CVE-2018-20230 in PSPP
Summary
by MITRE
An issue was discovered in PSPP 1.2.0. There is a heap-based buffer overflow at the function read_bytes_internal in utilities/pspp-dump-sav.c, which allows attackers to cause a denial of service (application crash) or possibly have unspecified other impact.
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Analysis
by VulDB Data Team • 02/22/2021
The vulnerability identified as CVE-2018-20230 represents a critical heap-based buffer overflow within the PSPP statistical analysis software version 1.2.0. This issue resides in the read_bytes_internal function located in the utilities/pspp-dump-sav.c source file, making it a fundamental flaw in the software's data processing pipeline. The vulnerability manifests when the application processes certain SAV (SPSS Data) files, specifically those that contain malformed or crafted data structures that trigger the buffer overflow condition. This type of vulnerability falls under CWE-121, heap-based buffer overflow, which is classified as a severe memory corruption issue that can lead to arbitrary code execution or system instability. The flaw represents a classic example of inadequate input validation and bounds checking in memory management operations.
The technical implementation of this vulnerability occurs when the read_bytes_internal function fails to properly validate the size of data being read from SAV files before attempting to copy it into allocated memory buffers. Attackers can exploit this by crafting malicious SAV files that contain oversized data structures or malformed headers that cause the function to write beyond the allocated buffer boundaries. This heap corruption can result in memory corruption that manifests as application crashes, segmentation faults, or unpredictable behavior during normal operation. The vulnerability's impact extends beyond simple denial of service since heap corruption can potentially be leveraged for more sophisticated attacks such as code execution or privilege escalation depending on the execution environment and memory layout. The issue demonstrates poor defensive programming practices and inadequate error handling in data parsing operations.
From an operational perspective, this vulnerability creates significant risks for organizations that rely on PSPP for statistical data analysis, particularly in research environments or business intelligence applications where large datasets are processed regularly. The vulnerability can be exploited through social engineering tactics where malicious actors distribute crafted SAV files to unsuspecting users, or through automated exploitation in environments where PSPP processes untrusted data from external sources. The impact includes potential data loss, system downtime, and disruption of analytical workflows that depend on the software's stability. Organizations using PSPP for sensitive data processing may face compliance issues if this vulnerability is exploited to access or corrupt confidential information. The vulnerability also affects the integrity of statistical analysis results since corrupted data processing can lead to incorrect conclusions or analysis failures. This flaw aligns with ATT&CK technique T1203, "Exploitation for Client Execution," and T1059, "Command and Scripting Interpreter," as it can be used to execute arbitrary code or manipulate system behavior through crafted input data.
The recommended mitigations for CVE-2018-20230 include immediate upgrading to PSPP version 1.2.1 or later, which contains the necessary patches to address the buffer overflow issue. Organizations should also implement input validation measures such as file format checking and size limitations for SAV files before processing them with PSPP. Network segmentation and access controls should be enforced to limit exposure to potentially malicious files, and regular security assessments should be conducted to identify similar vulnerabilities in other statistical analysis tools. System monitoring should be enhanced to detect unusual application behavior or crash patterns that may indicate exploitation attempts. Additionally, organizations should consider implementing sandboxing or containerization for statistical analysis applications to limit the potential impact of successful exploitation attempts. The vulnerability underscores the importance of proper input validation and memory safety practices in statistical software development, aligning with industry standards for secure coding practices and emphasizing the need for thorough security testing in data processing applications.