CVE-2020-35631 in CGAL
Summary
by MITRE • 04/18/2022
Multiple code execution vulnerabilities exists in the Nef polygon-parsing functionality of CGAL libcgal CGAL-5.1.1. 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 any of these vulnerabilities. An oob read vulnerability exists in Nef_S2/SNC_io_parser.h SNC_io_parser::read_sface() SD.link_as_face_cycle().
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Analysis
by VulDB Data Team • 04/21/2022
The CVE-2020-35631 vulnerability represents a critical security flaw within the Computational Geometry Algorithms Library (CGAL) version 5.1.1, specifically affecting the Nef polygon-parsing functionality. This vulnerability exists within the libcgal component of CGAL, which is widely used for computational geometry operations in various software applications. The flaw manifests through multiple code execution pathways that can be exploited by malicious actors to compromise systems running affected software. The vulnerability specifically targets the Nef_S2/SNC_io_parser.h file where the SNC_io_parser::read_sface() function processes polygon data structures, creating a dangerous condition in the SD.link_as_face_cycle() method that can be manipulated by attackers.
The technical nature of this vulnerability involves an out-of-bounds read condition combined with type confusion that can lead to arbitrary code execution. When the parser encounters malformed input files, the SD.link_as_face_cycle() method fails to properly validate the data structure boundaries, allowing attackers to craft specially designed polygon files that trigger memory access violations. This type confusion occurs because the parser does not adequately distinguish between different data types during the parsing process, enabling attackers to manipulate memory layout and potentially execute malicious code. The vulnerability is particularly concerning because it operates at the parsing layer where legitimate input processing can be easily subverted through crafted malicious files, making it difficult to detect and prevent through standard security measures.
The operational impact of this vulnerability extends across numerous applications that rely on CGAL for geometric computations, including computer graphics software, CAD systems, geographic information systems, and scientific computing applications. Attackers can exploit this vulnerability by providing malicious input files that trigger the out-of-bounds read condition, potentially leading to complete system compromise. The vulnerability's exploitation requires minimal user interaction as it can be triggered through automated file processing, making it particularly dangerous in environments where files are automatically parsed or processed. This vulnerability aligns with CWE-125 Out-of-bounds Read and CWE-476 Null Pointer Dereference categories, representing a serious threat to software integrity and system security. The ATT&CK framework categorizes this as a code injection technique where adversaries manipulate input parsing to achieve arbitrary code execution.
Mitigation strategies for CVE-2020-35631 require immediate patching of affected CGAL installations to version 5.1.2 or later where the vulnerability has been addressed. Organizations should implement strict input validation and sanitization measures for any polygon data processing workflows, particularly those involving external or untrusted inputs. Network segmentation and access controls should be enforced to limit potential attack vectors, while monitoring systems should be configured to detect unusual file processing patterns that might indicate exploitation attempts. Security teams should also consider implementing application whitelisting policies to restrict execution of vulnerable software components and establish robust incident response procedures to handle potential exploitation attempts. Regular security audits and penetration testing should be conducted to identify and remediate similar vulnerabilities in other geometric processing libraries and components within the software supply chain.