CVE-2021-47011 : Detail

CVE-2021-47011

5.5
/
Medium
0.04%V4
Local
2024-02-28
08h13 +00:00
2024-12-19
07h33 +00:00
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CVE Descriptions

mm: memcontrol: slab: fix obtain a reference to a freeing memcg

In the Linux kernel, the following vulnerability has been resolved: mm: memcontrol: slab: fix obtain a reference to a freeing memcg Patch series "Use obj_cgroup APIs to charge kmem pages", v5. Since Roman's series "The new cgroup slab memory controller" applied. All slab objects are charged with the new APIs of obj_cgroup. The new APIs introduce a struct obj_cgroup to charge slab objects. It prevents long-living objects from pinning the original memory cgroup in the memory. But there are still some corner objects (e.g. allocations larger than order-1 page on SLUB) which are not charged with the new APIs. Those objects (include the pages which are allocated from buddy allocator directly) are charged as kmem pages which still hold a reference to the memory cgroup. E.g. We know that the kernel stack is charged as kmem pages because the size of the kernel stack can be greater than 2 pages (e.g. 16KB on x86_64 or arm64). If we create a thread (suppose the thread stack is charged to memory cgroup A) and then move it from memory cgroup A to memory cgroup B. Because the kernel stack of the thread hold a reference to the memory cgroup A. The thread can pin the memory cgroup A in the memory even if we remove the cgroup A. If we want to see this scenario by using the following script. We can see that the system has added 500 dying cgroups (This is not a real world issue, just a script to show that the large kmallocs are charged as kmem pages which can pin the memory cgroup in the memory). #!/bin/bash cat /proc/cgroups | grep memory cd /sys/fs/cgroup/memory echo 1 > memory.move_charge_at_immigrate for i in range{1..500} do mkdir kmem_test echo $$ > kmem_test/cgroup.procs sleep 3600 & echo $$ > cgroup.procs echo `cat kmem_test/cgroup.procs` > cgroup.procs rmdir kmem_test done cat /proc/cgroups | grep memory This patchset aims to make those kmem pages to drop the reference to memory cgroup by using the APIs of obj_cgroup. Finally, we can see that the number of the dying cgroups will not increase if we run the above test script. This patch (of 7): The rcu_read_lock/unlock only can guarantee that the memcg will not be freed, but it cannot guarantee the success of css_get (which is in the refill_stock when cached memcg changed) to memcg. rcu_read_lock() memcg = obj_cgroup_memcg(old) __memcg_kmem_uncharge(memcg) refill_stock(memcg) if (stock->cached != memcg) // css_get can change the ref counter from 0 back to 1. css_get(&memcg->css) rcu_read_unlock() This fix is very like the commit: eefbfa7fd678 ("mm: memcg/slab: fix use after free in obj_cgroup_charge") Fix this by holding a reference to the memcg which is passed to the __memcg_kmem_uncharge() before calling __memcg_kmem_uncharge().

CVE Informations

Related Weaknesses

CWE-ID Weakness Name Source
CWE Other No informations.

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) 5.10.11 To (excluding) 5.10.37

Linux>>Linux_kernel >> Version From (including) 5.11 To (excluding) 5.11.21

Linux>>Linux_kernel >> Version From (including) 5.12 To (excluding) 5.12.4

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.