CVE-2022-41880 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. When the `BaseCandidateSamplerOp` function receives a value in `true_classes` larger than `range_max`, a heap oob read occurs. We have patched the issue in GitHub commit b389f5c944cadfdfe599b3f1e4026e036f30d2d4. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 12/20/2022
The vulnerability CVE-2022-41880 represents a critical heap out-of-bounds read condition within TensorFlow's machine learning platform that affects multiple versions including 2.8.4, 2.9.3, 2.10.1, and 2.11. This issue manifests in the BaseCandidateSamplerOp function where improper input validation leads to memory access violations when processing true_classes parameter values exceeding the defined range_max threshold. The flaw resides in TensorFlow's sampling operations that are fundamental to various machine learning workflows including recommendation systems, natural language processing, and neural network training processes. The vulnerability is classified under CWE-125 as an out-of-bounds read, which represents a common class of memory safety issues that can lead to unpredictable behavior and potential exploitation. This type of vulnerability falls within the ATT&CK technique T1587.001 for developing capabilities and T1059.001 for command and scripting interpreter, as it can potentially enable attackers to execute arbitrary code or cause denial of service through malformed inputs.
The technical exploitation of this vulnerability occurs when the BaseCandidateSamplerOp function processes a true_classes input value that surpasses the configured range_max parameter, leading to a heap memory access beyond allocated boundaries. This memory corruption can result in information disclosure, application crashes, or potentially remote code execution depending on the system configuration and memory layout. The heap overflow condition arises from insufficient bounds checking within the sampling algorithm, specifically during the processing of candidate selection operations that are prevalent in TensorFlow's neural network architectures. The fix implemented in commit b389f5c944cadfdfe599b3f1e4026e036f30d2d4 addresses the root cause by introducing proper validation of input parameters before memory access operations occur. This patch ensures that all true_classes values are validated against the range_max boundary, preventing the out-of-bounds memory access that could otherwise be exploited by malicious actors. The vulnerability affects TensorFlow's core functionality as sampling operations are integral to many machine learning algorithms and model training processes.
The operational impact of this vulnerability extends across various machine learning deployments where TensorFlow is used for production workloads including data science platforms, AI research environments, and enterprise applications. Organizations utilizing TensorFlow for recommendation engines, image recognition systems, or natural language processing tasks face potential service disruption or security risks when this vulnerability is exploited. The vulnerability affects both the current stable release and several previous versions that remain in support, making it particularly concerning for enterprises maintaining legacy systems. Attackers could potentially exploit this flaw to cause denial of service attacks against machine learning servers, leading to significant operational downtime and business disruption. The vulnerability also represents a potential information disclosure risk as heap memory contents could be accessed through the out-of-bounds read condition. Security teams must prioritize patching this vulnerability across all affected TensorFlow versions, particularly in environments where machine learning models are exposed to untrusted inputs or external data sources. The patch implementation requires careful consideration of backward compatibility and ensures that existing machine learning workflows continue to function properly while addressing the memory safety issue.
Mitigation strategies for CVE-2022-41880 include immediate deployment of the patched TensorFlow versions 2.11, 2.10.1, 2.9.3, and 2.8.4, with particular emphasis on environments processing untrusted data inputs. Organizations should implement input validation controls at the application level to prevent malformed true_classes values from reaching the vulnerable BaseCandidateSamplerOp function, though this represents a secondary defense measure. Network segmentation and access controls should be strengthened around machine learning infrastructure to limit potential exploitation paths. Regular security assessments should include verification that TensorFlow installations are running patched versions, particularly in environments with multiple TensorFlow deployments or custom configurations. The vulnerability highlights the importance of thorough input validation in machine learning frameworks, especially for operations involving sampling and candidate selection that are frequently used in production systems. Security monitoring should be enhanced to detect anomalous behavior patterns that might indicate exploitation attempts, including unexpected process crashes or memory access violations. System administrators should also consider implementing automated patch management processes to ensure timely deployment of security updates across all TensorFlow installations within their infrastructure.