chore(deps): update dependency tensorflow to ~2.11.0 [security]

[renovate-bot]

ℹ️ Note

This PR body was truncated due to platform limits.

This PR contains the following updates:

Package	Change	Age	Confidence
tensorflow	~2.8 → ~2.11.0

---

Seg fault in ndarray_tensor_bridge due to zero and large inputs

CVE-2022-41884 / GHSA-jq6x-99hj-q636

More information

Details

Impact

If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. E.g. the following raises an error:

np.ones((0, 2**31, 2**31))

An example of a proof of concept:

import numpy as np
import tensorflow as tf

input_val = tf.constant([1])
shape_val = np.array([i for i in range(21)])

tf.broadcast_to(input=input_val,shape=shape_val)

The return value of PyArray_SimpleNewFromData, which returns null on such shapes, is not checked.

Patches

We have patched the issue in GitHub commit 2b56169c16e375c521a3bc8ea658811cc0793784.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Pattarakrit Rattanukul.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636
• https://nvd.nist.gov/vuln/detail/CVE-2022-41884
• https://github.com/tensorflow/tensorflow/commit/2b56169c16e375c521a3bc8ea658811cc0793784
• https://github.com/advisories/GHSA-jq6x-99hj-q636

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

Overflow in ImageProjectiveTransformV2

CVE-2022-41886 / GHSA-54pp-c6pp-7fpx

More information

Details

Impact

When tf.raw_ops.ImageProjectiveTransformV2 is given a large output shape, it overflows.

import tensorflow as tf

interpolation = "BILINEAR"
fill_mode = "REFLECT"
images = tf.constant(0.184634328, shape=[2,5,8,3], dtype=tf.float32)
transforms = tf.constant(0.378575385, shape=[2,8], dtype=tf.float32)
output_shape = tf.constant([1879048192,1879048192], shape=[2], dtype=tf.int32)
tf.raw_ops.ImageProjectiveTransformV2(images=images, transforms=transforms, output_shape=output_shape, interpolation=interpolation, fill_mode=fill_mode)

Patches

We have patched the issue in GitHub commit 8faa6ea692985dbe6ce10e1a3168e0bd60a723ba.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Neophytos Christou from the Secure Systems Lab (SSL) at Brown University.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-54pp-c6pp-7fpx
• https://nvd.nist.gov/vuln/detail/CVE-2022-41886
• https://github.com/tensorflow/tensorflow/commit/8faa6ea692985dbe6ce10e1a3168e0bd60a723ba
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/image_ops.cc
• https://github.com/advisories/GHSA-54pp-c6pp-7fpx

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

Overflow in tf.keras.losses.poisson

CVE-2022-41887 / GHSA-8fvv-46hw-vpg3

More information

Details

Impact

tf.keras.losses.poisson receives a y_pred and y_true that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment.

import numpy as np
import tensorflow as tf

true_value = tf.reshape(shape=[1, 2500000000], tensor = tf.zeros(dtype=tf.bool, shape=[50000, 50000]))
pred_value = np.array([[[-2]], [[8]]], dtype = np.float64)

tf.keras.losses.poisson(y_true=true_value,y_pred=pred_value)

Patches

We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Pattarakrit Rattankul.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3
• https://nvd.nist.gov/vuln/detail/CVE-2022-41887
• https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py
• https://github.com/advisories/GHSA-8fvv-46hw-vpg3

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

FPE in tf.image.generate_bounding_box_proposals

CVE-2022-41888 / GHSA-6x99-gv2v-q76v

More information

Details

Impact

When running on GPU, tf.image.generate_bounding_box_proposals receives a scores input that must be of rank 4 but is not checked.

import tensorflow as tf

a = tf.constant(value=[[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])
b = tf.constant(value=[1])

tf.image.generate_bounding_box_proposals(scores=a,bbox_deltas=a,image_info=a,anchors=a,pre_nms_topn=b)

Patches

We have patched the issue in GitHub commit cf35502463a88ca7185a99daa7031df60b3c1c98.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Pattarakrit Rattankul.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6x99-gv2v-q76v
• https://nvd.nist.gov/vuln/detail/CVE-2022-41888
• https://github.com/tensorflow/tensorflow/commit/cf35502463a88ca7185a99daa7031df60b3c1c98
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/generate_box_proposals_op.cu.cc
• https://github.com/advisories/GHSA-6x99-gv2v-q76v

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

Segfault via invalid attributes in pywrap_tfe_src.cc

CVE-2022-41889 / GHSA-xxcj-rhqg-m46g

More information

Details

Impact

If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a nullptr, which is not caught. An example can be seen in tf.compat.v1.extract_volume_patches by passing in quantized tensors as input ksizes.

import numpy as np
import tensorflow as tf

a_input = np.array([1, -1], dtype= np.int32)
a_ksizes =  a_strides = tf.constant(dtype=tf.dtypes.qint16, value=[[1, 4], [5, 2]])

tf.compat.v1.extract_volume_patches(input=a_input,ksizes=a_ksizes,strides=a_strides,padding='VALID')

Patches

We have patched the issue in GitHub commit e9e95553e5411834d215e6770c81a83a3d0866ce.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Pattarakrit Rattankul.

