CVE-2021-29521
TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in tf.raw_ops.SparseCountSparseOutput results in a segmentation fault being thrown out from the standard library as std::vector invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a BatchedMap<T> (i.e., std::vector<absl::flat_hash_map<int64,T>>(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the shape tensor has more than one element, num_batches is the first value in shape. Ensuring that the dense_shape argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.
| Software | From | Fixed in |
|---|---|---|
| google / tensorflow | 2.4.0 | 2.4.2 |
| google / tensorflow | 2.3.0 | 2.3.3 |
tensorflow
|
2.3.0 | 2.3.3 |
|
tensorflow
|
2.4.0 | 2.4.2 |
|
tensorflow-cpu
|
2.3.0 | 2.3.3 |
|
tensorflow-cpu
|
2.4.0 | 2.4.2 |
|
tensorflow-gpu
|
2.3.0 | 2.3.3 |
|
tensorflow-gpu
|
2.4.0 | 2.4.2 |
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A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.
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