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A Type Confusion vulnerability (CVE-2023-42464) was discovered in the Spotlight RPC functions in Netatalk's afpd daemon, affecting versions 3.1.0 through 3.1.16. The vulnerability was found by Florent Saudel and Arnaud Gatignol of Thalium team and was disclosed on September 16, 2023. The issue affects the Apple Filing Protocol (AFP) service implementation, particularly in systems running Netatalk for providing file services to macOS clients (Netatalk Security, GitHub Issue).
The vulnerability stems from a lack of type checking in callers of the dallocvaluefor_key() function, which returns the object associated with a key. When parsing Spotlight RPC packets, one encoded data structure is a key-value style dictionary where the keys are character strings, and the values can be any of the supported types in the underlying protocol. The CVSS v3.1 base score is 9.8 (CRITICAL) with a vector of CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H. The vulnerability shares common heritage with Samba and is logically identical to CVE-2023-34967 (NVD, Netatalk Security).
Due to the type confusion vulnerability, a malicious actor may be able to fully control the value of the pointer and potentially achieve Remote Code Execution on the host. The vulnerability has received a critical severity rating due to its potential for complete system compromise without requiring authentication or user interaction (NVD).
The vulnerability has been patched in Netatalk version 3.1.17. Users can either upgrade to this version or apply the patch with git hash a0ee3c2. As a workaround, since Spotlight is an optional feature in Netatalk and is disabled by default, administrators can disable it by setting 'spotlight = no' in their afp.conf file (Netatalk Security).
Multiple Linux distributions have released security advisories and patches for this vulnerability, including Debian and Ubuntu. Debian released DSA-5503-1 and DLA-3584-1 to address this vulnerability in their stable and LTS releases respectively (Debian Security, Debian LTS).
Source: This report was generated using AI
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