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Total
9554 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2021-21202 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.6 HIGH |
| Use after free in extensions in Google Chrome prior to 90.0.4430.72 allowed an attacker who convinced a user to install a malicious extension to potentially perform a sandbox escape via a crafted Chrome Extension. | |||||
| CVE-2021-21197 | 2 Fedoraproject, Google | 2 Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Heap buffer overflow in TabStrip in Google Chrome prior to 89.0.4389.114 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. | |||||
| CVE-2021-21196 | 3 Fedoraproject, Google, Microsoft | 3 Fedora, Chrome, Windows | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Heap buffer overflow in TabStrip in Google Chrome on Windows prior to 89.0.4389.114 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. | |||||
| CVE-2021-21195 | 2 Fedoraproject, Google | 2 Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Use after free in V8 in Google Chrome prior to 89.0.4389.114 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. | |||||
| CVE-2021-21194 | 2 Fedoraproject, Google | 2 Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Use after free in screen sharing in Google Chrome prior to 89.0.4389.114 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. | |||||
| CVE-2021-21211 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 4.3 MEDIUM | 6.5 MEDIUM |
| Inappropriate implementation in Navigation in Google Chrome on iOS prior to 90.0.4430.72 allowed a remote attacker to leak cross-origin data via a crafted HTML page. | |||||
| CVE-2021-21212 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 4.3 MEDIUM | 6.5 MEDIUM |
| Incorrect security UI in Network Config UI in Google Chrome on ChromeOS prior to 90.0.4430.72 allowed a remote attacker to potentially compromise WiFi connection security via a malicious WAP. | |||||
| CVE-2021-21209 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 4.3 MEDIUM | 6.5 MEDIUM |
| Inappropriate implementation in storage in Google Chrome prior to 90.0.4430.72 allowed a remote attacker to leak cross-origin data via a crafted HTML page. | |||||
| CVE-2021-21213 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Use after free in WebMIDI in Google Chrome prior to 90.0.4430.72 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. | |||||
| CVE-2021-21214 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Use after free in Network API in Google Chrome prior to 90.0.4430.72 allowed a remote attacker to potentially exploit heap corruption via a crafted Chrome Extension. | |||||
| CVE-2021-21215 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 4.3 MEDIUM | 6.5 MEDIUM |
| Inappropriate implementation in Autofill in Google Chrome prior to 90.0.4430.72 allowed a remote attacker to spoof security UI via a crafted HTML page. | |||||
| CVE-2021-21225 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Out of bounds memory access in V8 in Google Chrome prior to 90.0.4430.85 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. | |||||
| CVE-2021-21223 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 9.6 CRITICAL |
| Integer overflow in Mojo in Google Chrome prior to 90.0.4430.85 allowed a remote attacker who had compromised the renderer process to potentially perform a sandbox escape via a crafted HTML page. | |||||
| CVE-2021-21224 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Type confusion in V8 in Google Chrome prior to 90.0.4430.85 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. | |||||
| CVE-2021-21221 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 4.3 MEDIUM | 6.5 MEDIUM |
| Insufficient validation of untrusted input in Mojo in Google Chrome prior to 90.0.4430.72 allowed a remote attacker who had compromised the renderer process to leak cross-origin data via a crafted HTML page. | |||||
| CVE-2021-21222 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 4.3 MEDIUM | 6.5 MEDIUM |
| Heap buffer overflow in V8 in Google Chrome prior to 90.0.4430.85 allowed a remote attacker who had compromised the renderer process to bypass site isolation via a crafted HTML page. | |||||
| CVE-2021-21218 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 4.3 MEDIUM | 5.5 MEDIUM |
| Uninitialized data in PDFium in Google Chrome prior to 90.0.4430.72 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted PDF file. | |||||
| CVE-2021-21216 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 4.3 MEDIUM | 6.5 MEDIUM |
| Inappropriate implementation in Autofill in Google Chrome prior to 90.0.4430.72 allowed a remote attacker to spoof security UI via a crafted HTML page. | |||||
| CVE-2021-21231 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Insufficient data validation in V8 in Google Chrome prior to 90.0.4430.93 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. | |||||
| CVE-2021-21228 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 4.3 MEDIUM | 4.3 MEDIUM |
| Insufficient policy enforcement in extensions in Google Chrome prior to 90.0.4430.93 allowed an attacker who convinced a user to install a malicious extension to bypass navigation restrictions via a crafted Chrome Extension. | |||||
| CVE-2021-21230 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Type confusion in V8 in Google Chrome prior to 90.0.4430.93 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. | |||||
| CVE-2021-21227 | 3 Debian, Fedoraproject, Google | 3 Debian Linux, Fedora, Chrome | 2021-06-01 | 6.8 MEDIUM | 8.8 HIGH |
| Insufficient data validation in V8 in Google Chrome prior to 90.0.4430.93 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. | |||||
| CVE-2021-21229 | 3 Debian, Fedoraproject, Google | 4 Debian Linux, Fedora, Android and 1 more | 2021-06-01 | 4.3 MEDIUM | 6.5 MEDIUM |
| Incorrect security UI in downloads in Google Chrome on Android prior to 90.0.4430.93 allowed a remote attacker to perform domain spoofing via a crafted HTML page. | |||||
| CVE-2021-29553 | 1 Google | 1 Tensorflow | 2021-05-21 | 3.6 LOW | 7.1 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can read data outside of bounds of heap allocated buffer in `tf.raw_ops.QuantizeAndDequantizeV3`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/11ff7f80667e6490d7b5174aa6bf5e01886e770f/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L237) does not validate the value of user supplied `axis` attribute before using it to index in the array backing the `input` argument. 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. | |||||
