CVE-2020-35633 in ProVide
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() store_sm_boundary_item() Edge_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 identified as CVE-2020-35633 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 vulnerability resides within the SNC_io_parser class implementation in the Nef_S2/SNC_io_parser.h file, where the read_sface() method processes geometric data structures. The flaw manifests as an out-of-bounds read condition that occurs during the parsing of polygonal data, particularly when handling the store_sm_boundary_item() function that processes Edge_of elements. The vulnerability stems from inadequate input validation and memory management within the geometric data parsing pipeline, creating a dangerous condition where malformed input can cause the parser to access memory locations beyond the intended boundaries.
The technical exploitation of this vulnerability involves a sophisticated attack vector that begins with crafting a specially designed malformed file containing malicious geometric data structures. When the vulnerable CGAL library processes this crafted input, the out-of-bounds read operation triggers a type confusion scenario that can potentially lead to arbitrary code execution. The root cause lies in the improper handling of memory references during the parsing of surface faces within the Nef geometry data structure, where the Edge_of element processing fails to validate array indices against the actual allocated memory boundaries. This type confusion vulnerability operates at the intersection of memory corruption and control flow manipulation, allowing an attacker to manipulate the program's execution path through carefully constructed input sequences that exploit the underlying memory layout.
From an operational perspective, this vulnerability poses significant risks to applications that rely on CGAL for computational geometry operations, particularly those handling untrusted geometric data from external sources. The impact extends beyond simple denial of service scenarios to potentially enable full system compromise, as the code execution capability allows attackers to run arbitrary commands with the privileges of the affected application. The vulnerability affects any software utilizing CGAL's Nef polygon functionality for processing geometric data, including CAD systems, geographic information systems, computer graphics applications, and scientific computing platforms that depend on robust geometric algorithms. The exploitability of this vulnerability is enhanced by the fact that it requires minimal user interaction beyond providing the malicious input file, making it particularly dangerous in automated attack scenarios.
Security mitigations for CVE-2020-35633 should prioritize immediate patching of affected CGAL versions to 5.1.2 or later, which contains the necessary memory boundary checks and input validation fixes. Organizations should implement comprehensive input sanitization measures for any geometric data processing pipelines, including strict validation of file formats and content before processing. Network segmentation and access controls should be enforced to limit exposure of systems handling geometric data, while monitoring solutions should be deployed to detect anomalous parsing behavior that might indicate exploitation attempts. The vulnerability aligns with CWE-125 Out-of-bounds Read and CWE-476 NULL Pointer Dereference categories, and its exploitation techniques correspond to ATT&CK tactics including TA0002 Execution and TA0004 Privilege Escalation. Regular security assessments of geometric processing libraries and maintaining up-to-date software versions form essential defensive measures against similar vulnerabilities in computational geometry frameworks.