CVE-2021-29583 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 05/19/2021
The vulnerability CVE-2021-29583 affects TensorFlow's implementation of the `tf.raw_ops.FusedBatchNorm` operation, which is a critical component in machine learning frameworks used for batch normalization. This specific implementation resides in the fused_batch_norm_op.cc file and represents a heap buffer overflow vulnerability that can lead to arbitrary code execution or system instability. The flaw occurs when processing tensors that have mismatched dimensions, specifically when the scale, offset, mean, and variance tensors do not align properly with the channel dimensions of the input tensor x. This type of vulnerability falls under CWE-125, heap-based buffer overflow, and can be categorized under the ATT&CK technique T1059.007 for command and scripting interpreter execution. The vulnerability impacts TensorFlow versions prior to 2.5.0, with affected releases including 2.4.2, 2.3.3, 2.2.3, and 2.1.4, all of which remain in supported ranges.
The technical flaw stems from inadequate input validation within the FusedBatchNorm implementation where the code fails to verify that the scale, offset, mean, and variance tensors contain exactly the same number of elements as the number of channels in the input tensor x. This validation gap allows attackers to craft malicious inputs where tensor dimensions do not match expected channel counts, leading to heap memory access violations. When tensors are empty, the implementation can also trigger undefined behavior through null pointer dereferences, which represents a separate but related vulnerability pattern. The buffer overflow occurs during memory indexing operations when the code attempts to access memory locations beyond the allocated buffer boundaries, potentially allowing attackers to read sensitive data or corrupt memory structures. This vulnerability directly impacts the integrity and availability of machine learning systems that rely on TensorFlow's batch normalization operations.
The operational impact of this vulnerability extends beyond simple memory corruption, as it can enable remote code execution when TensorFlow processes untrusted data through the vulnerable FusedBatchNorm operation. Attackers could exploit this weakness in machine learning pipelines, model serving environments, or training systems where TensorFlow processes data from external sources. The vulnerability affects the core functionality of batch normalization, which is widely used in deep learning models for stabilizing training and improving convergence rates. Systems utilizing TensorFlow for production machine learning workloads, particularly those involving web applications, cloud services, or automated model deployment, face significant risk exposure. The vulnerability can be exploited in scenarios where malicious inputs are processed through TensorFlow pipelines, potentially allowing attackers to gain unauthorized access to system resources or disrupt model training and inference operations. Organizations using TensorFlow in enterprise environments should prioritize patching affected versions to prevent potential exploitation.
Mitigation strategies for CVE-2021-29583 involve immediate deployment of patched TensorFlow versions, specifically 2.5.0 and the cherry-picked releases for older supported versions. System administrators should implement comprehensive monitoring for suspicious TensorFlow operations and consider restricting data inputs to validated dimensions. The fix addresses the core validation issue by ensuring proper dimension checking before buffer operations, preventing both heap overflow and null pointer dereference conditions. Organizations should also conduct thorough vulnerability assessments of their TensorFlow-based systems, particularly focusing on model serving endpoints and data processing pipelines. Additional defensive measures include implementing strict input validation at application boundaries, using containerization with restricted memory access, and monitoring for unusual memory access patterns. The vulnerability highlights the importance of proper input validation in machine learning frameworks and underscores the need for comprehensive security testing of mathematical and numerical operations in AI/ML platforms. This remediation aligns with industry best practices for secure software development and addresses the specific security requirements outlined in the Common Weakness Enumeration catalog.