CVSS: 9.3EPSS: 0%CPEs: 6EXPL: 0CVE-2021-37678 – Arbitrary code execution due to YAML deserialization
https://notcve.org/view.php?id=CVE-2021-37678
TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/python/keras/saving/model_config.py#L66-L104) uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. • https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6jx-9g48-2r5r • CWE-502: Deserialization of Untrusted Data •
CVSS: 5.5EPSS: 0%CPEs: 1EXPL: 0CVE-2021-37692 – Segfault on strings tensors with mistmatched dimensions in TensorFlow
https://notcve.org/view.php?id=CVE-2021-37692
TensorFlow is an end-to-end open source platform for machine learning. In affected versions under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, `C.TF_TString_Dealloc` is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions). • https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22 https://github.com/tensorflow/tensorflow/pull/50508 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cmgw-8vpc-rc59 • CWE-20: Improper Input Validation •
CVSS: 5.5EPSS: 0%CPEs: 6EXPL: 0CVE-2021-37669 – Crash in NMS ops caused by integer conversion to unsigned in TensorFlow
https://notcve.org/view.php?id=CVE-2021-37669
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`. However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. • https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c • CWE-681: Incorrect Conversion between Numeric Types •
CVSS: 5.5EPSS: 0%CPEs: 6EXPL: 0CVE-2021-37673 – `CHECK`-fail in `MapStage` in TensorFlow
https://notcve.org/view.php?id=CVE-2021-37673
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.MapStage`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the `key` input is a valid non-empty tensor. We have patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. The fix will be included in TensorFlow 2.6.0. • https://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d https://github.com/tensorflow/tensorflow/security/advisories/GHSA-278g-rq84-9hmg • CWE-20: Improper Input Validation •
CVSS: 7.8EPSS: 0%CPEs: 6EXPL: 0CVE-2021-37663 – Incomplete validation in `QuantizeV2` in TensorFlow
https://notcve.org/view.php?id=CVE-2021-37663
TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. • https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j • CWE-20: Improper Input Validation •