CVE-2022-49414 : Detail

CVE-2022-49414

4.7
/
Medium
0.04%V4
Local
2025-02-26
02h12 +00:00
2025-02-26
02h12 +00:00
CVE.org
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CVE Descriptions

ext4: fix race condition between ext4_write and ext4_convert_inline_data

In the Linux kernel, the following vulnerability has been resolved: ext4: fix race condition between ext4_write and ext4_convert_inline_data Hulk Robot reported a BUG_ON: ================================================================== EXT4-fs error (device loop3): ext4_mb_generate_buddy:805: group 0, block bitmap and bg descriptor inconsistent: 25 vs 31513 free clusters kernel BUG at fs/ext4/ext4_jbd2.c:53! invalid opcode: 0000 [#1] SMP KASAN PTI CPU: 0 PID: 25371 Comm: syz-executor.3 Not tainted 5.10.0+ #1 RIP: 0010:ext4_put_nojournal fs/ext4/ext4_jbd2.c:53 [inline] RIP: 0010:__ext4_journal_stop+0x10e/0x110 fs/ext4/ext4_jbd2.c:116 [...] Call Trace: ext4_write_inline_data_end+0x59a/0x730 fs/ext4/inline.c:795 generic_perform_write+0x279/0x3c0 mm/filemap.c:3344 ext4_buffered_write_iter+0x2e3/0x3d0 fs/ext4/file.c:270 ext4_file_write_iter+0x30a/0x11c0 fs/ext4/file.c:520 do_iter_readv_writev+0x339/0x3c0 fs/read_write.c:732 do_iter_write+0x107/0x430 fs/read_write.c:861 vfs_writev fs/read_write.c:934 [inline] do_pwritev+0x1e5/0x380 fs/read_write.c:1031 [...] ================================================================== Above issue may happen as follows: cpu1 cpu2 __________________________|__________________________ do_pwritev vfs_writev do_iter_write ext4_file_write_iter ext4_buffered_write_iter generic_perform_write ext4_da_write_begin vfs_fallocate ext4_fallocate ext4_convert_inline_data ext4_convert_inline_data_nolock ext4_destroy_inline_data_nolock clear EXT4_STATE_MAY_INLINE_DATA ext4_map_blocks ext4_ext_map_blocks ext4_mb_new_blocks ext4_mb_regular_allocator ext4_mb_good_group_nolock ext4_mb_init_group ext4_mb_init_cache ext4_mb_generate_buddy --> error ext4_test_inode_state(inode, EXT4_STATE_MAY_INLINE_DATA) ext4_restore_inline_data set EXT4_STATE_MAY_INLINE_DATA ext4_block_write_begin ext4_da_write_end ext4_test_inode_state(inode, EXT4_STATE_MAY_INLINE_DATA) ext4_write_inline_data_end handle=NULL ext4_journal_stop(handle) __ext4_journal_stop ext4_put_nojournal(handle) ref_cnt = (unsigned long)handle BUG_ON(ref_cnt == 0) ---> BUG_ON The lock held by ext4_convert_inline_data is xattr_sem, but the lock held by generic_perform_write is i_rwsem. Therefore, the two locks can be concurrent. To solve above issue, we add inode_lock() for ext4_convert_inline_data(). At the same time, move ext4_convert_inline_data() in front of ext4_punch_hole(), remove similar handling from ext4_punch_hole().

CVE Informations

Related Weaknesses

CWE-ID Weakness Name Source
CWE-362 Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition')
The product contains a concurrent code sequence that requires temporary, exclusive access to a shared resource, but a timing window exists in which the shared resource can be modified by another code sequence operating concurrently.

Metrics

Metrics Score Severity CVSS Vector Source
V3.1 4.7 MEDIUM CVSS:3.1/AV:L/AC:H/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.

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.

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.
EPSS First.org

Products Mentioned

Configuraton 0

Linux>>Linux_kernel >> Version From (including) 3.8 To (excluding) 5.4.207

Linux>>Linux_kernel >> Version From (including) 5.5 To (excluding) 5.10.132

Linux>>Linux_kernel >> Version From (including) 5.11 To (excluding) 5.15.46

Linux>>Linux_kernel >> Version From (including) 5.16 To (excluding) 5.17.14

Linux>>Linux_kernel >> Version From (including) 5.18 To (excluding) 5.18.3

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.