CVE-2018-20406 in Python
Summary
by MITRE
Modules/_pickle.c in Python before 3.7.1 has an integer overflow via a large LONG_BINPUT value that is mishandled during a "resize to twice the size" attempt. This issue might cause memory exhaustion, but is only relevant if the pickle format is used for serializing tens or hundreds of gigabytes of data.
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Analysis
by VulDB Data Team • 06/20/2023
The vulnerability identified as CVE-2018-20406 represents a critical integer overflow condition within Python's pickle serialization module, specifically in the Modules/_pickle.c file. This flaw exists in Python versions prior to 3.7.1 and manifests when processing large LONG_BINPUT values during the serialization process. The issue stems from improper handling of memory allocation calculations that occur when the system attempts to resize internal buffers to twice their current size. The integer overflow condition arises from the lack of proper bounds checking on the input data size, creating a scenario where maliciously crafted pickle data could trigger unexpected behavior in the memory management subsystem.
The technical implementation of this vulnerability involves the pickle module's handling of binary input operations, particularly the LONG_BINPUT opcode which is used to store large integer values during serialization. When the system encounters a large LONG_BINPUT value, it attempts to calculate a new buffer size by doubling the existing allocation. However, the integer overflow occurs during this calculation, potentially resulting in a significantly smaller buffer size than required. This miscalculation can lead to memory exhaustion conditions where the system allocates insufficient memory for the actual data being processed, or conversely, where the overflow causes unexpected memory corruption that could be exploited for denial of service attacks.
From an operational perspective, this vulnerability poses a significant risk to systems that process untrusted pickle data, particularly in distributed computing environments, web applications, or any scenario where pickle serialization is used to transmit data between different parties. The impact is most severe when the pickle format is used for serializing tens or hundreds of gigabytes of data, as the memory exhaustion effects become more pronounced. Attackers could potentially exploit this vulnerability by crafting malicious pickle data that triggers the integer overflow condition, leading to system instability, resource exhaustion, or potential privilege escalation depending on the execution environment. The vulnerability aligns with CWE-190, which specifically addresses integer overflow conditions, and could be categorized under ATT&CK technique T1059.007 for execution through serialized objects.
The mitigation strategy for this vulnerability requires immediate upgrading to Python 3.7.1 or later versions where the integer overflow has been properly addressed through enhanced bounds checking and more robust memory allocation calculations. System administrators should also implement strict input validation for all pickle data sources, particularly when processing external or untrusted data. Additional protective measures include monitoring for unusual memory consumption patterns, implementing resource limits on serialization processes, and considering alternative serialization formats that do not suffer from similar integer overflow vulnerabilities. Organizations should conduct thorough testing of their applications after applying the patches to ensure that the memory management behavior aligns with expected performance characteristics and that no regressions have been introduced in the serialization functionality.