CVE-2022-45907 in PyTorch
Summary
by MITRE • 11/26/2022
In PyTorch before trunk/89695, torch.jit.annotations.parse_type_line can cause arbitrary code execution because eval is used unsafely.
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Analysis
by VulDB Data Team • 03/09/2026
The vulnerability identified as CVE-2022-45907 represents a critical security flaw in the PyTorch machine learning framework that affects versions prior to trunk/89695. This issue resides within the torch.jit.annotations.parse_type_line function which is responsible for parsing type annotations in PyTorch's Just-In-Time compilation system. The vulnerability stems from the unsafe usage of Python's built-in eval function, which creates a dangerous attack surface where malicious input can trigger arbitrary code execution. The flaw specifically manifests when PyTorch processes type annotations that contain crafted payloads, allowing attackers to execute arbitrary commands on systems running vulnerable versions of the framework.
The technical implementation of this vulnerability leverages the inherent dangers of the eval function when processing untrusted input. When torch.jit.annotations.parse_type_line encounters type annotation strings, it passes these directly to eval without proper sanitization or validation, creating a path for code injection attacks. This pattern aligns with CWE-94, which describes the improper control of generation of code, specifically highlighting the risks associated with unsafe evaluation of dynamic code. The vulnerability operates at the intersection of code generation and type annotation processing within PyTorch's JIT compilation pipeline, where legitimate type parsing logic becomes a vector for malicious code execution.
From an operational impact perspective, this vulnerability poses significant risks to organizations utilizing PyTorch in production environments, particularly those that process user-provided model files or type annotations. Attackers could exploit this vulnerability by crafting malicious type annotations within PyTorch model files, leading to remote code execution on systems where these models are loaded and processed. The attack surface extends to any application that relies on PyTorch's JIT compilation features, including web applications, data science platforms, and machine learning pipelines that accept model uploads from untrusted sources. The vulnerability's severity is amplified by the fact that it can be triggered during normal model loading operations, making detection and prevention challenging.
The exploitation of CVE-2022-45907 follows patterns consistent with ATT&CK technique T1059.001, which involves executing malicious code through command and scripting interpreters. Attackers would need to craft type annotation payloads that, when processed by the vulnerable parse_type_line function, would execute malicious code with the privileges of the running PyTorch process. Mitigation strategies should focus on immediate version upgrades to PyTorch trunk/89695 or later releases where the unsafe eval usage has been eliminated. Organizations should also implement strict input validation for any type annotation data, avoid processing untrusted PyTorch models, and consider network segmentation to limit the potential impact of successful exploitation. Additionally, security monitoring should be enhanced to detect unusual patterns in model loading operations that might indicate exploitation attempts, particularly in environments where PyTorch is used for processing external model inputs.