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| CVE-ID | ||
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CVE-2021-29583 |
• CVSS Severity Rating • Fix Information • Vulnerable Software Versions • SCAP Mappings • CPE Information
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| Description | ||
| 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. | ||
| References | ||
| Note: References are provided for the convenience of the reader to help distinguish between vulnerabilities. The list is not intended to be complete. | ||
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| Assigning CNA | ||
| GitHub (maintainer security advisories) | ||
| Date Record Created | ||
| 20210330 | Disclaimer: The record creation date may reflect when the CVE ID was allocated or reserved, and does not necessarily indicate when this vulnerability was discovered, shared with the affected vendor, publicly disclosed, or updated in CVE. | |
| Phase (Legacy) | ||
| Assigned (20210330) | ||
| Votes (Legacy) | ||
| Comments (Legacy) | ||
| Proposed (Legacy) | ||
| N/A | ||
| This is an record on the CVE List, which provides common identifiers for publicly known cybersecurity vulnerabilities. | ||
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