CVE-2021-37679 in TensorFlow
Summary
by MITRE • 08/13/2021
TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 08/17/2021
TensorFlow represents a widely adopted open source platform for machine learning that processes complex tensor operations including nested function mappings through tf.map_fn calls. The vulnerability described in CVE-2021-37679 specifically targets the interaction between nested tf.map_fn operations when processing RaggedTensor inputs without explicit function signatures. This flaw creates a critical memory exposure condition where the system assumes output tensors have fully specified dimensions and proceeds to populate output buffers with uninitialized heap contents. The vulnerability manifests when a Variant tensor undergoes conversion to a RaggedTensor, with the implementation failing to validate that all inner shapes match during the conversion process. This inadequate validation leads to improper dimension handling and subsequent memory corruption issues.
The technical implementation flaw exists within the tensorflow/core/kernels/ragged_tensor_from_variant_op.cc file at lines 177-190, where the conversion logic does not perform necessary shape consistency checks. When input tensors are processed through nested tf.map_fn calls with RaggedTensor inputs, the system incorrectly handles dimension mismatches, causing the output tensor t to contain heap data in its final row while the expected output z remains properly initialized. This discrepancy creates a memory information leak vulnerability that allows attackers to potentially extract sensitive data from adjacent memory locations. The vulnerability's classification aligns with CWE-125: Out-of-Bounds Read, as the system reads beyond the bounds of allocated memory regions. Additionally, the flaw demonstrates characteristics of CWE-787: Out-of-bounds Write, where improper buffer handling results in writing to memory locations beyond intended boundaries.
The operational impact of this vulnerability extends beyond simple information disclosure to potentially enable more sophisticated attacks. Memory leakage through heap contents can provide attackers with insights into memory layout and potentially sensitive information stored in adjacent memory regions. This vulnerability affects TensorFlow versions 2.3.4 through 2.6.0, with the specific implementation changes requiring updates to handle RaggedTensor conversion properly. The security implications become more severe when considering that TensorFlow is used extensively in machine learning environments where data privacy and system integrity are paramount. Attackers could potentially exploit this vulnerability in environments where TensorFlow processes sensitive data, leading to information leakage that could compromise the confidentiality of machine learning models and their underlying datasets.
The mitigation strategy involves applying the patch referenced in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12, which ensures proper validation of inner tensor shapes during Variant to RaggedTensor conversion. The fix will be included in TensorFlow 2.6.0 and backported to versions 2.5.1, 2.4.3, and 2.3.4, maintaining support for older but still maintained releases. Organizations should prioritize upgrading to patched versions and implementing proper input validation when processing nested tf.map_fn operations with RaggedTensor inputs. The vulnerability's remediation addresses the core conversion logic and ensures that dimension consistency checks are properly enforced, preventing the uninitialized memory access patterns that lead to heap data leakage. This fix aligns with ATT&CK technique T1059.001: Command and Scripting Interpreter - PowerShell, where improper memory handling could potentially enable lateral movement through information extraction, though the primary concern remains memory corruption and information disclosure rather than direct execution exploits.