CVE-2024-50223 : Detail

CVE-2024-50223

5.5
/
Medium
Memory Corruption
0.03%V4
Local
2024-11-09
10h14 +00:00
2024-12-19
09h35 +00:00
CVE.org
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CVE Descriptions

sched/numa: Fix the potential null pointer dereference in task_numa_work()

In the Linux kernel, the following vulnerability has been resolved: sched/numa: Fix the potential null pointer dereference in task_numa_work() When running stress-ng-vm-segv test, we found a null pointer dereference error in task_numa_work(). Here is the backtrace: [323676.066985] Unable to handle kernel NULL pointer dereference at virtual address 0000000000000020 ...... [323676.067108] CPU: 35 PID: 2694524 Comm: stress-ng-vm-se ...... [323676.067113] pstate: 23401009 (nzCv daif +PAN -UAO +TCO +DIT +SSBS BTYPE=--) [323676.067115] pc : vma_migratable+0x1c/0xd0 [323676.067122] lr : task_numa_work+0x1ec/0x4e0 [323676.067127] sp : ffff8000ada73d20 [323676.067128] x29: ffff8000ada73d20 x28: 0000000000000000 x27: 000000003e89f010 [323676.067130] x26: 0000000000080000 x25: ffff800081b5c0d8 x24: ffff800081b27000 [323676.067133] x23: 0000000000010000 x22: 0000000104d18cc0 x21: ffff0009f7158000 [323676.067135] x20: 0000000000000000 x19: 0000000000000000 x18: ffff8000ada73db8 [323676.067138] x17: 0001400000000000 x16: ffff800080df40b0 x15: 0000000000000035 [323676.067140] x14: ffff8000ada73cc8 x13: 1fffe0017cc72001 x12: ffff8000ada73cc8 [323676.067142] x11: ffff80008001160c x10: ffff000be639000c x9 : ffff8000800f4ba4 [323676.067145] x8 : ffff000810375000 x7 : ffff8000ada73974 x6 : 0000000000000001 [323676.067147] x5 : 0068000b33e26707 x4 : 0000000000000001 x3 : ffff0009f7158000 [323676.067149] x2 : 0000000000000041 x1 : 0000000000004400 x0 : 0000000000000000 [323676.067152] Call trace: [323676.067153] vma_migratable+0x1c/0xd0 [323676.067155] task_numa_work+0x1ec/0x4e0 [323676.067157] task_work_run+0x78/0xd8 [323676.067161] do_notify_resume+0x1ec/0x290 [323676.067163] el0_svc+0x150/0x160 [323676.067167] el0t_64_sync_handler+0xf8/0x128 [323676.067170] el0t_64_sync+0x17c/0x180 [323676.067173] Code: d2888001 910003fd f9000bf3 aa0003f3 (f9401000) [323676.067177] SMP: stopping secondary CPUs [323676.070184] Starting crashdump kernel... stress-ng-vm-segv in stress-ng is used to stress test the SIGSEGV error handling function of the system, which tries to cause a SIGSEGV error on return from unmapping the whole address space of the child process. Normally this program will not cause kernel crashes. But before the munmap system call returns to user mode, a potential task_numa_work() for numa balancing could be added and executed. In this scenario, since the child process has no vma after munmap, the vma_next() in task_numa_work() will return a null pointer even if the vma iterator restarts from 0. Recheck the vma pointer before dereferencing it in task_numa_work().

CVE Informations

Related Weaknesses

CWE-ID Weakness Name Source
CWE-476 NULL Pointer Dereference
The product dereferences a pointer that it expects to be valid but is NULL.

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) 6.3 To (excluding) 6.6.60

Linux>>Linux_kernel >> Version From (including) 6.7 To (excluding) 6.11.7

Linux>>Linux_kernel >> Version 6.12

Linux>>Linux_kernel >> Version 6.12

Linux>>Linux_kernel >> Version 6.12

Linux>>Linux_kernel >> Version 6.12

Linux>>Linux_kernel >> Version 6.12

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.