CVE-2024-35970 : Detail

CVE-2024-35970

6.3
/
Medium
0.26%V4
Network
2024-05-20
09h41 +00:00
2024-12-19
08h59 +00:00
CVE.org
NVD
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CVE Descriptions

af_unix: Clear stale u->oob_skb.

In the Linux kernel, the following vulnerability has been resolved: af_unix: Clear stale u->oob_skb. syzkaller started to report deadlock of unix_gc_lock after commit 4090fa373f0e ("af_unix: Replace garbage collection algorithm."), but it just uncovers the bug that has been there since commit 314001f0bf92 ("af_unix: Add OOB support"). The repro basically does the following. from socket import * from array import array c1, c2 = socketpair(AF_UNIX, SOCK_STREAM) c1.sendmsg([b'a'], [(SOL_SOCKET, SCM_RIGHTS, array("i", [c2.fileno()]))], MSG_OOB) c2.recv(1) # blocked as no normal data in recv queue c2.close() # done async and unblock recv() c1.close() # done async and trigger GC A socket sends its file descriptor to itself as OOB data and tries to receive normal data, but finally recv() fails due to async close(). The problem here is wrong handling of OOB skb in manage_oob(). When recvmsg() is called without MSG_OOB, manage_oob() is called to check if the peeked skb is OOB skb. In such a case, manage_oob() pops it out of the receive queue but does not clear unix_sock(sk)->oob_skb. This is wrong in terms of uAPI. Let's say we send "hello" with MSG_OOB, and "world" without MSG_OOB. The 'o' is handled as OOB data. When recv() is called twice without MSG_OOB, the OOB data should be lost. >>> from socket import * >>> c1, c2 = socketpair(AF_UNIX, SOCK_STREAM, 0) >>> c1.send(b'hello', MSG_OOB) # 'o' is OOB data 5 >>> c1.send(b'world') 5 >>> c2.recv(5) # OOB data is not received b'hell' >>> c2.recv(5) # OOB date is skipped b'world' >>> c2.recv(5, MSG_OOB) # This should return an error b'o' In the same situation, TCP actually returns -EINVAL for the last recv(). Also, if we do not clear unix_sk(sk)->oob_skb, unix_poll() always set EPOLLPRI even though the data has passed through by previous recv(). To avoid these issues, we must clear unix_sk(sk)->oob_skb when dequeuing it from recv queue. The reason why the old GC did not trigger the deadlock is because the old GC relied on the receive queue to detect the loop. When it is triggered, the socket with OOB data is marked as GC candidate because file refcount == inflight count (1). However, after traversing all inflight sockets, the socket still has a positive inflight count (1), thus the socket is excluded from candidates. Then, the old GC lose the chance to garbage-collect the socket. With the old GC, the repro continues to create true garbage that will never be freed nor detected by kmemleak as it's linked to the global inflight list. That's why we couldn't even notice the issue.

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 6.3 MEDIUM CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L

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.

Network

The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed below, up to and including the entire Internet. Such a vulnerability is often termed “remotely exploitable” and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).

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.

Low

There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to 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.

Low

Modification of data is possible, but the attacker does not have control over the consequence of a modification, or the amount of modification is limited. The data modification does not have a direct, serious impact on the impacted component.

Availability Impact

This metric measures the impact to the availability of the impacted component resulting from a successfully exploited vulnerability.

Low

Performance is reduced or there are interruptions in resource availability. Even if repeated exploitation of the vulnerability is possible, the attacker does not have the ability to completely deny service to legitimate users. The resources in the impacted component are either partially available all of the time, or fully available only some of the time, but overall there is no direct, serious consequence to the impacted component.

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) 5.15.156

Linux>>Linux_kernel >> Version From (including) 5.16 To (excluding) 6.1.87

Linux>>Linux_kernel >> Version From (including) 6.2 To (excluding) 6.6.28

Linux>>Linux_kernel >> Version From (including) 6.7 To (excluding) 6.8.7

Linux>>Linux_kernel >> Version 6.9

Linux>>Linux_kernel >> Version 6.9

Linux>>Linux_kernel >> Version 6.9

References

The information presented on CVE Find originates from several carefully selected reference sources. CVE data is provided by MITRE Corporation and the National Vulnerability Database (NVD). The Known Exploited Vulnerabilities (KEV) catalog is sourced from the Cybersecurity and Infrastructure Security Agency (CISA), while EPSS scores come from FIRST.org. Additionally, data regarding software weaknesses (CWE) and common attack patterns (CAPEC) is maintained by MITRE Corporation, and information on hardware and software configurations (CPE) is provided by the NVD.