CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29567 – Lack of validation in `SparseDenseCwiseMul`
https://notcve.org/view.php?id=CVE-2021-29567
TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data. Since the implementation(https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no constraints between dimensions(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. 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. • https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp3c-xw9g-gpcg • CWE-617: Reachable Assertion •
CVSS: 7.8EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29568 – Reference binding to null in `ParameterizedTruncatedNormal`
https://notcve.org/view.php?id=CVE-2021-29568
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. The fix will be included in TensorFlow 2.5.0. • https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4p4p-www8-8fv9 • CWE-476: NULL Pointer Dereference CWE-824: Access of Uninitialized Pointer •
CVSS: 7.1EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29569 – Heap out of bounds read in `RequantizationRange`
https://notcve.org/view.php?id=CVE-2021-29569
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. • https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm • CWE-125: Out-of-bounds Read •
CVSS: 7.1EPSS: 0%CPEs: 4EXPL: 0CVE-2021-29570 – Heap out of bounds read in `MaxPoolGradWithArgmax`
https://notcve.org/view.php?id=CVE-2021-29570
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. 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. • https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-545v-42p7-98fq • CWE-125: Out-of-bounds Read •
CVSS: 7.8EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29571 – Memory corruption in `DrawBoundingBoxesV2`
https://notcve.org/view.php?id=CVE-2021-29571
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. • https://github.com/tensorflow/tensorflow/commit/79865b542f9ffdc9caeb255631f7c56f1d4b6517 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-whr9-vfh2-7hm6 • CWE-787: Out-of-bounds Write •