\0\4\25\345\366\302\273sE6\365wI\225\321|\3435Z\362\216\372\215\251aO"..., 253) = 253
<HTML><HEAD><meta http-equiv="content-type" content="text/html;charset=utf-8">
<TITLE>301 Moved</TITLE></HEAD><BODY>
<H1>301 Moved</H1>
The document has moved
<A HREF="https://www.google.com/">here</A>.
</BODY></HTML>
read(3, "\27\3\3\0!", 5)                = 5
# ...

Brendan Gregg has a nice guide on strace and alternatives.

… with docker

When the application is running in docker you can usually just strace it from the host with the correct PID (from e.g. docker inspect --format '{{.State.Pid}}' my_container). Sometimes you may need to trace the startup of an application though, which is a bit trickier. Instead you can run a new container using the same PID namespace as your target, and the permissions needed to strace:

$ docker run --rm -it --pid=container:my_container \
  --net=container:my_container \
  --cap-add sys_admin \
  --cap-add sys_ptrace \
  alpine

From within the new container you can install strace, and trace any running program within the target container using strace -p as normal. To start a new program you need access to the target container’s filesystem, which you can get to via /proc/1/root (PID 1 being the main process that docker started in the target container).

Files

Sometimes the problem might relate to file access. There are a couple of straight forward — but nonetheless useful — tools which might help here. inotifywait uses the Linux inotify subsystem to watch files or directories for operations. For example:

$ inotifywait -mr site/content
Setting up watches.  Beware: since -r was given, this may take a while!
Watches established.
site/content/post/ MODIFY 2019-05-08-debugging-beyond-the-debugger.md
site/content/post/ OPEN 2019-05-08-debugging-beyond-the-debugger.md
site/content/post/ MODIFY 2019-05-08-debugging-beyond-the-debugger.md
site/content/post/ MODIFY 2019-05-08-debugging-beyond-the-debugger.md
site/content/post/ CLOSE_WRITE,CLOSE 2019-05-08-debugging-beyond-the-debugger.md
# ...

Here the -m switch makes inotifywait monitor the files forever (instead of exiting on the first modification, which is the normal behaviour) and r makes it recurse into the directory and monitor each file and subdirectory in there.

If you want to see what processes currently have a file open, fuser is the go-to tool. For example:

$ fuser -v /
                     USER PID ACCESS COMMAND
/:                   root     kernel mount /
                     chris      2961 .rc.. systemd
                     chris      2986 .r... gdm-x-session
                     chris      2994 .r... dbus-daemon
                     chris      3001 .r... gnome-session-b
# ...

Honourable mentions

These aren’t really debugging tools, but I feel it’s worth mentioning as they often feature somewhere along the debugging-of-weird-problems journey.

I’ve seen some weird and wonderful problems happen because a disk is full, so a quick df early on in the debugging process never hurts. Some apps may hang, some may corrupt their config, some may fall over and die; sometimes the manner in which they fail doesn’t obviously point to a disk space issue.

Another issue that comes up now and then — especially inside VMs or other environment that don’t have a decent amount of “noise” happening — is entropy exhaustion. A quick look at /proc/sys/kernel/random/entropy_avail should be enough to confirm that everything is ticking along nicely. If it’s exceedingly low then you may find that anything involving random number generation stalls (TLS connections for example).

Why you should be using HTTPS

2016-06-17T00:00:00Z

The EFF’s HTTPS Everywhere logo

One of my favourite hobbyhorses recently has been the use of HTTPS, or lack thereof. HTTPS is the thing that makes the little padlock appear in your browser, and has existed for over 20 years. In the past, that little padlock was the exclusive preserve of banks and other ‘high security’ establishments; over time its use has gradually expanded to most (but not all) websites that handle user information, and the time is now right for it to become ubiquitous.

Why use HTTPS?

There are numerous advantages to using HTTPS, both for the users of a website and for the operator:

Privacy

The most obvious advantage is that HTTPS gives your users additional privacy. An insecure (HTTP) request can potentially be read by anyone on the same network, or the network operators, or anyone who happens to operate a network along the path between the user and the server.