| CVE-2021-29519 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| 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. 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. | |||||
| CVE-2021-29521 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| 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. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3. | |||||
| CVE-2021-29522 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| 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. 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. | |||||
| CVE-2021-29523 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| 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. 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. | |||||
| CVE-2021-29565 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.SparseFillEmptyRows`. This is because of missing validation(https://github.com/tensorflow/tensorflow/blob/fdc82089d206e281c628a93771336bf87863d5e8/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L230-L231) that was covered under a `TODO`. If the `dense_shape` tensor is empty, then `dense_shape_t.vec<>()` would cause a null pointer dereference in the implementation of the op. 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. | |||||
| CVE-2021-29564 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.EditDistance`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/79865b542f9ffdc9caeb255631f7c56f1d4b6517/tensorflow/core/kernels/edit_distance_op.cc#L103-L159) has incomplete validation of the input parameters. 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. | |||||
| CVE-2021-29609 | 1 Google | 1 Tensorflow | 2021-05-20 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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. | |||||
| CVE-2021-29515 | 1 Google | 1 Tensorflow | 2021-05-20 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. 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. | |||||
| CVE-2021-29517 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| 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. 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. | |||||
| CVE-2021-29529 | 1 Google | 1 Tensorflow | 2021-05-20 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow. 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. | |||||
| CVE-2021-29524 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| 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. | |||||
| CVE-2021-29541 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a dereference of a null pointer in `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the `data_splits` argument. This would result in `ngrams_data`(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. 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. | |||||
| CVE-2021-29526 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that 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. | |||||
| CVE-2021-29528 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedMul`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55900e961ed4a23b438392024912154a2c2f5e85/tensorflow/core/kernels/quantized_mul_op.cc#L188-L198) does a division by a quantity that 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. | |||||
| CVE-2021-29516 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| 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. 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. | |||||
| CVE-2021-29554 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected. | |||||
| CVE-2021-29518 | 1 Google | 1 Tensorflow | 2021-05-20 | 4.6 MEDIUM | 7.8 HIGH |
| 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. 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. | |||||
| CVE-2021-29601 | 1 Google | 1 Tensorflow | 2021-05-20 | 3.6 LOW | 7.1 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of concatenation is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76). An attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of `int`. TFLite uses `int` to represent tensor dimensions, whereas TF uses `int64`. Hence, valid TF models can trigger an integer overflow when converted to TFLite format. 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. | |||||
| CVE-2021-29530 | 1 Google | 1 Tensorflow | 2021-05-20 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. 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. | |||||
| CVE-2021-29527 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedConv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that 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. | |||||
| CVE-2021-29531 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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. | |||||
| CVE-2021-29578 | 1 Google | 1 Tensorflow | 2021-05-20 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. 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. | |||||
| CVE-2021-29579 | 1 Google | 1 Tensorflow | 2021-05-20 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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. | |||||
| CVE-2021-29582 | 1 Google | 1 Tensorflow | 2021-05-20 | 3.6 LOW | 7.1 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131) accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same shape. 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. | |||||
| CVE-2021-29584 | 1 Google | 1 Tensorflow | 2021-05-20 | 2.1 LOW | 5.5 MEDIUM |
| 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 caused by an integer overflow in constructing a new tensor shape. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/0908c2f2397c099338b901b067f6495a5b96760b/tensorflow/core/kernels/sparse_split_op.cc#L66-L70) builds a dense shape without checking that the dimensions would not result in overflow. 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. 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. | |||||
| CVE-2021-29577 | 1 Google | 1 Tensorflow | 2021-05-20 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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. | |||||