CVE-2020-28629 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->sprev().
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Analysis
by VulDB Data Team • 04/21/2022
The CVE-2020-28629 vulnerability represents a critical security flaw within the Computational Geometry Algorithms Library (CGAL) version 5.1.1, specifically within its Nef polygon-parsing functionality. This vulnerability resides in the libcgal component of CGAL and affects the processing of malformed input files that are intended for polygon parsing operations. The flaw manifests through multiple code execution pathways, making it particularly dangerous for applications that process untrusted geometric data. The vulnerability is particularly concerning because it can be triggered by simply providing malicious input to the polygon parsing functionality, requiring no special privileges or complex attack vectors.
The technical implementation of this vulnerability involves a combination of out-of-bounds read conditions and type confusion issues that occur during the parsing of Nef polygons. Specifically, the vulnerability is located within the Nef_S2/SNC_io_parser.h file in the SNC_io_parser::read_sedge() function, where the code accesses the seh->sprev() member without proper bounds checking. This out-of-bounds read occurs when the parser encounters malformed input that does not conform to the expected data structure format. The type confusion aspect arises from the improper handling of object types during the parsing process, where memory structures are interpreted incorrectly, potentially leading to arbitrary code execution. The vulnerability is classified under CWE-125 for out-of-bounds read and CWE-467 for use of sizeof() on a pointer, with potential elements of CWE-787 for out-of-bounds write if exploitation leads to memory corruption.
The operational impact of this vulnerability extends beyond simple denial-of-service scenarios, as it can enable remote code execution when applications process untrusted polygon data. This vulnerability affects software systems that utilize CGAL for computational geometry operations, including CAD applications, geographic information systems, computer graphics software, and any system that parses geometric data from external sources. Attackers can exploit this vulnerability by crafting malicious polygon files that trigger the out-of-bounds read condition in the SNC_io_parser::read_sedge() function, potentially allowing them to execute arbitrary code with the privileges of the affected application. The exploitation chain typically involves constructing a malformed input file that causes the parser to access memory beyond allocated boundaries, leading to information disclosure, system crashes, or full code execution. This vulnerability aligns with ATT&CK technique T1059.007 for command and script interpreter and T1203 for Exploitation for Client Execution, as it enables attackers to execute malicious code through the parsing of geometric data.
Mitigation strategies for CVE-2020-28629 should prioritize immediate patching of affected CGAL versions, with particular attention to upgrading to versions that have addressed the Nef polygon parsing vulnerabilities. Organizations should implement input validation measures that sanitize all polygon data before processing, including implementing strict bounds checking and data format verification. Network segmentation and access controls can help limit the potential impact if exploitation occurs, while monitoring systems should be deployed to detect anomalous parsing behavior or unexpected memory access patterns. Additionally, developers should consider implementing defensive programming practices such as bounds checking, memory safety validations, and input sanitization routines when integrating CGAL functionality into applications. The vulnerability underscores the importance of secure coding practices in mathematical and geometric libraries, particularly when handling untrusted data inputs, and highlights the need for comprehensive testing of parsing functions against malformed inputs. Organizations should also consider implementing automated vulnerability scanning tools that can detect the presence of vulnerable CGAL versions in their software stacks and establish incident response procedures for handling potential exploitation attempts.