CVE-2024-40927 : Detail

CVE-2024-40927

7.8
/
High
Memory Corruption
0.01%V4
Local
2024-07-12
12h25 +00:00
2025-11-03
21h57 +00:00
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CVE Descriptions

xhci: Handle TD clearing for multiple streams case

In the Linux kernel, the following vulnerability has been resolved: xhci: Handle TD clearing for multiple streams case When multiple streams are in use, multiple TDs might be in flight when an endpoint is stopped. We need to issue a Set TR Dequeue Pointer for each, to ensure everything is reset properly and the caches cleared. Change the logic so that any N>1 TDs found active for different streams are deferred until after the first one is processed, calling xhci_invalidate_cancelled_tds() again from xhci_handle_cmd_set_deq() to queue another command until we are done with all of them. Also change the error/"should never happen" paths to ensure we at least clear any affected TDs, even if we can't issue a command to clear the hardware cache, and complain loudly with an xhci_warn() if this ever happens. This problem case dates back to commit e9df17eb1408 ("USB: xhci: Correct assumptions about number of rings per endpoint.") early on in the XHCI driver's life, when stream support was first added. It was then identified but not fixed nor made into a warning in commit 674f8438c121 ("xhci: split handling halted endpoints into two steps"), which added a FIXME comment for the problem case (without materially changing the behavior as far as I can tell, though the new logic made the problem more obvious). Then later, in commit 94f339147fc3 ("xhci: Fix failure to give back some cached cancelled URBs."), it was acknowledged again. [Mathias: commit 94f339147fc3 ("xhci: Fix failure to give back some cached cancelled URBs.") was a targeted regression fix to the previously mentioned patch. Users reported issues with usb stuck after unmounting/disconnecting UAS devices. This rolled back the TD clearing of multiple streams to its original state.] Apparently the commit author was aware of the problem (yet still chose to submit it): It was still mentioned as a FIXME, an xhci_dbg() was added to log the problem condition, and the remaining issue was mentioned in the commit description. The choice of making the log type xhci_dbg() for what is, at this point, a completely unhandled and known broken condition is puzzling and unfortunate, as it guarantees that no actual users would see the log in production, thereby making it nigh undebuggable (indeed, even if you turn on DEBUG, the message doesn't really hint at there being a problem at all). It took me *months* of random xHC crashes to finally find a reliable repro and be able to do a deep dive debug session, which could all have been avoided had this unhandled, broken condition been actually reported with a warning, as it should have been as a bug intentionally left in unfixed (never mind that it shouldn't have been left in at all). > Another fix to solve clearing the caches of all stream rings with > cancelled TDs is needed, but not as urgent. 3 years after that statement and 14 years after the original bug was introduced, I think it's finally time to fix it. And maybe next time let's not leave bugs unfixed (that are actually worse than the original bug), and let's actually get people to review kernel commits please. Fixes xHC crashes and IOMMU faults with UAS devices when handling errors/faults. Easiest repro is to use `hdparm` to mark an early sector (e.g. 1024) on a disk as bad, then `cat /dev/sdX > /dev/null` in a loop. At least in the case of JMicron controllers, the read errors end up having to cancel two TDs (for two queued requests to different streams) and the one that didn't get cleared properly ends up faulting the xHC entirely when it tries to access DMA pages that have since been unmapped, referred to by the stale TDs. This normally happens quickly (after two or three loops). After this fix, I left the `cat` in a loop running overnight and experienced no xHC failures, with all read errors recovered properly. Repro'd and tested on an Apple M1 Mac Mini (dwc3 host). On systems without an IOMMU, this bug would instead silently corrupt freed memory, making this a ---truncated---

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

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.

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) 2.6.35 To (excluding) 5.15.162

Linux>>Linux_kernel >> Version From (including) 5.16 To (excluding) 6.1.95

Linux>>Linux_kernel >> Version From (including) 6.2 To (excluding) 6.6.35

Linux>>Linux_kernel >> Version From (including) 6.7 To (excluding) 6.9.6

Linux>>Linux_kernel >> Version 6.10

Linux>>Linux_kernel >> Version 6.10

Linux>>Linux_kernel >> Version 6.10

References