CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29542 – Heap buffer overflow in `StringNGrams`
https://notcve.org/view.php?id=CVE-2021-29542
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements. If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. The fix will be included in TensorFlow 2.5.0. • https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg • CWE-131: Incorrect Calculation of Buffer Size CWE-787: Out-of-bounds Write •
CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29543 – CHECK-fail in `CTCGreedyDecoder`
https://notcve.org/view.php?id=CVE-2021-29543
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.CTCGreedyDecoder`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks. • https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fphq-gw9m-ghrv • CWE-617: Reachable Assertion •
CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29545 – Heap buffer overflow in `SparseTensorToCSRSparseMatrix`
https://notcve.org/view.php?id=CVE-2021-29545
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 converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. The fix will be included in TensorFlow 2.5.0. • https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hmg3-c7xj-6qwm • CWE-131: Incorrect Calculation of Buffer Size •
CVSS: 7.8EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29546 – Division by 0 in `QuantizedBiasAdd`
https://notcve.org/view.php?id=CVE-2021-29546
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel(https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero. 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/67784700869470d65d5f2ef20aeb5e97c31673cb https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m34j-p8rj-wjxq • CWE-369: Divide By Zero •
CVSS: 5.5EPSS: 0%CPEs: 4EXPL: 1CVE-2021-29547 – Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`
https://notcve.org/view.php?id=CVE-2021-29547
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. The fix will be included in TensorFlow 2.5.0. • https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj • CWE-125: Out-of-bounds Read •