CVE-2022-49547 : Detail

CVE-2022-49547

5.5
/
Medium
0.02%V4
Local
2025-02-26
02h13 +00:00
2025-02-26
02h13 +00:00
CVE.org
NVD
Notifications for a CVE
Stay informed of any changes for a specific CVE.
Notifications manage

CVE Descriptions

btrfs: fix deadlock between concurrent dio writes when low on free data space

In the Linux kernel, the following vulnerability has been resolved: btrfs: fix deadlock between concurrent dio writes when low on free data space When reserving data space for a direct IO write we can end up deadlocking if we have multiple tasks attempting a write to the same file range, there are multiple extents covered by that file range, we are low on available space for data and the writes don't expand the inode's i_size. The deadlock can happen like this: 1) We have a file with an i_size of 1M, at offset 0 it has an extent with a size of 128K and at offset 128K it has another extent also with a size of 128K; 2) Task A does a direct IO write against file range [0, 256K), and because the write is within the i_size boundary, it takes the inode's lock (VFS level) in shared mode; 3) Task A locks the file range [0, 256K) at btrfs_dio_iomap_begin(), and then gets the extent map for the extent covering the range [0, 128K). At btrfs_get_blocks_direct_write(), it creates an ordered extent for that file range ([0, 128K)); 4) Before returning from btrfs_dio_iomap_begin(), it unlocks the file range [0, 256K); 5) Task A executes btrfs_dio_iomap_begin() again, this time for the file range [128K, 256K), and locks the file range [128K, 256K); 6) Task B starts a direct IO write against file range [0, 256K) as well. It also locks the inode in shared mode, as it's within the i_size limit, and then tries to lock file range [0, 256K). It is able to lock the subrange [0, 128K) but then blocks waiting for the range [128K, 256K), as it is currently locked by task A; 7) Task A enters btrfs_get_blocks_direct_write() and tries to reserve data space. Because we are low on available free space, it triggers the async data reclaim task, and waits for it to reserve data space; 8) The async reclaim task decides to wait for all existing ordered extents to complete (through btrfs_wait_ordered_roots()). It finds the ordered extent previously created by task A for the file range [0, 128K) and waits for it to complete; 9) The ordered extent for the file range [0, 128K) can not complete because it blocks at btrfs_finish_ordered_io() when trying to lock the file range [0, 128K). This results in a deadlock, because: - task B is holding the file range [0, 128K) locked, waiting for the range [128K, 256K) to be unlocked by task A; - task A is holding the file range [128K, 256K) locked and it's waiting for the async data reclaim task to satisfy its space reservation request; - the async data reclaim task is waiting for ordered extent [0, 128K) to complete, but the ordered extent can not complete because the file range [0, 128K) is currently locked by task B, which is waiting on task A to unlock file range [128K, 256K) and task A waiting on the async data reclaim task. This results in a deadlock between 4 task: task A, task B, the async data reclaim task and the task doing ordered extent completion (a work queue task). This type of deadlock can sporadically be triggered by the test case generic/300 from fstests, and results in a stack trace like the following: [12084.033689] INFO: task kworker/u16:7:123749 blocked for more than 241 seconds. [12084.034877] Not tainted 5.18.0-rc2-btrfs-next-115 #1 [12084.035562] "echo 0 > /proc/sys/kernel/hung_task_timeout_secs" disables this message. [12084.036548] task:kworker/u16:7 state:D stack: 0 pid:123749 ppid: 2 flags:0x00004000 [12084.036554] Workqueue: btrfs-flush_delalloc btrfs_work_helper [btrfs] [12084.036599] Call Trace: [12084.036601] [12084.036606] __schedule+0x3cb/0xed0 [12084.036616] schedule+0x4e/0xb0 [12084.036620] btrfs_start_ordered_extent+0x109/0x1c0 [btrfs] [12084.036651] ? prepare_to_wait_exclusive+0xc0/0xc0 [12084.036659] btrfs_run_ordered_extent_work+0x1a/0x30 [btrfs] [12084.036688] btrfs_work_helper+0xf8/0x400 [btrfs] [12084.0367 ---truncated---

CVE Informations

Related Weaknesses

CWE-ID Weakness Name Source
CWE-667 Improper Locking
The product does not properly acquire or release a lock on a resource, leading to unexpected resource state changes and behaviors.

