GHSA-rrfp-j2mp-hq9c

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GHSA-rrfp-j2mp-hq9c

Summary: Segfault in `tf.quantization.quantize_and_dequantize`

Details: ### Impact An attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`: ```python tf.quantization.quantize_and_dequantize( input=[2.5, 2.5], input_min=[0,0], input_max=[1,1], axis=10) ``` This results in accessing [a dimension outside the rank of the input tensor](https://github.com/tensorflow/tensorflow/blob/0225022b725993bfc19b87a02a2faaad9a53bc17/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74) in the C++ kernel implementation: ``` const int depth = (axis_ == -1) ? 1 : input.dim_size(axis_); ``` However, [`dim_size` only does a `DCHECK`](https://github.com/tensorflow/tensorflow/blob/0225022b725993bfc19b87a02a2faaad9a53bc17/tensorflow/core/framework/tensor_shape.cc#L292-L307) to validate the argument and then uses it to access the corresponding element of an array: ``` int64 TensorShapeBase<Shape>::dim_size(int d) const { DCHECK_GE(d, 0); DCHECK_LT(d, dims()); DoStuffWith(dims_[d]); } ``` Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. ### Patches We have patched the issue in eccb7ec454e6617738554a255d77f08e60ee0808 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported in #42105

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c, https://nvd.nist.gov/vuln/detail/CVE-2020-15265, https://github.com/tensorflow/tensorflow/issues/42105, https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-295.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-330.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-138.yaml, https://github.com/tensorflow/tensorflow