CVE-2023-35952 in libigl
Summary
by MITRE • 05/28/2024
Multiple stack-based buffer overflow vulnerabilities exist in the readOFF.cpp functionality of libigl v2.4.0. A specially-crafted .off file can lead to a buffer overflow. An attacker can arbitrary code execution to trigger these vulnerabilities.This vulnerability exists within the code responsible for parsing comments within the geometric faces section within an OFF file.
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Analysis
by VulDB Data Team • 03/27/2025
The vulnerability identified as CVE-2023-35952 represents a critical stack-based buffer overflow within the libigl library version 2.4.0, specifically affecting the readOFF.cpp component responsible for parsing OFF (Object File Format) files. This flaw resides in the comment parsing functionality within the geometric faces section of OFF files, making it particularly dangerous as OFF files are commonly used in computational geometry and 3D mesh processing applications. The vulnerability stems from inadequate bounds checking during the parsing of comment strings, where the library fails to properly validate the length of comment data before copying it into fixed-size stack buffers. This design flaw allows attackers to craft malicious OFF files containing excessively long comment sequences that exceed the allocated buffer space, resulting in memory corruption and potential arbitrary code execution.
The technical implementation of this vulnerability aligns with CWE-121 Stack-based Buffer Overflow, which occurs when a program writes data to a buffer located on the stack and exceeds the buffer's capacity. The attack vector requires an attacker to prepare a specially crafted .off file that contains oversized comment fields within the geometric faces section, where the parser does not enforce proper length validation before copying data into stack-allocated memory regions. This vulnerability demonstrates a classic insufficient validation pattern that enables attackers to overwrite adjacent stack memory, potentially corrupting return addresses and control flow information. The flaw is particularly concerning because OFF files are frequently used in scientific computing, computer graphics applications, and 3D modeling software, making the attack surface quite broad.
The operational impact of CVE-2023-35952 extends beyond simple denial of service scenarios, as it enables full arbitrary code execution capabilities that can be leveraged by attackers to compromise systems running applications that utilize libigl for OFF file processing. Systems that process untrusted OFF files, such as 3D model viewers, CAD software, or computational geometry libraries, become vulnerable to remote code execution attacks. The vulnerability can be triggered through various attack scenarios including web-based file uploads, automated processing pipelines, or direct file manipulation, making it particularly dangerous in environments where users can submit or import OFF files from untrusted sources. This represents a significant risk for applications in sectors such as aerospace, automotive design, medical imaging, and entertainment where 3D mesh processing is prevalent.
Mitigation strategies for CVE-2023-35952 should prioritize immediate patching of affected libigl versions, with security updates addressing the buffer overflow through proper bounds checking and input validation mechanisms. Organizations should implement defensive programming practices including stack canaries, address space layout randomization, and input sanitization to reduce exploitability. The vulnerability highlights the importance of secure coding practices and proper memory management in scientific computing libraries, aligning with ATT&CK technique T1059.007 for command and script interpreter execution. Additionally, network segmentation, file validation, and least privilege access controls should be implemented to limit potential damage from successful exploitation. Regular security assessments and dependency updates are essential for maintaining system integrity, particularly for libraries handling geometric data formats that may be subject to similar parsing vulnerabilities.