CVE-2022-23591 in Tensorflow
Summary
by MITRE • 02/05/2022
Tensorflow is an Open Source Machine Learning Framework. The `GraphDef` format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a `GraphDef` containing a fragment such as the following can be consumed when loading a `SavedModel`. This would result in a stack overflow during execution as resolving each `NodeDef` means resolving the function itself and its nodes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 02/07/2022
The vulnerability described in CVE-2022-23591 represents a critical stack overflow issue within the TensorFlow machine learning framework that stems from improper handling of GraphDef format validation during SavedModel loading operations. This flaw exists in the runtime execution environment where TensorFlow assumes that GraphDef structures will not contain self-recursive functions, a fundamental invariant that governs the integrity of computational graphs. The vulnerability manifests when a maliciously crafted GraphDef containing recursive function references is processed during the loading of a SavedModel, creating a condition where each NodeDef resolution triggers the evaluation of the same function recursively without proper termination conditions.
The technical implementation of this vulnerability exploits the absence of validation checks for recursive function references within TensorFlow's GraphDef parser, which operates under the assumption that all functions referenced within a computational graph are acyclic. When a GraphDef containing such recursive structures is loaded, the runtime enters an infinite recursion loop where each NodeDef resolution attempts to resolve its containing function, which in turn attempts to resolve the same NodeDef, creating an unbounded stack growth pattern. This behavior directly violates the CWE-674 principle of "Uncontrolled Recursion" and represents a classic stack overflow vulnerability that can be triggered through improper input validation during graph loading operations.
The operational impact of this vulnerability extends beyond simple denial of service scenarios, as it can potentially enable remote code execution or system compromise when exploited in environments where TensorFlow processes untrusted model data. Attackers could craft malicious SavedModel files containing recursive GraphDef structures that would cause the TensorFlow runtime to crash or behave unpredictably when attempting to execute the loaded model. This vulnerability affects multiple TensorFlow versions including 2.5.3, 2.6.3, 2.7.1, and requires immediate attention as these versions remain within supported release ranges. The security implications are particularly severe in production environments where TensorFlow serves as a core component of machine learning pipelines, as it could be exploited to disrupt services or potentially gain unauthorized access to systems processing sensitive data.
The mitigation strategy for CVE-2022-23591 involves implementing comprehensive GraphDef validation during the SavedModel loading process to detect and reject recursive function references before they can be processed by the runtime engine. The fix addresses this by introducing proper cycle detection mechanisms within the TensorFlow GraphDef parser that can identify self-referential structures and prevent their execution. Organizations should immediately upgrade to TensorFlow 2.8.0 or apply the cherry-picked fixes to their affected versions to ensure proper validation of GraphDef structures. Security practitioners should also implement additional monitoring and validation procedures for any TensorFlow model loading operations, particularly those involving external or untrusted inputs, aligning with ATT&CK technique T1059.001 for command and scripting interpreter execution and T1499.004 for network denial of service through resource exhaustion. The vulnerability underscores the importance of proper input validation and invariant checking in complex computational frameworks, particularly those handling graph-based representations where recursive structures can lead to catastrophic runtime failures.