CVE-2023-52886:详情

CVE-2023-52886

6.4
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中危
0.05%V4
2024-07-16
10h15 +00:00
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Physical
2024-11-21
08h40 +00:00
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CVE 描述

In the Linux kernel, the following vulnerability has been resolved: USB: core: Fix race by not overwriting udev->descriptor in hub_port_init() Syzbot reported an out-of-bounds read in sysfs.c:read_descriptors(): BUG: KASAN: slab-out-of-bounds in read_descriptors+0x263/0x280 drivers/usb/core/sysfs.c:883 Read of size 8 at addr ffff88801e78b8c8 by task udevd/5011 CPU: 0 PID: 5011 Comm: udevd Not tainted 6.4.0-rc6-syzkaller-00195-g40f71e7cd3c6 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 05/27/2023 Call Trace: __dump_stack lib/dump_stack.c:88 [inline] dump_stack_lvl+0xd9/0x150 lib/dump_stack.c:106 print_address_description.constprop.0+0x2c/0x3c0 mm/kasan/report.c:351 print_report mm/kasan/report.c:462 [inline] kasan_report+0x11c/0x130 mm/kasan/report.c:572 read_descriptors+0x263/0x280 drivers/usb/core/sysfs.c:883 ... Allocated by task 758: ... __do_kmalloc_node mm/slab_common.c:966 [inline] __kmalloc+0x5e/0x190 mm/slab_common.c:979 kmalloc include/linux/slab.h:563 [inline] kzalloc include/linux/slab.h:680 [inline] usb_get_configuration+0x1f7/0x5170 drivers/usb/core/config.c:887 usb_enumerate_device drivers/usb/core/hub.c:2407 [inline] usb_new_device+0x12b0/0x19d0 drivers/usb/core/hub.c:2545 As analyzed by Khazhy Kumykov, the cause of this bug is a race between read_descriptors() and hub_port_init(): The first routine uses a field in udev->descriptor, not expecting it to change, while the second overwrites it. Prior to commit 45bf39f8df7f ("USB: core: Don't hold device lock while reading the "descriptors" sysfs file") this race couldn't occur, because the routines were mutually exclusive thanks to the device locking. Removing that locking from read_descriptors() exposed it to the race. The best way to fix the bug is to keep hub_port_init() from changing udev->descriptor once udev has been initialized and registered. Drivers expect the descriptors stored in the kernel to be immutable; we should not undermine this expectation. In fact, this change should have been made long ago. So now hub_port_init() will take an additional argument, specifying a buffer in which to store the device descriptor it reads. (If udev has not yet been initialized, the buffer pointer will be NULL and then hub_port_init() will store the device descriptor in udev as before.) This eliminates the data race responsible for the out-of-bounds read. The changes to hub_port_init() appear more extensive than they really are, because of indentation changes resulting from an attempt to avoid writing to other parts of the usb_device structure after it has been initialized. Similar changes should be made to the code that reads the BOS descriptor, but that can be handled in a separate patch later on. This patch is sufficient to fix the bug found by syzbot.

CVE 信息

相关弱点

CWE-ID 弱点名称 来源
CWE-125 Out-of-bounds Read
The product reads data past the end, or before the beginning, of the intended buffer.

指标

指标 评分 严重程度 CVSS 向量 来源
V3.1 6.4 MEDIUM CVSS:3.1/AV:P/AC:H/PR:N/UI:N/S:U/C:H/I:H/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.

Physical

The attack requires the attacker to physically touch or manipulate the vulnerable component. Physical interaction may be brief (e.g., evil maid attack1) or persistent. An example of such an attack is a cold boot attack in which an attacker gains access to disk encryption keys after physically accessing the target system. Other examples include peripheral attacks via FireWire/USB Direct Memory Access (DMA).

Attack Complexity

This metric describes the conditions beyond the attacker’s control that must exist in order to exploit the vulnerability.

High

successful attack depends on conditions beyond the attacker's control. That is, a successful attack cannot be accomplished at will, but requires the attacker to invest in some measurable amount of effort in preparation or execution against the vulnerable component before a successful attack can be expected.

Privileges Required

This metric describes the level of privileges an attacker must possess before successfully exploiting the vulnerability.

None

The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files of the vulnerable system to carry out an attack.

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.

nvd@nist.gov

EPSS

EPSS 是一种预测漏洞被利用可能性的评分模型。

EPSS 评分

EPSS 模型生成一个介于 0 到 1(即 0% 到 100%)之间的概率评分。评分越高,漏洞被利用的可能性越大。

EPSS 百分位

百分位用于根据 EPSS 评分对 CVE 进行排名。例如,EPSS 百分位为第 95 的 CVE,其被利用的可能性高于 95% 的其他 CVE。因此,百分位可用于比较不同 CVE 的 EPSS 评分。

相关产品

配置 0

Linux>>Linux_kernel >> Version 从(含) 5.10.171 至(不含) 5.10.195

Linux>>Linux_kernel >> Version 从(含) 5.15.97 至(不含) 5.15.132

Linux>>Linux_kernel >> Version 从(含) 6.1.15 至(不含) 6.1.53

Linux>>Linux_kernel >> Version 从(含) 6.3 至(不含) 6.4.16

Linux>>Linux_kernel >> Version 从(含) 6.5 至(不含) 6.5.3

参考资料