CVE-2020-35634 in libcgal
Summary
by MITRE • 08/31/2021
A code execution vulnerability exists in the Nef polygon-parsing functionality of CGAL libcgal CGAL-5.1.1. An oob read vulnerability exists in Nef_S2/SNC_io_parser.h SNC_io_parser::read_sface() sfh->boundary_entry_objects Sloop_of. A specially crafted malformed file can lead to an out-of-bounds read and type confusion, which could lead to code execution. An attacker can provide malicious input to trigger this vulnerability.
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Analysis
by VulDB Data Team • 09/03/2021
The vulnerability CVE-2020-35634 represents a critical code execution flaw within the Computational Geometry Algorithms Library (CGAL) version 5.1.1, specifically affecting the Nef polygon-parsing functionality. This issue resides in the SNC_io_parser::read_sface() function within the Nef_S2/SNC_io_parser.h file, where an out-of-bounds read condition occurs during the parsing of polygonal data structures. The vulnerability manifests when processing malformed input files that contain specially crafted data structures, creating a dangerous scenario where memory access occurs beyond the allocated boundaries of the Sloop_of data structure. The root cause stems from inadequate input validation and boundary checking mechanisms within the polygon parsing routines that handle Nef polyhedral data structures, which are fundamental components used for representing and manipulating geometric objects in computational geometry applications.
The technical exploitation of this vulnerability leverages a combination of out-of-bounds read conditions and type confusion errors that occur during the parsing process. When the SNC_io_parser attempts to read from the boundary_entry_objects structure within the Sloop_of data structure, it accesses memory locations that are not properly validated or constrained, leading to unpredictable memory behavior. This type confusion vulnerability arises from the parser's inability to properly distinguish between different data types during the parsing sequence, allowing an attacker to manipulate the memory layout in ways that can be exploited to achieve arbitrary code execution. The vulnerability is particularly concerning because it operates at the parsing layer of the library, meaning that any application or system that relies on CGAL for processing polygonal data, including CAD software, geographic information systems, and computational geometry applications, could be compromised through malicious input files. This issue directly maps to CWE-125, which describes out-of-bounds read vulnerabilities, and CWE-787, which covers out-of-bounds write conditions that can lead to type confusion.
The operational impact of this vulnerability extends across numerous industries and applications that depend on CGAL for geometric computations and polygonal data processing. Systems utilizing this library for CAD/CAM operations, geographic information systems, computer graphics rendering, and computational geometry research become potential attack vectors for adversaries seeking to compromise these environments. The vulnerability's exploitation potential is significant because it can be triggered through simple file manipulation, requiring no complex attack vectors or privileged access. An attacker could craft malicious polygonal files that, when processed by an application using the vulnerable CGAL library, would cause the application to execute arbitrary code on the target system. This makes the vulnerability particularly dangerous in environments where users might process untrusted polygonal data, such as web applications that accept user-uploaded CAD files or automated systems that process geometric data from external sources. The attack surface is broad and includes any software that integrates CGAL's Nef polygon functionality, potentially affecting thousands of applications across multiple platforms and operating systems.
Mitigation strategies for CVE-2020-35634 should focus on immediate patching of the CGAL library to version 5.1.2 or later, which contains the necessary fixes for the out-of-bounds read and type confusion issues. Organizations should implement input validation measures that sanitize all polygonal data before processing, particularly when dealing with untrusted sources, and employ defensive programming techniques such as bounds checking and memory access validation. The implementation of sandboxing mechanisms around CGAL library usage can provide additional protection layers, while network-based intrusion detection systems should be configured to monitor for suspicious file upload patterns that might indicate exploitation attempts. Security teams should also consider implementing runtime protections such as address space layout randomization and stack canaries to make exploitation more difficult. From an ATT&CK perspective, this vulnerability aligns with techniques involving execution through libraries and privilege escalation through code injection, making it a critical target for both defensive and offensive security operations. Regular security assessments of applications using CGAL should include vulnerability scanning and penetration testing focused on the polygon parsing components to ensure continued protection against similar issues.