CVE-2024-47741 : Detail

CVE-2024-47741

7
/
High
0.03%V4
Local
2024-10-21
12h14 +00:00
2024-12-19
09h27 +00:00
CVE.org
NVD
Notifications for a CVE
Stay informed of any changes for a specific CVE.
Notifications manage

CVE Descriptions

btrfs: fix race setting file private on concurrent lseek using same fd

In the Linux kernel, the following vulnerability has been resolved: btrfs: fix race setting file private on concurrent lseek using same fd When doing concurrent lseek(2) system calls against the same file descriptor, using multiple threads belonging to the same process, we have a short time window where a race happens and can result in a memory leak. The race happens like this: 1) A program opens a file descriptor for a file and then spawns two threads (with the pthreads library for example), lets call them task A and task B; 2) Task A calls lseek with SEEK_DATA or SEEK_HOLE and ends up at file.c:find_desired_extent() while holding a read lock on the inode; 3) At the start of find_desired_extent(), it extracts the file's private_data pointer into a local variable named 'private', which has a value of NULL; 4) Task B also calls lseek with SEEK_DATA or SEEK_HOLE, locks the inode in shared mode and enters file.c:find_desired_extent(), where it also extracts file->private_data into its local variable 'private', which has a NULL value; 5) Because it saw a NULL file private, task A allocates a private structure and assigns to the file structure; 6) Task B also saw a NULL file private so it also allocates its own file private and then assigns it to the same file structure, since both tasks are using the same file descriptor. At this point we leak the private structure allocated by task A. Besides the memory leak, there's also the detail that both tasks end up using the same cached state record in the private structure (struct btrfs_file_private::llseek_cached_state), which can result in a use-after-free problem since one task can free it while the other is still using it (only one task took a reference count on it). Also, sharing the cached state is not a good idea since it could result in incorrect results in the future - right now it should not be a problem because it end ups being used only in extent-io-tree.c:count_range_bits() where we do range validation before using the cached state. Fix this by protecting the private assignment and check of a file while holding the inode's spinlock and keep track of the task that allocated the private, so that it's used only by that task in order to prevent user-after-free issues with the cached state record as well as potentially using it incorrectly in the future.

CVE Informations

Related Weaknesses

CWE-ID Weakness Name Source
CWE-362 Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition')
The product contains a concurrent code sequence that requires temporary, exclusive access to a shared resource, but a timing window exists in which the shared resource can be modified by another code sequence operating concurrently.

Metrics

Metrics Score Severity CVSS Vector Source
V3.1 7 HIGH CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/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.

High

successful attack depends on conditions beyond the attacker's control. That is, a successful attack cannot be accomplished at will, but requires the attacker to invest in some measurable amount of effort in preparation or execution against the vulnerable component before a successful attack can be expected.

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.

High

There is a total loss of confidentiality, resulting in all resources within the impacted component being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact. For example, an attacker steals the administrator's password, or private encryption keys of a web server.

Integrity Impact

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

High

There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the impacted component. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to 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) 6.2 To (excluding) 6.6.54

Linux>>Linux_kernel >> Version From (including) 6.7 To (excluding) 6.10.13

Linux>>Linux_kernel >> Version From (including) 6.11 To (excluding) 6.11.2

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.