Metrics

Metrics Score Severity CVSS Vector Source
V3.1 5.5 MEDIUM CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Base: Exploitabilty Metrics

The Exploitability metrics reflect the characteristics of the thing that is vulnerable, which we refer to formally as the vulnerable component.

Attack Vector

This metric reflects the context by which vulnerability exploitation is possible.

Local

The vulnerable component is not bound to the network stack and the attacker’s path is via read/write/execute capabilities.

Attack Complexity

This metric describes the conditions beyond the attacker’s control that must exist in order to exploit the vulnerability.

Low

Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success when attacking the vulnerable component.

Privileges Required

This metric describes the level of privileges an attacker must possess before successfully exploiting the vulnerability.

Low

The attacker requires privileges that provide basic user capabilities that could normally affect only settings and files owned by a user. Alternatively, an attacker with Low privileges has the ability to access only non-sensitive resources.

User Interaction

This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable component.

None

The vulnerable system can be exploited without interaction from any user.

Base: Scope Metrics

The Scope metric captures whether a vulnerability in one vulnerable component impacts resources in components beyond its security scope.

Scope

Formally, a security authority is a mechanism (e.g., an application, an operating system, firmware, a sandbox environment) that defines and enforces access control in terms of how certain subjects/actors (e.g., human users, processes) can access certain restricted objects/resources (e.g., files, CPU, memory) in a controlled manner. All the subjects and objects under the jurisdiction of a single security authority are considered to be under one security scope. If a vulnerability in a vulnerable component can affect a component which is in a different security scope than the vulnerable component, a Scope change occurs. Intuitively, whenever the impact of a vulnerability breaches a security/trust boundary and impacts components outside the security scope in which vulnerable component resides, a Scope change occurs.

Unchanged

An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.

Base: Impact Metrics

The Impact metrics capture the effects of a successfully exploited vulnerability on the component that suffers the worst outcome that is most directly and predictably associated with the attack. Analysts should constrain impacts to a reasonable, final outcome which they are confident an attacker is able to achieve.

Confidentiality Impact

This metric measures the impact to the confidentiality of the information resources managed by a software component due to a successfully exploited vulnerability.

None

There is no loss of confidentiality within the impacted component.

Integrity Impact

This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information.

None

There is no loss of integrity within the impacted component.

Availability Impact

This metric measures the impact to the availability of the impacted component resulting from a successfully exploited vulnerability.

High

There is a total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).

Temporal Metrics

The Temporal metrics measure the current state of exploit techniques or code availability, the existence of any patches or workarounds, or the confidence in the description of a vulnerability.

Environmental Metrics

These metrics enable the analyst to customize the CVSS score depending on the importance of the affected IT asset to a user’s organization, measured in terms of Confidentiality, Integrity, and Availability.

 nvd@nist.gov

EPSS

EPSS is a scoring model that predicts the likelihood of a vulnerability being exploited.

EPSS Score

The EPSS model produces a probability score between 0 and 1 (0 and 100%). The higher the score, the greater the probability that a vulnerability will be exploited.

EPSS Percentile

The percentile is used to rank CVE according to their EPSS score. For example, a CVE in the 95th percentile according to its EPSS score is more likely to be exploited than 95% of other CVE. Thus, the percentile is used to compare the EPSS score of a CVE with that of other CVE.
EPSS First.org

Products Mentioned

Configuraton 0

Linux>>Linux_kernel >> Version From (including) 5.15.27 To (excluding) 5.16

Linux>>Linux_kernel >> Version From (including) 5.16.13 To (excluding) 5.18.3

References

The information presented on CVE Find originates from several carefully selected reference sources. CVE data is provided by MITRE Corporation and the National Vulnerability Database (NVD). The Known Exploited Vulnerabilities (KEV) catalog is sourced from the Cybersecurity and Infrastructure Security Agency (CISA), while EPSS scores come from FIRST.org. Additionally, data regarding software weaknesses (CWE) and common attack patterns (CAPEC) is maintained by MITRE Corporation, and information on hardware and software configurations (CPE) is provided by the NVD.