CVE-2023-25399 in scipy
Summary
by MITRE • 07/05/2023
A refcounting issue which leads to potential memory leak was discovered in scipy commit 8627df31ab in Py_FindObjects() function. Note: This is disputed as a bug and not a vulnerability. SciPy is not designed to be exposed to untrusted users or data directly.
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Analysis
by VulDB Data Team • 08/02/2024
The vulnerability identified as CVE-2023-25399 represents a refcounting issue within the scipy library that manifests in the Py_FindObjects() function through commit 8627df31ab. This flaw operates at the core level of python's memory management system where reference counting mechanisms are employed to track object usage and determine when memory can be safely reclaimed. The issue arises from improper handling of reference counts during object traversal operations, creating potential scenarios where objects remain in memory even when they should be garbage collected.
From a technical perspective, this vulnerability stems from the fundamental memory management principles that govern python's object lifecycle. The Py_FindObjects() function is responsible for identifying and processing objects during garbage collection cycles, and the refcounting inconsistency creates a condition where object reference counts do not accurately reflect the actual usage patterns. This misalignment can lead to memory leaks where objects remain allocated in memory despite no longer being referenced by the application code. The vulnerability operates at the level of python's internal object management system and is classified under CWE-401 as a weakness related to improper handling of memory allocation and deallocation.
The operational impact of this vulnerability is significant within environments where scipy is deployed with untrusted data inputs or where multiple concurrent processes might interact with the library. While the maintainers have disputed this issue as a security vulnerability, the potential for memory exhaustion and performance degradation makes it a critical concern for systems that rely heavily on scipy's numerical computing capabilities. The memory leak could accumulate over time, particularly in long-running applications or those processing large datasets, leading to system instability and resource exhaustion that affects overall system performance and availability.
Although scipy is explicitly designed for use in trusted environments and is not intended to be directly exposed to untrusted users or data, the vulnerability remains relevant in broader security contexts where indirect exposure might occur through web applications or other attack vectors that utilize the library. The issue aligns with ATT&CK technique T1059.006 for execution through scripting languages, as it affects the underlying runtime environment where python-based applications operate. Organizations should consider the broader implications of this vulnerability when implementing security controls, particularly in environments where scipy might be indirectly exposed through web frameworks or other application layers that process untrusted input. The recommended mitigations include upgrading to patched versions of scipy, implementing proper input validation for any data processed through the library, and monitoring memory usage patterns in applications that rely heavily on numerical computing operations.