Users on shared WiFi networks (such as those in coffee shops, hotels, or offices) are particularly vulnerable to passive sniffing by anyone else on that network. If the network is open (as is frequently the case) then anyone in radio range can see exactly what the user is up to.

Integrity

HTTPS also helps to maintain the integrity of your site. With a plain HTTP request, there’s nothing to stop anyone in between the server and the user from modifying the content of the request or the response. This is a frequent tactic used by annoying WiFi gateways (such as the ones you’d find in a hotel), dubious ISPs who want to serve you extra adverts, or just plain old nefarious attackers.

If you’re trying to convey some kind of information to users (and if you aren’t, why exactly are you running a website again?) it seems beneficial to both you and them if the information arrives as you intended, rather than in a modified form due to someone or something tampering with it.

Security

If your website has any kind of authentication, or session identifiers, it becomes extremely vulnerable to an attacker monitoring the traffic and stealing the credentials. This was starkly demonstrated in 2010 when Firesheep was released. This tool allowed anyone to quickly and automatically hijack social media accounts of anyone on the same network who was using HTTP to access them.

Even if your login pages are served over HTTPS, if you send a single session ID cookie over HTTP (such as a page you decided wasn’t particularly ‘important’) then an attacker can probably spoof the user’s session and gain full access to their account. Again, in the case of open WiFi networks that could be anyone in radio range.

Search engine rankings

Some search engines use HTTPS as a signal in their ranking algorithms. Google announced in 2004 that it was using the presence of HTTPS as a small positive signal, but that it may strengthen that signal over time as more and more websites switch to using a secure transport. It’s not unthinkable that at some point in the future there will be HTTPS-only search engines.

But… But… But…

There are lots of excuses for not implementing HTTPS. Most of them are either misguided or outdated.

It’s too expensive and/or complicated

In the past, getting HTTPS certificates was a pain. A number of free suppliers have existed for a while but the process for getting their certificates wasn’t particularly straight forward, and many imposed arbitrary restrictions on the certificate parameters. Even once you had the certificate, you had to fiddle about with your HTTP server configuration to make it work, remember to manually get a new certificate when the old one expired, and lots of other annoying busywork.

With the arrival of Let’s Encrypt, all that changed. You can retrieve and deploy a free HTTPS certificate with two or three commands. Renewal can be handled completely automatically with a single command executed by cron.

There’s no point; nothing on my site is sensitive

You might not think your content warrants privacy, but can you speak for everyone who accesses it? Even content that seems mundane to you — such as travel advice, or technical writing — could be used to build up a profile of a user. If an attacker is monitoring traffic in a coffee shop and sees a user looking at travel advice and weather forecasts for a foreign country, he could use that information to plan a burglary knowing that the user will be away. Similarly, some content which is perfectly mundane to you may actually be very sensitive in other countries with repressive governments. HTTPS makes it much harder for these people to snoop on traffic.

From another angle, if you’re offering any kind of information, instructions, or especially file downloads, there’s a severe risk to users if the content is modified on its way to them. An evil sysadmin could rewrite your travel advice to suggest visiting the local drug dealer’s hangout, or replace your download with a malware-infested version.

HTTPS is slower, uses more resources, etc

Back in 1995 this might have been a valid argument. Enabling HTTPS on a modern server will make an almost negligible difference to performance. If you also enable HTTP/2 (which most implementations only support over HTTPS), it’s likely to actually use fewer resources, and result in a faster, smoother experience for your users. HTTP/2 was designed to work with HTTPS, and designed with modern requirements and networking techniques in mind.

CloudFlare have an excellent demonstration of the benefits of HTTP/2, and it can show speed improvements of 2-3x in a typical environment. On top of being faster, HTTP/2 uses fewer connections which results in less resource overhead on both the server and the client.

So what are you waiting for?

If you run a website and aren’t using HTTPS, give it a try.