{"vulnerability": "CVE-2021-3764", "sightings": [{"uuid": "5065e8ef-86a9-49ad-9b02-54e2d710bf6f", "vulnerability_lookup_origin": "1a89b78e-f703-45f3-bb86-59eb712668bd", "author": "2a075640-a300-48a4-bb44-bc6130783b9b", "vulnerability": "CVE-2021-37644", "type": "published-proof-of-concept", "source": "https://t.me/cibsecurity/27281", "content": "\u203c CVE-2021-37644 \u203c\n\nTensorFlow is an end-to-end open source platform for machine learning. In affected versions providing a negative element to `num_elements` list argument of `tf.raw_ops.TensorListReserve` causes the runtime to abort the process due to reallocating a `std::vector` to have a negative number of elements. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls `std::vector.resize()` with the new size controlled by input given by the user, without checking that this input is valid. We have patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.\n\n\ud83d\udcd6 Read\n\nvia \"National Vulnerability Database\".", "creation_timestamp": "2021-08-13T00:40:24.000000Z"}, {"uuid": "d006e83d-44ed-4cdc-9983-5d461560ee66", "vulnerability_lookup_origin": "1a89b78e-f703-45f3-bb86-59eb712668bd", "author": "2a075640-a300-48a4-bb44-bc6130783b9b", "vulnerability": "CVE-2021-37646", "type": "published-proof-of-concept", "source": "https://t.me/cibsecurity/27280", "content": "\u203c CVE-2021-37646 \u203c\n\nTensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.StringNGrams` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve` which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.\n\n\ud83d\udcd6 Read\n\nvia \"National Vulnerability Database\".", "creation_timestamp": "2021-08-13T00:40:23.000000Z"}, {"uuid": "dea311a4-6221-4382-a725-e910d900392d", "vulnerability_lookup_origin": "1a89b78e-f703-45f3-bb86-59eb712668bd", "author": "2a075640-a300-48a4-bb44-bc6130783b9b", "vulnerability": "CVE-2021-37641", "type": "published-proof-of-concept", "source": "https://t.me/cibsecurity/27276", "content": "\u203c CVE-2021-37641 \u203c\n\nTensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.\n\n\ud83d\udcd6 Read\n\nvia \"National Vulnerability Database\".", "creation_timestamp": "2021-08-13T00:40:18.000000Z"}, {"uuid": "a76fd7d5-ba3d-4732-a6c4-3c376b1ed154", "vulnerability_lookup_origin": "1a89b78e-f703-45f3-bb86-59eb712668bd", "author": "2a075640-a300-48a4-bb44-bc6130783b9b", "vulnerability": "CVE-2021-37643", "type": "published-proof-of-concept", "source": "https://t.me/cibsecurity/27266", "content": "\u203c CVE-2021-37643 \u203c\n\nTensorFlow is an end-to-end open source platform for machine learning. If a user does not provide a valid padding value to `tf.raw_ops.MatrixDiagPartOp`, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. We have patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.\n\n\ud83d\udcd6 Read\n\nvia \"National Vulnerability Database\".", "creation_timestamp": "2021-08-12T22:40:13.000000Z"}, {"uuid": "631d822d-b4d4-4fa2-b6d4-36444025d582", "vulnerability_lookup_origin": "1a89b78e-f703-45f3-bb86-59eb712668bd", "author": "2a075640-a300-48a4-bb44-bc6130783b9b", "vulnerability": "CVE-2021-37640", "type": "published-proof-of-concept", "source": "https://t.me/cibsecurity/27252", "content": "\u203c CVE-2021-37640 \u203c\n\nTensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version.\n\n\ud83d\udcd6 Read\n\nvia \"National Vulnerability Database\".", "creation_timestamp": "2021-08-12T22:39:57.000000Z"}]}