CVE-2007-2052 in Python
Summary
by MITRE
Off-by-one error in the PyLocale_strxfrm function in Modules/_localemodule.c for Python 2.4 and 2.5 causes an incorrect buffer size to be used for the strxfrm function, which allows context-dependent attackers to read portions of memory via unknown manipulations that trigger a buffer over-read due to missing null termination.
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Analysis
by VulDB Data Team • 01/15/2025
The vulnerability identified as CVE-2007-2052 represents a critical buffer over-read condition within Python's locale module implementation that affects versions 2.4 and 2.5. This flaw exists in the PyLocale_strxfrm function located in Modules/_localemodule.c, where an off-by-one error manifests during buffer size calculation for the strxfrm system function. The root cause stems from improper handling of string transformation operations within the locale subsystem, creating a scenario where memory boundaries are exceeded during string processing. The vulnerability operates through a context-dependent attack vector where specific input conditions trigger the flawed buffer management, leading to unauthorized memory access patterns that can expose sensitive data to attackers.
The technical implementation of this vulnerability demonstrates a classic buffer over-read scenario that aligns with CWE-121, which describes buffer overflow conditions where data is read beyond the allocated buffer boundaries. The flaw specifically occurs when the PyLocale_strxfrm function calculates buffer sizes without proper null termination handling, causing the strxfrm function to access memory regions beyond its intended scope. This improper buffer management creates opportunities for attackers to manipulate input strings in such a way that they can trigger memory read operations that expose portions of the process memory space, including potentially sensitive information such as stack contents, heap data, or other program variables that may contain authentication tokens or cryptographic keys.
From an operational impact perspective, this vulnerability represents a significant security risk for Python applications running on affected versions, particularly in environments where locale-specific string processing is utilized. The context-dependent nature of the attack means that exploitation requires specific conditions to be met, but once triggered, the vulnerability can lead to information disclosure that may aid in further attacks. Attackers can potentially leverage this flaw to gather information about memory layout, application state, or sensitive data that may be present in adjacent memory regions, which could subsequently be used to facilitate more sophisticated attacks such as privilege escalation or code execution. The vulnerability affects the fundamental string processing capabilities within Python's locale handling mechanism, making it particularly dangerous for applications that rely heavily on internationalization features or locale-sensitive operations.
Mitigation strategies for CVE-2007-2052 should prioritize immediate patching of affected Python installations to version 2.5.1 or later, which includes the corrected buffer management implementation. System administrators should also implement monitoring for unusual string processing patterns that might indicate exploitation attempts, particularly in applications that handle user-provided locale data. The vulnerability demonstrates the importance of proper buffer boundary checking in system libraries, and organizations should conduct thorough vulnerability assessments of their Python environments to identify and remediate similar issues. Additionally, implementing application-level input validation and sanitization can provide additional defense-in-depth measures, though the primary mitigation remains the official Python security patch that corrects the off-by-one error in the buffer size calculation. This vulnerability underscores the critical need for proper memory management practices in system libraries and highlights the potential for seemingly minor implementation flaws to create significant security risks in widely-used software components.