CVE-2017-10791 in PSPP
Summary
by MITRE
There is an Integer overflow in the hash_int function of the libpspp library in GNU PSPP 0.10.5-pre2. For example, a crash was observed within the library code when attempting to convert invalid SPSS data into CSV format. A crafted input will lead to a remote denial of service attack.
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Analysis
by VulDB Data Team • 12/09/2022
The vulnerability identified as CVE-2017-10791 represents a critical integer overflow flaw within the libpspp library of GNU PSPP version 0.10.5-pre2. This library serves as a fundamental component for processing statistical data files, particularly those in SPSS format, making it a critical element in data analysis workflows across various organizations. The vulnerability manifests specifically within the hash_int function, which is responsible for generating hash values during data processing operations. When processing malformed SPSS data files, particularly those containing crafted inputs designed to exploit this weakness, the library fails catastrophically leading to application crashes and system instability. The integer overflow occurs during the internal computation of hash values, where the function fails to properly validate input parameters before performing arithmetic operations that exceed the maximum representable value for the integer data type.
The technical exploitation of this vulnerability follows a well-defined pattern that aligns with common software security principles and attack methodologies. The hash_int function processes input data to generate hash values for internal data structures, but when confronted with specially crafted integer values that cause overflow conditions, the computation produces unexpected results that corrupt memory structures. This type of vulnerability falls under the CWE-190 category of Integer Overflow or Wraparound, which is classified as a serious weakness in software development practices. The flaw demonstrates poor input validation and inadequate boundary checking mechanisms that are fundamental requirements for secure coding practices. Attackers can leverage this vulnerability by creating malicious SPSS data files containing oversized or malformed integer values that trigger the overflow condition during the CSV conversion process, which is the specific scenario where the crash was observed in testing environments.
The operational impact of CVE-2017-10791 extends beyond simple application instability to potentially enable broader security compromises within systems that rely on GNU PSPP for data processing. Remote denial of service attacks can be executed without requiring authentication or special privileges, making this vulnerability particularly dangerous in networked environments where data processing systems are accessible to untrusted users. When exploited, the vulnerability can cause complete application termination, forcing administrators to restart services and potentially disrupting critical data analysis workflows. Organizations using GNU PSPP for processing sensitive statistical data face significant risks including potential data loss, service interruptions, and increased operational overhead as system administrators must monitor and patch affected installations. The vulnerability's impact is amplified in environments where automated data processing pipelines exist, as a single malicious input could cause cascading failures throughout the entire data processing infrastructure.
Mitigation strategies for CVE-2017-10791 must address both immediate remediation and long-term security improvements in software development practices. The most effective immediate solution involves upgrading to a patched version of GNU PSPP where the integer overflow has been properly addressed through enhanced input validation and proper integer boundary checking. System administrators should implement strict input validation policies for all data processing systems, particularly those handling external or untrusted data sources. The vulnerability demonstrates the importance of following secure coding guidelines and implementing comprehensive testing procedures including fuzz testing to identify potential integer overflow conditions. Organizations should also consider implementing network segmentation and access controls to limit exposure of vulnerable systems to untrusted users, aligning with ATT&CK framework techniques that emphasize privilege escalation and denial of service attack vectors. Additionally, regular security assessments and vulnerability scanning should be implemented to identify similar weaknesses in other software components that may be susceptible to the same class of integer overflow vulnerabilities.