CVE-2022-23587 in Tensorflow
Summary
by MITRE • 02/05/2022
Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. 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 identified as CVE-2022-23587 resides within TensorFlow's Grappler component, which serves as the optimizer responsible for transforming and optimizing computational graphs before execution. This particular flaw manifests as an integer overflow occurring during cost estimation processes specifically for crop and resize operations within the machine learning framework. The Grappler component's cost estimation mechanism is designed to evaluate the computational resources required for various operations, enabling optimization decisions that impact performance and resource allocation. When processing crop and resize operations, the system calculates costs based on parameters that may be derived from user inputs, creating a potential attack surface where malicious actors can manipulate these values to trigger unintended behavior.
The technical nature of this vulnerability stems from improper handling of integer arithmetic within the cost estimation logic. Specifically, when user-controlled cropping parameters exceed certain thresholds, the integer overflow condition causes the system to produce incorrect cost calculations that can lead to undefined behavior. This type of vulnerability falls under CWE-190, which classifies integer overflow conditions that can result in unexpected program behavior, memory corruption, or exploitation opportunities. The integer overflow occurs during the mathematical computations used to estimate computational costs for these specific operations, where the overflow can cause negative values or wraparound behavior that corrupts the optimization process.
The operational impact of this vulnerability extends beyond simple performance degradation, as it creates potential security risks for systems utilizing TensorFlow's Grappler optimization. Attackers who can control the cropping parameters passed to the framework can potentially trigger the integer overflow condition, leading to unpredictable behavior that may manifest as crashes, incorrect computations, or even potential code execution vulnerabilities. This is particularly concerning in environments where TensorFlow processes untrusted input data, such as web applications or data processing pipelines that accept user uploads. The vulnerability affects multiple versions of TensorFlow including 2.5.3, 2.6.3, 2.7.1, and requires immediate attention as these versions are still within their supported lifecycle. The fix implementation addresses the integer overflow by introducing proper bounds checking and overflow detection mechanisms within the cost estimation calculations.
The mitigation strategy for this vulnerability involves upgrading to TensorFlow version 2.8.0 or applying the cherry-picked fixes to the affected supported versions. This approach aligns with standard security practices for maintaining software integrity and protecting against known vulnerabilities. Organizations utilizing TensorFlow in production environments should prioritize patching these affected versions to prevent exploitation. The vulnerability demonstrates the importance of proper integer handling in optimization components, as these systems are often critical to the overall performance and correctness of machine learning workflows. From an ATT&CK perspective, this vulnerability could be categorized under technique T1203, which involves the exploitation of software vulnerabilities to gain unauthorized access or cause system instability. The remediation process requires careful testing to ensure that the patched versions maintain compatibility with existing machine learning workflows while eliminating the integer overflow conditions that could be exploited by malicious actors.