CVE-2020-28632 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_sedge() seh->incident_sface().
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Analysis
by VulDB Data Team • 04/21/2022
The CVE-2020-28632 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 resides in the libcgal component of the CGAL library, 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 through carefully crafted input files. The vulnerability is particularly concerning because it affects the core parsing mechanisms that handle polygonal data structures, making it a potential attack vector in applications that process geometric data from untrusted sources.
The technical implementation of this vulnerability involves a combination of out-of-bounds read conditions and type confusion errors within the Nef_S2/SNC_io_parser.h file, specifically within the SNC_io_parser::read_sedge() function. The vulnerability occurs when processing the seh->incident_sface() element, which represents a surface incident to an edge in the spherical arrangement data structure. This particular code path allows for memory access violations that can be leveraged to execute arbitrary code. The out-of-bounds read vulnerability stems from insufficient input validation and boundary checking when parsing malformed polygon data structures, while the type confusion aspect arises from improper handling of data type conversions during the parsing process. These issues are classified under CWE-125 for out-of-bounds read and CWE-704 for type confusion, both of which are well-documented in the Common Weakness Enumeration catalog.
The operational impact of this vulnerability extends across numerous applications that depend on CGAL for geometric computations, including computer graphics software, CAD systems, geographic information systems, and scientific computing applications. Attackers can exploit this vulnerability by crafting malicious polygon files that trigger the flawed parsing logic, potentially leading to complete system compromise. The vulnerability's exploitability is enhanced by the fact that it can be triggered through normal input processing flows without requiring special privileges or complex attack chains. According to ATT&CK framework, this vulnerability maps to T1059.007 for command and scripting interpreter and T1203 for Exploitation for Client Execution, as it enables arbitrary code execution on vulnerable systems. The attack surface is particularly broad given CGAL's widespread adoption in both commercial and open-source software ecosystems.
Mitigation strategies for CVE-2020-28632 primarily involve immediate patching of affected CGAL installations to version 5.1.2 or later, which contains the necessary fixes for the parsing vulnerabilities. Organizations should also implement input validation measures that sanitize all polygon data before processing, particularly when dealing with untrusted sources. Additional defensive measures include network segmentation to limit access to vulnerable applications, implementing runtime monitoring for suspicious memory access patterns, and conducting thorough security assessments of applications that utilize CGAL components. The vulnerability highlights the importance of proper boundary checking and type safety in geometric algorithms, as well as the need for comprehensive testing of parsing functions that handle complex data structures. Security teams should also consider implementing automated vulnerability scanning tools that can detect the presence of vulnerable CGAL versions in their software inventory, as the complexity of geometric data processing makes these vulnerabilities particularly challenging to identify through conventional testing approaches.