CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29552 – CHECK-failure in `UnsortedSegmentJoin`
https://notcve.org/view.php?id=CVE-2021-29552
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. The fix will be included in TensorFlow 2.5.0. • https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jhq9-wm9m-cf89 • CWE-617: Reachable Assertion •
CVSS: 7.1EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29553 – Heap OOB in `QuantizeAndDequantizeV3`
https://notcve.org/view.php?id=CVE-2021-29553
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. • https://github.com/tensorflow/tensorflow/commit/99085e8ff02c3763a0ec2263e44daec416f6a387 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h9px-9vqg-222h • CWE-125: Out-of-bounds Read •
CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29554 – Division by 0 in `DenseCountSparseOutput`
https://notcve.org/view.php?id=CVE-2021-29554
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. • https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5 • CWE-369: Divide By Zero •
CVSS: 7.8EPSS: 0%CPEs: 2EXPL: 1CVE-2021-29512 – Heap buffer overflow in `RaggedBinCount`
https://notcve.org/view.php?id=CVE-2021-29512
TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. • https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4278-2v5v-65r4 • CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow') CWE-787: Out-of-bounds Write •
CVSS: 5.3EPSS: 0%CPEs: 5EXPL: 1CVE-2020-26266 – Uninitialized memory access in Eigen types in TensorFlow
https://notcve.org/view.php?id=CVE-2020-26266
In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. En las versiones afectadas de TensorFlow, en determinados casos, un modelo guardado puede activar el uso de valores no inicializados durante la ejecución del código. Esto es debido a que los búferes de tensor se llenan con el valor predeterminado del tipo, pero se olvidan de inicializar por defecto los tipos de punto flotante cuantificados en Eigen. • https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2 • CWE-908: Use of Uninitialized Resource •