CVE-2013-1424 in matplotlib
Summary
by MITRE • 06/26/2025
Buffer overflow vulnerability in matplotlib.This issue affects matplotlib: before upstream commit ba4016014cb4fb4927e36ce8ea429fed47dcb787.
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Analysis
by VulDB Data Team • 06/30/2025
The buffer overflow vulnerability identified as CVE-2013-1424 resides within the matplotlib plotting library, a widely used Python library for creating static, animated, and interactive visualizations. This vulnerability represents a critical security flaw that could potentially allow attackers to execute arbitrary code or cause denial of service conditions when the library processes malformed input data. The issue specifically manifests in versions of matplotlib prior to the upstream commit ba4016014cb4fb4927e36ce8ea429fed47dcb787, indicating that the vulnerability was introduced in a specific code path related to data handling within the library's internal processing mechanisms. The vulnerability affects systems that rely on matplotlib for data visualization, particularly in environments where the library processes untrusted input from external sources or user-generated content.
The technical flaw stems from inadequate bounds checking in matplotlib's internal buffer management routines when processing certain types of data structures. When the library encounters malformed input data, particularly in the context of handling complex data arrays or specific file formats, the buffer overflow occurs due to insufficient validation of input sizes before memory allocation. This condition allows an attacker to write data beyond the allocated buffer boundaries, potentially overwriting adjacent memory locations with malicious content. The vulnerability operates at the memory management level, where the library fails to properly validate the size of incoming data before attempting to store it in fixed-size buffers, creating an exploitable condition that aligns with CWE-121, which describes heap-based buffer overflow conditions. The specific nature of the vulnerability suggests that it may be triggered through improper handling of data arrays, particularly when processing certain types of numerical data or when dealing with malformed data files that the library attempts to parse and render.
The operational impact of this vulnerability extends beyond simple denial of service scenarios to potentially enable remote code execution on systems where matplotlib is deployed with sufficient privileges. Attackers could exploit this condition by crafting malicious input data that, when processed by matplotlib, triggers the buffer overflow and allows for arbitrary code execution within the context of the application using the library. This makes the vulnerability particularly dangerous in web applications or server environments where matplotlib might be processing user-uploaded data or data from external sources without proper sanitization. The vulnerability affects various deployment scenarios including web applications, scientific computing environments, and data analysis platforms where matplotlib serves as a core visualization component. The risk is compounded by the widespread adoption of matplotlib across different domains, making the potential attack surface extensive and increasing the likelihood of exploitation in real-world environments. Systems that process large datasets or handle user-generated content through matplotlib are particularly vulnerable, as these scenarios provide multiple entry points for attackers to deliver malicious payloads.
Mitigation strategies for CVE-2013-1424 focus primarily on updating to patched versions of matplotlib that contain the fix implemented in commit ba4016014cb4fb4927e36ce8ea429fed47dcb787. Organizations should prioritize upgrading their matplotlib installations to versions that address the buffer overflow condition through proper bounds checking and input validation. Additionally, implementing proper input sanitization measures can help reduce the risk of exploitation even in environments where immediate upgrades are not feasible. Security measures should include restricting the execution privileges of applications using matplotlib, implementing network segmentation to limit exposure, and monitoring for suspicious usage patterns that might indicate exploitation attempts. The fix addresses the underlying CWE-121 vulnerability through improved memory management practices and enhanced validation of input data sizes before buffer allocation, aligning with recommended practices in the software security domain. Organizations should also consider implementing application whitelisting, using sandboxing techniques, and employing runtime protection mechanisms to further reduce the risk of exploitation. The vulnerability demonstrates the importance of maintaining up-to-date software dependencies and implementing robust security practices in scientific computing and data visualization environments where libraries like matplotlib are extensively used across various industries and research domains.