CVE-2022-35979 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. If `QuantizedRelu` or `QuantizedRelu6` are given nonscalar inputs for `min_features` or `max_features`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
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Analysis
by VulDB Data Team • 10/19/2022
The vulnerability described in CVE-2022-35979 represents a critical denial of service flaw within the TensorFlow machine learning platform that specifically affects the QuantizedRelu and QuantizedRelu6 operations. This issue arises from improper input validation mechanisms within the quantization functions that process neural network layers. The flaw manifests when these operations receive non-scalar inputs for the min_features or max_features parameters, creating a condition where the software fails to properly handle the unexpected data structure. The segmentation fault that occurs under these circumstances represents a classic buffer overflow or memory access violation that terminates the application process. This vulnerability directly impacts the reliability and availability of machine learning applications that depend on TensorFlow's quantization capabilities, particularly in production environments where system stability is paramount.
The technical implementation of this vulnerability stems from a fundamental lack of input validation within the TensorFlow kernel operations. When QuantizedRelu and QuantizedRelu6 functions process inputs, they expect scalar values for their feature range parameters but fail to validate that these inputs maintain the expected scalar dimensionality. This oversight creates a path where malformed inputs can cause memory corruption during tensor operations, leading to immediate process termination. The flaw aligns with CWE-129, which addresses improper validation of array indices and other input validation issues that can result in memory corruption. The segmentation fault occurs because the software attempts to access memory locations that are either invalid or unauthorized, causing the operating system to terminate the process. This type of vulnerability is particularly dangerous in machine learning pipelines where TensorFlow processes large volumes of data through complex computational graphs, as a single malformed input can bring down entire inference systems.
The operational impact of this vulnerability extends beyond simple denial of service, as it can be exploited by malicious actors to disrupt machine learning services and inference pipelines. In production environments, this vulnerability can lead to service outages that affect critical applications such as autonomous vehicles, medical imaging systems, or financial fraud detection platforms that rely on TensorFlow for their computational workloads. The vulnerability is particularly concerning because it requires no special privileges to exploit and can be triggered through normal input processing. Attackers can craft malicious inputs that cause the software to crash, leading to availability issues that may persist until the system is manually restarted. The fix implemented by TensorFlow addresses the core validation issue by ensuring that the min_features and max_features parameters are properly validated before processing, preventing the segmentation fault from occurring. This aligns with ATT&CK technique T1499.004, which covers network denial of service attacks that can be achieved through application-level vulnerabilities.
The remediation strategy implemented by TensorFlow developers involves a targeted fix that validates input parameters before processing them within the quantization functions. The patch ensures that scalar inputs are properly validated and that non-scalar inputs are rejected with appropriate error handling rather than allowing the segmentation fault to occur. This approach follows secure coding practices that emphasize input validation and proper error handling to prevent memory corruption vulnerabilities. The fix has been integrated into TensorFlow 2.10.0 and backported to older supported versions to ensure comprehensive protection across the affected product line. Organizations using TensorFlow in production environments should prioritize updating to the patched versions to eliminate exposure to this vulnerability. The lack of known workarounds means that administrators must rely on applying the official patches rather than implementing temporary mitigations. This vulnerability underscores the importance of thorough input validation in machine learning frameworks, particularly in operations that handle large-scale data processing and complex mathematical computations.