CVE-2025-39767 : Détail

CVE-2025-39767

5.5
/
Moyen
0.02%V4
Local
2025-09-11
16h56 +00:00
2025-09-29
05h59 +00:00
Notifications pour un CVE
Restez informé de toutes modifications pour un CVE spécifique.
Gestion des notifications

Descriptions du CVE

LoongArch: Optimize module load time by optimizing PLT/GOT counting

In the Linux kernel, the following vulnerability has been resolved: LoongArch: Optimize module load time by optimizing PLT/GOT counting When enabling CONFIG_KASAN, CONFIG_PREEMPT_VOLUNTARY_BUILD and CONFIG_PREEMPT_VOLUNTARY at the same time, there will be soft deadlock, the relevant logs are as follows: rcu: INFO: rcu_sched self-detected stall on CPU ... Call Trace: [<900000000024f9e4>] show_stack+0x5c/0x180 [<90000000002482f4>] dump_stack_lvl+0x94/0xbc [<9000000000224544>] rcu_dump_cpu_stacks+0x1fc/0x280 [<900000000037ac80>] rcu_sched_clock_irq+0x720/0xf88 [<9000000000396c34>] update_process_times+0xb4/0x150 [<90000000003b2474>] tick_nohz_handler+0xf4/0x250 [<9000000000397e28>] __hrtimer_run_queues+0x1d0/0x428 [<9000000000399b2c>] hrtimer_interrupt+0x214/0x538 [<9000000000253634>] constant_timer_interrupt+0x64/0x80 [<9000000000349938>] __handle_irq_event_percpu+0x78/0x1a0 [<9000000000349a78>] handle_irq_event_percpu+0x18/0x88 [<9000000000354c00>] handle_percpu_irq+0x90/0xf0 [<9000000000348c74>] handle_irq_desc+0x94/0xb8 [<9000000001012b28>] handle_cpu_irq+0x68/0xa0 [<9000000001def8c0>] handle_loongarch_irq+0x30/0x48 [<9000000001def958>] do_vint+0x80/0xd0 [<9000000000268a0c>] kasan_mem_to_shadow.part.0+0x2c/0x2a0 [<90000000006344f4>] __asan_load8+0x4c/0x120 [<900000000025c0d0>] module_frob_arch_sections+0x5c8/0x6b8 [<90000000003895f0>] load_module+0x9e0/0x2958 [<900000000038b770>] __do_sys_init_module+0x208/0x2d0 [<9000000001df0c34>] do_syscall+0x94/0x190 [<900000000024d6fc>] handle_syscall+0xbc/0x158 After analysis, this is because the slow speed of loading the amdgpu module leads to the long time occupation of the cpu and then the soft deadlock. When loading a module, module_frob_arch_sections() tries to figure out the number of PLTs/GOTs that will be needed to handle all the RELAs. It will call the count_max_entries() to find in an out-of-order date which counting algorithm has O(n^2) complexity. To make it faster, we sort the relocation list by info and addend. That way, to check for a duplicate relocation, it just needs to compare with the previous entry. This reduces the complexity of the algorithm to O(n log n), as done in commit d4e0340919fb ("arm64/module: Optimize module load time by optimizing PLT counting"). This gives sinificant reduction in module load time for modules with large number of relocations. After applying this patch, the soft deadlock problem has been solved, and the kernel starts normally without "Call Trace". Using the default configuration to test some modules, the results are as follows: Module Size ip_tables 36K fat 143K radeon 2.5MB amdgpu 16MB Without this patch: Module Module load time (ms) Count(PLTs/GOTs) ip_tables 18 59/6 fat 0 162/14 radeon 54 1221/84 amdgpu 1411 4525/1098 With this patch: Module Module load time (ms) Count(PLTs/GOTs) ip_tables 18 59/6 fat 0 162/14 radeon 22 1221/84 amdgpu 45 4525/1098

Informations du CVE

Faiblesses connexes

CWE-ID Nom de la faiblesse Source
CWE-667 Improper Locking
The product does not properly acquire or release a lock on a resource, leading to unexpected resource state changes and behaviors.

Métriques

Métriques Score Gravité CVSS Vecteur 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 est un modèle de notation qui prédit la probabilité qu'une vulnérabilité soit exploitée.

Score EPSS

Le modèle EPSS produit un score de probabilité compris entre 0 et 1 (0 et 100 %). Plus la note est élevée, plus la probabilité qu'une vulnérabilité soit exploitée est grande.

Percentile EPSS

Le percentile est utilisé pour classer les CVE en fonction de leur score EPSS. Par exemple, une CVE dans le 95e percentile selon son score EPSS est plus susceptible d'être exploitée que 95 % des autres CVE. Ainsi, le percentile sert à comparer le score EPSS d'une CVE par rapport à d'autres CVE.

Products Mentioned

Configuraton 0

Linux>>Linux_kernel >> Version From (including) 5.19 To (excluding) 6.6.103

Linux>>Linux_kernel >> Version From (including) 6.7 To (excluding) 6.12.44

Linux>>Linux_kernel >> Version From (including) 6.13 To (excluding) 6.16.4

Linux>>Linux_kernel >> Version 6.17

Linux>>Linux_kernel >> Version 6.17

Références