CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29516 – Null pointer dereference via invalid Ragged Tensors
https://notcve.org/view.php?id=CVE-2021-29516
TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.RaggedTensorToVariant` with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of `RaggedTensorToVariant` operations(https://github.com/tensorflow/tensorflow/blob/904b3926ed1c6c70380d5313d282d248a776baa1/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L39-L40) does not validate that the ragged tensor argument is non-empty. Since `batched_ragged` contains no elements, `batched_ragged.splits` is a null vector, thus `batched_ragged.splits(0)` will result in dereferencing `nullptr`. The fix will be included in TensorFlow 2.5.0. • https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-84mw-34w6-2q43 • CWE-476: NULL Pointer Dereference •
CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29517 – Division by zero in `Conv3D`
https://notcve.org/view.php?id=CVE-2021-29517
TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. • https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv • CWE-369: Divide By Zero •
CVSS: 7.8EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29518 – Session operations in eager mode lead to null pointer dereferences
https://notcve.org/view.php?id=CVE-2021-29518
TensorFlow is an end-to-end open source platform for machine learning. In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference. The implementation(https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid. Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior. • https://github.com/tensorflow/tensorflow/commit/ff70c47a396ef1e3cb73c90513da4f5cb71bebba https://github.com/tensorflow/tensorflow/security/advisories/GHSA-62gx-355r-9fhg • CWE-476: NULL Pointer Dereference •
CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29519 – CHECK-fail in SparseCross due to type confusion
https://notcve.org/view.php?id=CVE-2021-29519
TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The fix will be included in TensorFlow 2.5.0. • https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772j-h9xw-ffp5 • CWE-843: Access of Resource Using Incompatible Type ('Type Confusion') •
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 •