CVE-2022-0845 in pytorch-lightning
Summary
by MITRE • 03/06/2022
Code Injection in GitHub repository pytorchlightning/pytorch-lightning prior to 1.6.0.
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Analysis
by VulDB Data Team • 10/09/2024
The vulnerability identified as CVE-2022-0845 represents a code injection flaw discovered in the pytorchlightning/pytorch-lightning repository affecting versions prior to 1.6.0. This issue arises from insufficient input validation and sanitization mechanisms within the codebase, particularly in how the system processes user-provided data during model training and configuration operations. The flaw exists in the parameter handling and configuration parsing components of the lightning framework, which are fundamental to the framework's operation and data processing workflows.
The technical implementation of this vulnerability stems from improper handling of serialized data structures and configuration parameters that are passed to the training pipeline. Attackers can exploit this weakness by crafting malicious input that gets interpreted as executable code rather than benign configuration data. The vulnerability manifests when user-supplied parameters are directly incorporated into the execution context without proper sanitization or validation, creating opportunities for arbitrary code execution within the context of the running application. This type of flaw falls under the CWE-94 category of "Improper Control of Generation of Code" and aligns with ATT&CK technique T1059.001 for executing malicious code through command-line interfaces.
The operational impact of CVE-2022-0845 extends significantly within machine learning environments where pytorch-lightning is deployed for model training and experimentation. An attacker who successfully exploits this vulnerability could gain unauthorized access to the training environment, potentially leading to data exfiltration, model poisoning, or disruption of training processes. The vulnerability is particularly concerning in collaborative environments where multiple users contribute configurations or models, as it could allow malicious actors to inject harmful code that executes during the training phase. Organizations using pytorch-lightning for production machine learning workloads face elevated risk of compromise, especially when dealing with untrusted input sources or when the framework operates with elevated privileges.
Mitigation strategies for CVE-2022-0845 primarily focus on upgrading to pytorch-lightning version 1.6.0 or later, which includes proper input validation and sanitization mechanisms. Security teams should implement comprehensive code review processes to identify similar patterns in custom extensions or forked repositories that may not have received the official patches. Additional protective measures include deploying strict input validation at multiple layers of the application architecture, implementing sandboxing techniques for configuration processing, and establishing robust monitoring for unusual execution patterns. Organizations should also consider implementing principle of least privilege access controls and regular security assessments of their machine learning pipelines to prevent exploitation of similar vulnerabilities. The fix addresses the root cause by introducing proper serialization handling and validation routines that prevent malicious code from being executed during configuration parsing operations, aligning with security best practices outlined in the OWASP Top Ten and NIST cybersecurity frameworks.