CVE-2022-29210 in TensorFlow
Summary
by MITRE • 05/21/2022
TensorFlow is an open source platform for machine learning. In version 2.8.0, the `TensorKey` hash function used total estimated `AllocatedBytes()`, which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. `int32_t`). It also tried to access individual tensor bytes through `tensor.data()` of size `AllocatedBytes()`. This led to ASAN failures because the `AllocatedBytes()` is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the `.data()` buffer. The discoverers could not use this byte vector anyway because types such as `tstring` include pointers, whereas they needed to hash the string values themselves. This issue is patched in Tensorflow versions 2.9.0 and 2.8.1.
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Analysis
by VulDB Data Team • 05/27/2022
The vulnerability described in CVE-2022-29210 affects TensorFlow version 2.8.0 and stems from a critical flaw in the `TensorKey` hash function implementation. This issue resides in the tensor management subsystem where the hash function incorrectly utilizes the `AllocatedBytes()` method to estimate memory allocation for individual tensors. The fundamental problem lies in the misunderstanding of what `AllocatedBytes()` represents, which is an estimated total memory footprint that includes not only the primary tensor data but also any referenced constructs such as strings or other pointer-based data structures. This estimation mechanism creates inherent instability in the hashing process, particularly when dealing with constant values like int32_t types where the estimation becomes highly unreliable and produces poor hash distribution.
The technical implementation flaw manifests when the system attempts to access individual tensor bytes through `tensor.data()` using the size parameter derived from `AllocatedBytes()`. This approach fails catastrophically because `AllocatedBytes()` does not correspond to contiguous bytes within the `.data()` buffer as assumed by the implementation. The memory layout of tensors with complex data types such as tstring includes pointer references that are not represented in the contiguous data buffer, creating a mismatch between expected and actual memory access patterns. This mismatch triggers AddressSanitizer (ASAN) failures during runtime, as the system attempts to read memory locations that may not be accessible or may contain invalid data. The vulnerability specifically affects the hash computation process where the developers needed to hash string values themselves but instead attempted to work with pointer-based data structures that cannot be directly converted to byte vectors for hashing purposes.
The operational impact of this vulnerability extends beyond simple memory access violations, as it represents a fundamental design flaw in TensorFlow's tensor key management system that could lead to unpredictable behavior in machine learning applications. The issue affects any application using TensorFlow 2.8.0 that relies on tensor key hashing for operations such as dictionary lookups, caching mechanisms, or graph optimization processes. The poor hash function quality creates potential for hash collisions and performance degradation, while the ASAN failures indicate underlying memory safety issues that could be exploited by malicious actors to cause application crashes or potentially more serious security implications. This vulnerability is particularly concerning in production environments where TensorFlow is used for critical machine learning workloads, as it could lead to system instability or data corruption during tensor processing operations.
The fix for this vulnerability was implemented in TensorFlow versions 2.9.0 and 2.8.1, where the developers corrected the hash function implementation to properly handle tensor data access patterns. The mitigation strategy involved removing the dependency on `AllocatedBytes()` for hash computation and implementing proper handling of different tensor types, particularly those containing pointer-based data structures. This solution aligns with security best practices for memory management and hash function design, ensuring that the hash computation operates on actual tensor data rather than estimated memory allocations. The fix addresses the core issue identified in CWE-129, which relates to improper validation of array indices, and follows ATT&CK technique T1059.001 for command and scripting interpreter, as it prevents the exploitation of memory access vulnerabilities that could lead to arbitrary code execution. Organizations using TensorFlow should immediately upgrade to patched versions to eliminate this security risk and ensure stable operation of their machine learning applications.