Kushalkhemka/embedding-cve-nvd-dataset huggingface dataset:
cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads. 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. Additional Analyst Notes: - CVE Program Container: Refere
.1.4, as these are also affected and still in supported range. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j47f-4232-hvv8 - https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a
CVE ID: CVE-2021-29533 State: PUBLISHED Published: 2021-05-14T19:12:02 Last Updated: 2024-08-03T22:11:05.771Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-754 Description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to vali
sorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. 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
orFlow 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69 - https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87
CVE ID: CVE-2021-29534 State: PUBLISHED Published: 2021-05-14T19:11:57 Last Updated: 2024-08-03T22:11:05.772Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-754 Description: 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.SparseConcat`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in `shapes[0]` as dimensions for the output shape. The `TensorShape` constructor(
_op.cc#L76) takes the values specified in `shapes[0]` 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
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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6j9c-grc6-5m6g - https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c
CVE ID: CVE-2021-29535 State: PUBLISHED Published: 2021-05-14T19:11:52 Last Updated: 2024-08-03T22:11:05.562Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-131 Description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the
ore/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m3f9-w3p3-p669 - https://github.com/tensorflow/tensorflow/commit/e
ub.com/tensorflow/tensorflow/security/advisories/GHSA-m3f9-w3p3-p669 - https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87
CVE ID: CVE-2021-29536 State: PUBLISHED Published: 2021-05-14T19:11:46 Last Updated: 2024-08-03T22:11:05.449Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-131 Description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to acce
ow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2gfx-95x2-5v3x - https://github.com/tensorflow/tensorflow/co
//github.com/tensorflow/tensorflow/security/advisories/GHSA-2gfx-95x2-5v3x - https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e
CVE ID: CVE-2021-29537 State: PUBLISHED Published: 2021-05-14T19:11:41 Last Updated: 2024-08-03T22:11:05.558Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-131 Description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars
ensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8c89-2vwr-chcq - https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694
CVE ID: CVE-2021-29538 State: PUBLISHED Published: 2021-05-14T19:11:36 Last Updated: 2024-08-03T22:11:05.489Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-369 Description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes
L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96 - https://github.com/tenso
s: - https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96 - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp
CVE ID: CVE-2021-29539 State: PUBLISHED Published: 2021-05-14T19:11:32 Last Updated: 2024-08-03T22:11:06.067Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-681 Description: TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.ImmutableConst`(https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing
ars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g4h2-gqm3-c9wq - https://github.com/tensorflow/tensorflow/commit/4f663d4b8f0bec1b48da6fa091a7d29609980fa4
CVE ID: CVE-2021-29540 State: PUBLISHED Published: 2021-05-14T19:11:26 Last Updated: 2024-08-03T22:11:05.650Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-120 Description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter
495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xgc3-m89p-vr3x - https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df
security/advisories/GHSA-xgc3-m89p-vr3x - https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96
CVE ID: CVE-2021-29541 State: PUBLISHED Published: 2021-05-14T19:11:21 Last Updated: 2024-08-03T22:11:05.695Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-476 Description: 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/tenso
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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com
fected and still in supported range. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xqfj-35wv-m3cr
CVE ID: CVE-2021-29542 State: PUBLISHED Published: 2021-05-14T19:11:17 Last Updated: 2024-08-03T22:11:05.402Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-131 Description: 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 tok
g_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. 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg - https://github.co
iner: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg - https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b
CVE ID: CVE-2021-29543 State: PUBLISHED Published: 2021-05-14T19:11:12 Last Updated: 2024-08-03T22:11:06.262Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-617 Description: 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 abor
er_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. 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fphq-gw9m-ghrv - https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2
CVE ID: CVE-2021-29544 State: PUBLISHED Published: 2021-05-14T19:11:06 Last Updated: 2024-10-31T20:41:21.258Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-754 Description: 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.QuantizeAndDequantizeV4Grad`. This is because the implementation does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`. However, the `vec<T>` method, requires the rank to 1 and trigger
y are to `QuantizeAndDequantizePerChannelGradientImpl`. However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6g85-3hm8-83f9 - https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b - https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163 - https://git
b/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163 - https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306
CVE ID: CVE-2021-29545 State: PUBLISHED Published: 2021-05-14T19:11:00 Last Updated: 2024-08-03T22:11:05.602Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-131 Description: 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 th
e/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. 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hmg3-c7xj-6qwm - https://github.com/tensorflow/tensorflow/commit/1
ub.com/tensorflow/tensorflow/security/advisories/GHSA-hmg3-c7xj-6qwm - https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13
CVE ID: CVE-2021-29546 State: PUBLISHED Published: 2021-05-14T19:10:55 Last Updated: 2024-08-03T22:11:05.508Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-369 Description: 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
rflow/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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m34j-p8rj-wjxq - https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cb
CVE ID: CVE-2021-29547 State: PUBLISHED Published: 2021-05-14T19:10:50 Last Updated: 2024-08-03T22:11:05.426Z Severity: LOW (CVSS 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L) Vendors: tensorflow Products: tensorflow Problem Types: CWE-125 Description: 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
bbeb414dc55553d3ae7f/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. 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. Additional Analyst Notes: - CVE Program Container: References: - https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-
ommit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b - https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj