CVSS: 7.8EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29520 – Heap buffer overflow in `Conv3DBackprop*`
https://notcve.org/view.php?id=CVE-2021-29520
TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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/8f37b52e1320d8d72a9529b2468277791a261197 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wcv5-qrj6-9pfm • CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow') CWE-787: Out-of-bounds Write •
CVSS: 5.5EPSS: 0%CPEs: 2EXPL: 1CVE-2021-29521 – Segfault in SparseCountSparseOutput
https://notcve.org/view.php?id=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. • https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm • CWE-131: Incorrect Calculation of Buffer Size •
CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29522 – Division by 0 in `Conv3DBackprop*`
https://notcve.org/view.php?id=CVE-2021-29522
TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. • https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c968-pq7h-7fxv • CWE-369: Divide By Zero •
CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29523 – CHECK-fail in AddManySparseToTensorsMap
https://notcve.org/view.php?id=CVE-2021-29523
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.AddManySparseToTensorsMap`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/kernels/sparse_tensors_map_ops.cc#L257) takes the values specified in `sparse_shape` as dimensions for the output shape. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. • https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2cpx-427x-q2c6 • CWE-190: Integer Overflow or Wraparound •
CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29524 – Division by 0 in `Conv2DBackpropFilter`
https://notcve.org/view.php?id=CVE-2021-29524
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller. 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/fca9874a9b42a2134f907d2fb46ab774a831404a https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4pj-74mg-8868 • CWE-369: Divide By Zero •