CVE-2022-23594 in Tensorflow
Summary
by MITRE • 02/05/2022
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
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Analysis
by VulDB Data Team • 02/07/2022
The vulnerability identified as CVE-2022-23594 affects TensorFlow's TFG dialect implementation within the MLIR framework, representing a critical security flaw that stems from insufficient input validation during the conversion process from GraphDef to MLIR-based intermediate representation. This issue specifically targets the SavedModel format which serves as TensorFlow's standard method for saving and restoring machine learning models, where the framework's internal assumptions about the structure and content of incoming graph definitions become invalid when manipulated by adversaries. The vulnerability manifests when an attacker modifies the SavedModel format on disk in ways that violate the expected GraphDef structure, leading to a cascade of potential security implications during the conversion phase.
The technical root cause of this vulnerability lies in the lack of proper validation mechanisms within TensorFlow's MLIR dialect conversion pipeline, where the system assumes certain structural properties of the GraphDef without adequate verification. This design flaw creates opportunities for attackers to craft malicious SavedModel files that contain malformed or unexpected data structures, causing the Python interpreter to encounter undefined behavior during processing. The vulnerability encompasses both crash conditions and more severe heap-based buffer overflows that can result in out-of-bounds read and write operations, potentially allowing for arbitrary code execution or system compromise. The discovery of this vulnerability through fuzzing techniques indicates that similar weaknesses may exist within the broader TensorFlow codebase, suggesting a systemic issue with input validation in the MLIR conversion process.
The operational impact of CVE-2022-23594 extends beyond simple denial of service scenarios, as the potential for heap-based buffer overflows creates significant security risks for systems that process untrusted TensorFlow model files. Attackers could exploit this vulnerability to crash Python processes running TensorFlow applications, potentially leading to service disruption in production environments where machine learning models are regularly loaded and processed. More critically, the heap OOB read/write capabilities could enable attackers to manipulate memory contents, potentially leading to privilege escalation or remote code execution depending on the execution context. This vulnerability affects TensorFlow users who rely on the TFG dialect for model conversion, particularly in scenarios where models are loaded from untrusted sources or when automated model processing pipelines are in place, making it a substantial concern for organizations deploying machine learning solutions in production environments.
Mitigation strategies for CVE-2022-23594 should prioritize immediate patching of affected TensorFlow versions, as the vulnerability exists in the core conversion logic of the MLIR framework. Organizations should implement strict input validation for all SavedModel files, particularly when processing models from external sources or untrusted environments, by verifying the integrity and structure of model files before attempting conversion to MLIR representations. Additional defensive measures include running TensorFlow applications in restricted execution environments with memory protection mechanisms, implementing sandboxing for model loading operations, and establishing automated scanning processes to detect potentially malicious model files. The vulnerability aligns with CWE-129 and CWE-787 categories related to improper input validation and out-of-bounds writes, and represents a significant concern under ATT&CK framework's T1190 and T1059 techniques for exploitation through application vulnerabilities and command execution. Organizations should also consider implementing network segmentation and access controls to limit exposure of systems that process machine learning models, while maintaining regular security assessments to identify similar vulnerabilities in other components of their machine learning pipelines.