CVE-2022-41887 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.
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Analysis
by VulDB Data Team • 12/20/2022
The vulnerability identified as CVE-2022-41887 resides within the TensorFlow machine learning framework, specifically within the tf.keras.losses.poisson implementation. This flaw manifests when processing loss calculations involving prediction and true value tensors that undergo multiplication operations through the BinaryOp functor::mul mechanism. The core issue stems from integer overflow conditions that occur during dimension validation, creating a critical runtime failure scenario. The vulnerability affects the broadcast assignment process where dimension mismatches cause TensorFlow to crash, representing a significant reliability concern for machine learning workloads that depend on poisson loss calculations.
The technical implementation of this vulnerability involves the interaction between tensor dimension handling and integer overflow conditions within TensorFlow's internal computation graph. When y_pred and y_true tensors are processed through the poisson loss function, the multiplication operation in BinaryOp creates intermediate tensor dimensions that may exceed the maximum value representable by int32 data types. This overflow condition triggers a size mismatch during the broadcast assignment phase, causing the framework to terminate unexpectedly. The flaw demonstrates a classic integer overflow vulnerability pattern that can be categorized under CWE-191, which specifically addresses integer underflow and overflow conditions. The issue occurs because TensorFlow's dimension validation logic does not adequately account for potential overflow scenarios during tensor operation processing, particularly when dealing with large tensor dimensions that result from multiplication operations.
The operational impact of CVE-2022-41887 extends beyond simple framework instability to encompass potential service disruption for machine learning applications relying on poisson loss functions. Attackers could exploit this vulnerability to cause denial of service conditions in production environments where TensorFlow serves as the core ML platform, particularly affecting applications in areas such as anomaly detection, count data modeling, and statistical analysis where poisson loss functions are commonly employed. The vulnerability affects multiple TensorFlow versions including 2.10.0, 2.9.2, and other supported releases, with the specific fix being implemented through GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. This represents a significant concern for organizations maintaining legacy TensorFlow deployments that may not immediately receive updates, as the vulnerability creates a window of exposure for service interruption.
Mitigation strategies for CVE-2022-41887 primarily focus on applying the patched TensorFlow versions that incorporate the fix for integer overflow conditions in tensor dimension handling. Organizations should prioritize upgrading to TensorFlow 2.11 where the fix is included, along with applying the cherry-picked patches to 2.10.1 and 2.9.3 releases that remain in supported maintenance windows. System administrators should implement monitoring solutions to detect potential overflow conditions in tensor operations and establish alerting mechanisms for unusual tensor dimension patterns that might precede the overflow scenario. The fix addresses the root cause by implementing proper dimension validation that prevents integer overflow conditions during multiplication operations in the BinaryOp functor, thereby eliminating the size mismatch that leads to crash conditions. Organizations should also consider implementing defensive programming practices in their ML applications, such as validating tensor dimensions before processing and implementing graceful degradation mechanisms when encountering unexpected tensor size conditions.
The vulnerability demonstrates characteristics consistent with ATT&CK technique T1499.004, which involves network disruption through resource exhaustion or system instability, though in this case the disruption manifests as application crash rather than network-based attack. The security implications extend to supply chain concerns where organizations using TensorFlow components may be affected by this instability, particularly in environments where TensorFlow serves as a foundational element for ML pipelines and automated decision-making systems. The patch implementation follows standard security practices by addressing the specific overflow condition in the tensor processing pipeline while maintaining backward compatibility with existing ML workloads that utilize poisson loss functions. This vulnerability serves as a reminder of the importance of robust integer overflow handling in mathematical computing frameworks, particularly those handling large-scale data processing operations where tensor dimension calculations can quickly exceed standard integer limits.