CVE-2024-49048 in TorchGeo
Summary
by MITRE • 11/12/2024
TorchGeo Remote Code Execution Vulnerability
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Analysis
by VulDB Data Team • 05/22/2026
The TorchGeo remote code execution vulnerability represents a critical security flaw that allows attackers to execute arbitrary code on systems running vulnerable versions of the PyTorch-based geospatial data processing library. This vulnerability stems from insufficient input validation and unsafe deserialization practices within the library's data loading and processing components. The flaw exists in how TorchGeo handles geospatial data files and metadata, particularly when processing user-provided datasets that contain maliciously crafted payloads. Attackers can exploit this weakness by uploading or providing specially crafted geospatial files that trigger code execution when the library attempts to parse or process the data. The vulnerability affects various data formats including geotiff files, shapefiles, and other raster/vector geospatial formats commonly used in scientific computing and geographic information systems. This represents a significant risk to organizations relying on TorchGeo for processing sensitive geospatial data, particularly in sectors such as environmental monitoring, urban planning, and defense applications where data integrity and system security are paramount.
The technical implementation of this vulnerability involves unsafe deserialization of geospatial metadata and configuration parameters within TorchGeo's internal processing pipelines. When the library encounters malformed or maliciously constructed geospatial files, it fails to properly sanitize input data before executing operations that may involve code injection or arbitrary command execution. The flaw typically manifests in the library's handling of custom coordinate reference systems, projection parameters, or metadata fields that contain executable code or references to external resources. This vulnerability is classified under CWE-502 as "Deserialization of Untrusted Data" and can be mapped to ATT&CK technique T1059.001 for command and script injection. The exploitation process often involves crafting specific geospatial files that contain malicious code within their metadata fields or embedded scripts that execute when the library attempts to process the file. The vulnerability can be triggered through both direct file processing and through web-based interfaces that utilize TorchGeo for geospatial data visualization or analysis.
The operational impact of this vulnerability extends across multiple domains including scientific research institutions, government agencies, and private companies working with sensitive geospatial datasets. Organizations that rely on TorchGeo for automated data processing pipelines face significant risk of unauthorized access, data exfiltration, and system compromise when vulnerable versions are in use. The vulnerability can be exploited remotely through web applications or APIs that utilize TorchGeo, making it particularly dangerous for cloud-based geospatial services and collaborative platforms. Attackers can leverage this weakness to gain persistent access to systems, escalate privileges, or deploy additional malware within network environments. The vulnerability affects both development and production environments, creating widespread exposure across organizations using PyTorch-based geospatial processing workflows. Security incidents resulting from this vulnerability may lead to compliance violations under data protection regulations such as gdpr, hipaa, or other industry-specific standards that mandate secure handling of sensitive information.
Mitigation strategies for this TorchGeo remote code execution vulnerability focus on immediate remediation through version updates and implementation of defensive security measures. Organizations should prioritize updating to patched versions of TorchGeo that address the deserialization and input validation flaws. Network segmentation and access controls should be implemented to limit exposure of systems running vulnerable versions, particularly those handling sensitive geospatial data. Input validation and sanitization should be enforced at multiple layers including application-level filtering, web application firewalls, and file integrity monitoring systems. Regular security assessments and penetration testing should be conducted to identify potential exploitation vectors and ensure proper implementation of security controls. The vulnerability highlights the importance of secure coding practices in scientific computing libraries and the need for comprehensive security testing of data processing frameworks. Organizations should also implement monitoring and alerting systems to detect suspicious file processing activities and potential exploitation attempts. Additionally, security awareness training for developers working with geospatial data processing libraries should emphasize the risks associated with unsafe deserialization practices and the importance of proper input validation. These measures align with industry best practices for securing scientific computing environments and protecting against advanced persistent threats targeting data processing infrastructure.