CVE-2020-35357 in Scientific Library
Summary
by MITRE • 08/22/2023
A buffer overflow can occur when calculating the quantile value using the Statistics Library of GSL (GNU Scientific Library), versions 2.5 and 2.6. Processing a maliciously crafted input data for gsl_stats_quantile_from_sorted_data of the library may lead to unexpected application termination or arbitrary code execution.
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Analysis
by VulDB Data Team • 12/07/2024
The vulnerability identified as CVE-2020-35357 represents a critical buffer overflow condition within the GNU Scientific Library's statistics component, specifically affecting versions 2.5 and 2.6. This flaw resides in the gsl_stats_quantile_from_sorted_data function which is designed to calculate quantile values from pre-sorted data arrays. The issue stems from inadequate input validation and bounds checking during the quantile calculation process, creating a scenario where maliciously crafted data can trigger memory corruption. The buffer overflow occurs when the library attempts to process input data that exceeds expected boundaries, leading to memory overwrite conditions that can compromise the integrity of the executing application.
The technical exploitation of this vulnerability follows a well-established pattern that aligns with CWE-121, which describes buffer overflow conditions where insufficient bounds checking allows attackers to write beyond allocated memory regions. When the gsl_stats_quantile_from_sorted_data function receives malformed input, it fails to properly validate array dimensions and data pointers before performing calculations. This allows an attacker to manipulate the memory layout of the application by providing carefully constructed data sequences that cause the library to write beyond intended buffer boundaries. The vulnerability manifests as a classic stack-based buffer overflow scenario where the function's internal calculations reference memory locations that are not properly validated against the actual data boundaries.
The operational impact of this vulnerability extends beyond simple application crashes to potentially enable arbitrary code execution within the context of the affected process. An attacker who successfully exploits this buffer overflow can gain control over the instruction pointer and redirect execution flow to malicious code injected into the application's memory space. This makes the vulnerability particularly dangerous in environments where the GNU Scientific Library is used for processing untrusted data inputs, such as web applications, data analysis platforms, or scientific computing frameworks that rely on statistical functions for data processing. The vulnerability affects any application that utilizes the affected GSL versions, creating a widespread risk across multiple software ecosystems that depend on this mathematical library for statistical computations.
Mitigation strategies for CVE-2020-35357 should prioritize immediate patching of affected systems to upgrade to GNU Scientific Library versions 2.7 or later, where the buffer overflow has been addressed through enhanced input validation and proper bounds checking mechanisms. Organizations should implement comprehensive input sanitization procedures for any data processed through the statistics library, particularly when handling external or untrusted inputs. Additionally, system administrators should consider implementing memory protection mechanisms such as stack canaries, address space layout randomization, and data execution prevention to reduce the exploitability of potential buffer overflow conditions. The vulnerability demonstrates the importance of robust input validation practices as outlined in the OWASP Top Ten security controls, particularly focusing on preventing injection flaws that can lead to memory corruption attacks. Security monitoring should include detection of unusual application termination patterns or memory access violations that may indicate exploitation attempts against this or similar buffer overflow vulnerabilities.