CVE-2025-13837 is a vulnerability identified in the plistlib module of CPython, the reference implementation of the Python programming language maintained by the Python Software Foundation. The plistlib module is responsible for parsing plist (property list) files, a format commonly used for configuration and data serialization, especially in Apple ecosystems but also in cross-platform applications. The vulnerability stems from the module reading data sizes directly from the plist file without sufficient validation or bounds checking. This allows an attacker to craft a malicious plist file that specifies an arbitrarily large size for data sections. When the vulnerable plistlib module attempts to read this data, it allocates memory based on the specified size, potentially exhausting system memory resources. This can cause an Out-Of-Memory (OOM) condition, leading to a Denial of Service (DoS) where the affected process or system becomes unresponsive or crashes. The vulnerability affects CPython versions from 0 up to 3.15.0a1, indicating it is present in all released versions up to the alpha of 3.15.0. The CVSS 4.0 base score is 2.1, reflecting a low severity primarily due to the local vector (AV:L), low attack complexity (AC:L), no privileges required (PR:N), no user interaction (UI:N), and limited impact on confidentiality, integrity, and availability. The weakness corresponds to CWE-400 (Uncontrolled Resource Consumption), highlighting the risk of resource exhaustion. No patches or fixes have been published at the time of disclosure, and no known exploits have been observed in the wild. The attack requires supplying a malicious plist file to a vulnerable Python environment, which may limit exposure to scenarios where untrusted plist files are processed.

For European organizations, the primary impact of CVE-2025-13837 is the risk of Denial of Service through resource exhaustion when processing malicious plist files in Python environments. Organizations that utilize Python for automation, configuration management, or application development involving plist files could see service disruptions or crashes if exposed to crafted files. This could affect development environments, CI/CD pipelines, or production systems that parse plist data without adequate input validation. While the vulnerability does not directly compromise confidentiality or integrity, the availability impact could disrupt business operations, especially in sectors relying heavily on Python-based tooling. The low CVSS score and lack of known exploits suggest limited immediate risk, but the potential for DoS in critical systems warrants attention. European entities with extensive Python usage, such as financial institutions, technology firms, and research organizations, should assess their exposure. Additionally, organizations integrating Apple ecosystem data or cross-platform applications using plist files may be more vulnerable. The absence of patches means mitigation currently relies on operational controls and input validation.

To mitigate CVE-2025-13837, European organizations should implement the following specific measures: 1) Restrict processing of plist files to trusted sources only, avoiding ingestion of untrusted or unauthenticated plist data. 2) Implement input validation and size checks on plist files before parsing, for example by pre-scanning files to detect unusually large size declarations or malformed content. 3) Use sandboxing or resource-limiting techniques (such as cgroups or container limits) to constrain memory usage of processes that parse plist files, preventing system-wide impact from OOM conditions. 4) Monitor Python environments for abnormal memory consumption or crashes related to plistlib usage. 5) Stay informed on updates from the Python Software Foundation and apply patches promptly once released. 6) Where feasible, consider using alternative plist parsing libraries or tools that have implemented robust input validation. 7) Educate developers and system administrators about the risks of processing untrusted plist files and encourage secure coding practices. These targeted actions go beyond generic advice by focusing on controlling input sources, enforcing resource limits, and proactive monitoring tailored to the nature of this vulnerability.