CVE-2025-39844 : Detail

CVE-2025-39844

5.5
/
Medium
0.02%V4
Local
2025-09-19
15h26 +00:00
2026-05-12
12h07 +00:00
CVE.org
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CVE Descriptions

mm: move page table sync declarations to linux/pgtable.h

In the Linux kernel, the following vulnerability has been resolved: mm: move page table sync declarations to linux/pgtable.h During our internal testing, we started observing intermittent boot failures when the machine uses 4-level paging and has a large amount of persistent memory: BUG: unable to handle page fault for address: ffffe70000000034 #PF: supervisor write access in kernel mode #PF: error_code(0x0002) - not-present page PGD 0 P4D 0 Oops: 0002 [#1] SMP NOPTI RIP: 0010:__init_single_page+0x9/0x6d Call Trace: __init_zone_device_page+0x17/0x5d memmap_init_zone_device+0x154/0x1bb pagemap_range+0x2e0/0x40f memremap_pages+0x10b/0x2f0 devm_memremap_pages+0x1e/0x60 dev_dax_probe+0xce/0x2ec [device_dax] dax_bus_probe+0x6d/0xc9 [... snip ...] It turns out that the kernel panics while initializing vmemmap (struct page array) when the vmemmap region spans two PGD entries, because the new PGD entry is only installed in init_mm.pgd, but not in the page tables of other tasks. And looking at __populate_section_memmap(): if (vmemmap_can_optimize(altmap, pgmap)) // does not sync top level page tables r = vmemmap_populate_compound_pages(pfn, start, end, nid, pgmap); else // sync top level page tables in x86 r = vmemmap_populate(start, end, nid, altmap); In the normal path, vmemmap_populate() in arch/x86/mm/init_64.c synchronizes the top level page table (See commit 9b861528a801 ("x86-64, mem: Update all PGDs for direct mapping and vmemmap mapping changes")) so that all tasks in the system can see the new vmemmap area. However, when vmemmap_can_optimize() returns true, the optimized path skips synchronization of top-level page tables. This is because vmemmap_populate_compound_pages() is implemented in core MM code, which does not handle synchronization of the top-level page tables. Instead, the core MM has historically relied on each architecture to perform this synchronization manually. We're not the first party to encounter a crash caused by not-sync'd top level page tables: earlier this year, Gwan-gyeong Mun attempted to address the issue [1] [2] after hitting a kernel panic when x86 code accessed the vmemmap area before the corresponding top-level entries were synced. At that time, the issue was believed to be triggered only when struct page was enlarged for debugging purposes, and the patch did not get further updates. It turns out that current approach of relying on each arch to handle the page table sync manually is fragile because 1) it's easy to forget to sync the top level page table, and 2) it's also easy to overlook that the kernel should not access the vmemmap and direct mapping areas before the sync. # The solution: Make page table sync more code robust and harder to miss To address this, Dave Hansen suggested [3] [4] introducing {pgd,p4d}_populate_kernel() for updating kernel portion of the page tables and allow each architecture to explicitly perform synchronization when installing top-level entries. With this approach, we no longer need to worry about missing the sync step, reducing the risk of future regressions. The new interface reuses existing ARCH_PAGE_TABLE_SYNC_MASK, PGTBL_P*D_MODIFIED and arch_sync_kernel_mappings() facility used by vmalloc and ioremap to synchronize page tables. pgd_populate_kernel() looks like this: static inline void pgd_populate_kernel(unsigned long addr, pgd_t *pgd, p4d_t *p4d) { pgd_populate(&init_mm, pgd, p4d); if (ARCH_PAGE_TABLE_SYNC_MASK & PGTBL_PGD_MODIFIED) arch_sync_kernel_mappings(addr, addr); } It is worth noting that vmalloc() and apply_to_range() carefully synchronizes page tables by calling p*d_alloc_track() and arch_sync_kernel_mappings(), and thus they are not affected by ---truncated---

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) 5.13 To (excluding) 5.15.192

Linux>>Linux_kernel >> Version From (including) 5.16 To (excluding) 6.1.151

Linux>>Linux_kernel >> Version From (including) 6.2 To (excluding) 6.6.105

Linux>>Linux_kernel >> Version From (including) 6.7 To (excluding) 6.12.46

Linux>>Linux_kernel >> Version From (including) 6.13 To (excluding) 6.16.6

Linux>>Linux_kernel >> Version 6.17

Linux>>Linux_kernel >> Version 6.17

Linux>>Linux_kernel >> Version 6.17

Linux>>Linux_kernel >> Version 6.17

Configuraton 0

Debian>>Debian_linux >> Version 11.0

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.