CVE-2024-53190 : Detail

CVE-2024-53190

5.5
/
Medium
0.01%V4
Local
2024-12-27
13h49 +00:00
2025-11-03
20h47 +00:00
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CVE Descriptions

wifi: rtlwifi: Drastically reduce the attempts to read efuse in case of failures

In the Linux kernel, the following vulnerability has been resolved: wifi: rtlwifi: Drastically reduce the attempts to read efuse in case of failures Syzkaller reported a hung task with uevent_show() on stack trace. That specific issue was addressed by another commit [0], but even with that fix applied (for example, running v6.12-rc5) we face another type of hung task that comes from the same reproducer [1]. By investigating that, we could narrow it to the following path: (a) Syzkaller emulates a Realtek USB WiFi adapter using raw-gadget and dummy_hcd infrastructure. (b) During the probe of rtl8192cu, the driver ends-up performing an efuse read procedure (which is related to EEPROM load IIUC), and here lies the issue: the function read_efuse() calls read_efuse_byte() many times, as loop iterations depending on the efuse size (in our example, 512 in total). This procedure for reading efuse bytes relies in a loop that performs an I/O read up to *10k* times in case of failures. We measured the time of the loop inside read_efuse_byte() alone, and in this reproducer (which involves the dummy_hcd emulation layer), it takes 15 seconds each. As a consequence, we have the driver stuck in its probe routine for big time, exposing a stack trace like below if we attempt to reboot the system, for example: task:kworker/0:3 state:D stack:0 pid:662 tgid:662 ppid:2 flags:0x00004000 Workqueue: usb_hub_wq hub_event Call Trace: __schedule+0xe22/0xeb6 schedule_timeout+0xe7/0x132 __wait_for_common+0xb5/0x12e usb_start_wait_urb+0xc5/0x1ef ? usb_alloc_urb+0x95/0xa4 usb_control_msg+0xff/0x184 _usbctrl_vendorreq_sync+0xa0/0x161 _usb_read_sync+0xb3/0xc5 read_efuse_byte+0x13c/0x146 read_efuse+0x351/0x5f0 efuse_read_all_map+0x42/0x52 rtl_efuse_shadow_map_update+0x60/0xef rtl_get_hwinfo+0x5d/0x1c2 rtl92cu_read_eeprom_info+0x10a/0x8d5 ? rtl92c_read_chip_version+0x14f/0x17e rtl_usb_probe+0x323/0x851 usb_probe_interface+0x278/0x34b really_probe+0x202/0x4a4 __driver_probe_device+0x166/0x1b2 driver_probe_device+0x2f/0xd8 [...] We propose hereby to drastically reduce the attempts of doing the I/O reads in case of failures, restricted to USB devices (given that they're inherently slower than PCIe ones). By retrying up to 10 times (instead of 10000), we got reponsiveness in the reproducer, while seems reasonable to believe that there's no sane USB device implementation in the field requiring this amount of retries at every I/O read in order to properly work. Based on that assumption, it'd be good to have it backported to stable but maybe not since driver implementation (the 10k number comes from day 0), perhaps up to 6.x series makes sense. [0] Commit 15fffc6a5624 ("driver core: Fix uevent_show() vs driver detach race") [1] A note about that: this syzkaller report presents multiple reproducers that differs by the type of emulated USB device. For this specific case, check the entry from 2024/08/08 06:23 in the list of crashes; the C repro is available at https://syzkaller.appspot.com/text?tag=ReproC&x=1521fc83980000.

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.

Products Mentioned

Configuraton 0

Linux>>Linux_kernel >> Version From (including) 2.6.38 To (excluding) 6.1.120

Linux>>Linux_kernel >> Version From (including) 6.2 To (excluding) 6.6.64

Linux>>Linux_kernel >> Version From (including) 6.7 To (excluding) 6.11.11

Linux>>Linux_kernel >> Version From (including) 6.12 To (excluding) 6.12.2

References