CVE-2025-21693

Score / Severity

7.8

High

CVE Descriptions

mm: zswap: properly synchronize freeing resources during CPU hotunplug

In the Linux kernel, the following vulnerability has been resolved: mm: zswap: properly synchronize freeing resources during CPU hotunplug In zswap_compress() and zswap_decompress(), the per-CPU acomp_ctx of the current CPU at the beginning of the operation is retrieved and used throughout. However, since neither preemption nor migration are disabled, it is possible that the operation continues on a different CPU. If the original CPU is hotunplugged while the acomp_ctx is still in use, we run into a UAF bug as some of the resources attached to the acomp_ctx are freed during hotunplug in zswap_cpu_comp_dead() (i.e. acomp_ctx.buffer, acomp_ctx.req, or acomp_ctx.acomp). The problem was introduced in commit 1ec3b5fe6eec ("mm/zswap: move to use crypto_acomp API for hardware acceleration") when the switch to the crypto_acomp API was made. Prior to that, the per-CPU crypto_comp was retrieved using get_cpu_ptr() which disables preemption and makes sure the CPU cannot go away from under us. Preemption cannot be disabled with the crypto_acomp API as a sleepable context is needed. Use the acomp_ctx.mutex to synchronize CPU hotplug callbacks allocating and freeing resources with compression/decompression paths. Make sure that acomp_ctx.req is NULL when the resources are freed. In the compression/decompression paths, check if acomp_ctx.req is NULL after acquiring the mutex (meaning the CPU was offlined) and retry on the new CPU. The initialization of acomp_ctx.mutex is moved from the CPU hotplug callback to the pool initialization where it belongs (where the mutex is allocated). In addition to adding clarity, this makes sure that CPU hotplug cannot reinitialize a mutex that is already locked by compression/decompression. Previously a fix was attempted by holding cpus_read_lock() [1]. This would have caused a potential deadlock as it is possible for code already holding the lock to fall into reclaim and enter zswap (causing a deadlock). A fix was also attempted using SRCU for synchronization, but Johannes pointed out that synchronize_srcu() cannot be used in CPU hotplug notifiers [2]. Alternative fixes that were considered/attempted and could have worked: - Refcounting the per-CPU acomp_ctx. This involves complexity in handling the race between the refcount dropping to zero in zswap_[de]compress() and the refcount being re-initialized when the CPU is onlined. - Disabling migration before getting the per-CPU acomp_ctx [3], but that's discouraged and is a much bigger hammer than needed, and could result in subtle performance issues. [1]https://lkml.kernel.org/20241219212437.2714151-1-yosryahmed@google.com/ [2]https://lkml.kernel.org/20250107074724.1756696-2-yosryahmed@google.com/ [3]https://lkml.kernel.org/20250107222236.2715883-2-yosryahmed@google.com/ [yosryahmed@google.com: remove comment]

CVE Informations

Related Weaknesses

CWE-ID	Weakness Name	Source
CWE-416	Use After Free The product reuses or references memory after it has been freed. At some point afterward, the memory may be allocated again and saved in another pointer, while the original pointer references a location somewhere within the new allocation. Any operations using the original pointer are no longer valid because the memory "belongs" to the code that operates on the new pointer.

Metrics

Metrics	Score	Severity	CVSS Vector	Source
V3.1	7.8	HIGH	CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H More informations 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. 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.	134c704f-9b21-4f2e-91b3-4a467353bcc0

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.

Date	EPSS V0	EPSS V1	EPSS V2 (> 2022-02-04)	EPSS V3 (> 2025-03-07)	EPSS V4 (> 2025-03-17)
2025-02-11	–	–	–	0.04%	–
2025-02-16	–	–	–	0.04%	–
2025-02-16	–	–	–	0.04%	–
2025-03-18	–	–	–	–	0.02%
2025-04-15	–	–	–	–	0.02%
2025-04-22	–	–	–	–	0.02%
2025-04-22	–	–	–	–	0.02,%

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

Date	Percentile
2025-02-11	11%
2025-02-16	12%
2025-02-16	12%
2025-03-18	2%
2025-04-15	3%
2025-04-22	4%
2025-04-22	4%