CVE-2024-42239 : Detail

CVE-2024-42239

5.5
/
Medium
0.04%V3
Local
2024-08-07
15h14 +00:00
2024-12-19
09h14 +00:00
CVE.org
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CVE Descriptions

bpf: Fail bpf_timer_cancel when callback is being cancelled

In the Linux kernel, the following vulnerability has been resolved: bpf: Fail bpf_timer_cancel when callback is being cancelled Given a schedule: timer1 cb timer2 cb bpf_timer_cancel(timer2); bpf_timer_cancel(timer1); Both bpf_timer_cancel calls would wait for the other callback to finish executing, introducing a lockup. Add an atomic_t count named 'cancelling' in bpf_hrtimer. This keeps track of all in-flight cancellation requests for a given BPF timer. Whenever cancelling a BPF timer, we must check if we have outstanding cancellation requests, and if so, we must fail the operation with an error (-EDEADLK) since cancellation is synchronous and waits for the callback to finish executing. This implies that we can enter a deadlock situation involving two or more timer callbacks executing in parallel and attempting to cancel one another. Note that we avoid incrementing the cancelling counter for the target timer (the one being cancelled) if bpf_timer_cancel is not invoked from a callback, to avoid spurious errors. The whole point of detecting cur->cancelling and returning -EDEADLK is to not enter a busy wait loop (which may or may not lead to a lockup). This does not apply in case the caller is in a non-callback context, the other side can continue to cancel as it sees fit without running into errors. Background on prior attempts: Earlier versions of this patch used a bool 'cancelling' bit and used the following pattern under timer->lock to publish cancellation status. lock(t->lock); t->cancelling = true; mb(); if (cur->cancelling) return -EDEADLK; unlock(t->lock); hrtimer_cancel(t->timer); t->cancelling = false; The store outside the critical section could overwrite a parallel requests t->cancelling assignment to true, to ensure the parallely executing callback observes its cancellation status. It would be necessary to clear this cancelling bit once hrtimer_cancel is done, but lack of serialization introduced races. Another option was explored where bpf_timer_start would clear the bit when (re)starting the timer under timer->lock. This would ensure serialized access to the cancelling bit, but may allow it to be cleared before in-flight hrtimer_cancel has finished executing, such that lockups can occur again. Thus, we choose an atomic counter to keep track of all outstanding cancellation requests and use it to prevent lockups in case callbacks attempt to cancel each other while executing in parallel.

CVE Informations

Related Weaknesses

CWE-ID Weakness Name 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.

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.

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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) 5.15 To (excluding) 6.6.41

Linux>>Linux_kernel >> Version From (including) 6.7 To (excluding) 6.9.10

References