
Cloud Vulnerability DB
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CVE-2022-48628 is a vulnerability in the Linux kernel's Ceph filesystem implementation that affects the unmounting process. The vulnerability was discovered and disclosed in March 2024. When unmounting a Ceph filesystem, all dirty buffers are flushed and after the last OSD request is finished, the last reference of the i_count is released. The issue occurs when flushing dirty cap/snap to MDSs, where the unmounting process doesn't wait for possible acknowledgments, which can lead to skipped inodes during eviction (NVD).
The vulnerability manifests when encrypted filesystems are in use, causing the kernel to generate warnings when removing encryption keys while skipped inodes still hold the keyring. This can lead to a crash when iput() attempts to dereference the sb->s_master_keys which has been released by generic_shutdown_super(). The vulnerability has been assigned a CVSS v3.1 base score of 5.5 (Medium) with vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H (NVD).
The primary impact of this vulnerability is system instability and potential crashes during the unmounting of Ceph filesystems. This is particularly problematic when encryption is enabled, as it can lead to kernel warnings and subsequent system crashes when attempting to handle encrypted inodes during the unmount process (NVD).
The vulnerability requires local access and can be triggered during the unmounting process of a Ceph filesystem. It specifically affects scenarios where encrypted filesystems are in use and occurs during the normal operation of unmounting, rather than requiring specific exploit code (NVD).
The vulnerability has been patched in the Linux kernel. The fix involves properly handling the unmounting process by implementing a stopping blocker mechanism that ensures proper cleanup of resources and prevents the race condition during unmount. The patch adds new functions ceph_inc_mds_stopping_blocker() and ceph_dec_mds_stopping_blocker() to manage this process (Kernel Patch).
Source: This report was generated using AI
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