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 •
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 •