Severity

• CVSS Score: 5.5 / 10 (Medium)
• Vector String: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xxcj-rhqg-m46g
• https://nvd.nist.gov/vuln/detail/CVE-2022-41889
• https://github.com/tensorflow/tensorflow/commit/e9e95553e5411834d215e6770c81a83a3d0866ce
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/generate_box_proposals_op.cu.cc
• https://github.com/advisories/GHSA-xxcj-rhqg-m46g

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

CHECK fail in BCast overflow

CVE-2022-41890 / GHSA-h246-cgh4-7475

More information

Details

Impact

If BCast::ToShape is given input larger than an int32, it will crash, despite being supposed to handle up to an int64. An example can be seen in tf.experimental.numpy.outer by passing in large input to the input b.

import tensorflow as tf
value = tf.constant(shape=[2, 1024, 1024, 1024], value=False)
tf.experimental.numpy.outer(a=6,b=value)

Patches

We have patched the issue in GitHub commit 8310bf8dd188ff780e7fc53245058215a05bdbe5.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Pattarakrit Rattankul.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h246-cgh4-7475
• https://nvd.nist.gov/vuln/detail/CVE-2022-41890
• https://github.com/tensorflow/tensorflow/commit/8310bf8dd188ff780e7fc53245058215a05bdbe5
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/bcast.h
• https://github.com/advisories/GHSA-h246-cgh4-7475

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

Segfault in tf.raw_ops.TensorListConcat

CVE-2022-41891 / GHSA-66vq-54fq-6jvv

More information

Details

Impact

If tf.raw_ops.TensorListConcat is given element_shape=[], it results segmentation fault which can be used to trigger a denial of service attack.

import tensorflow as tf
tf.raw_ops.TensorListConcat(
    input_handle=tf.data.experimental.to_variant(tf.data.Dataset.from_tensor_slices([1, 2, 3])),
    element_dtype=tf.dtypes.float32,
    element_shape=[]
)

Patches

We have patched the issue in GitHub commit fc33f3dc4c14051a83eec6535b608abe1d355fde.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Tong Liu, ShanghaiTech University

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-66vq-54fq-6jvv
• https://nvd.nist.gov/vuln/detail/CVE-2022-41891
• https://github.com/tensorflow/tensorflow/commit/fc33f3dc4c14051a83eec6535b608abe1d355fde
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/list_kernels.h
• https://github.com/advisories/GHSA-66vq-54fq-6jvv

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

CHECK_EQ fail in tf.raw_ops.TensorListResize

CVE-2022-41893 / GHSA-67pf-62xr-q35m

More information

Details

Impact

If tf.raw_ops.TensorListResize is given a nonscalar value for input size, it results CHECK fail which can be used to trigger a denial of service attack.

import numpy as np
import tensorflow as tf

a = data_structures.tf_tensor_list_new(elements = tf.constant(value=[3, 4, 5]))
b = np.zeros([0, 2, 3, 3])

tf.raw_ops.TensorListResize(input_handle=a, size=b)

Patches

We have patched the issue in GitHub commit 888e34b49009a4e734c27ab0c43b0b5102682c56.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Pattarakrit Rattankul

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-67pf-62xr-q35m
• https://nvd.nist.gov/vuln/detail/CVE-2022-41893
• https://github.com/tensorflow/tensorflow/commit/888e34b49009a4e734c27ab0c43b0b5102682c56
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/list_kernels.cc
• https://github.com/advisories/GHSA-67pf-62xr-q35m

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

MirrorPadGrad heap out of bounds read

CVE-2022-41895 / GHSA-gq2j-cr96-gvqx

More information

Details

Impact

If MirrorPadGrad is given outsize input paddings, TensorFlow will give a heap OOB error.

import tensorflow as tf
tf.raw_ops.MirrorPadGrad(input=[1],
             paddings=[[0x77f00000,0xa000000]],
             mode = 'REFLECT')

Patches

We have patched the issue in GitHub commit 717ca98d8c3bba348ff62281fdf38dcb5ea1ec92.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Vul AI.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gq2j-cr96-gvqx
• https://nvd.nist.gov/vuln/detail/CVE-2022-41895
• https://github.com/tensorflow/tensorflow/commit/717ca98d8c3bba348ff62281fdf38dcb5ea1ec92
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/mirror_pad_op.cc
• https://github.com/advisories/GHSA-gq2j-cr96-gvqx

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

tf.raw_ops.Mfcc crashes

CVE-2022-41896 / GHSA-rmg2-f698-wq35

More information

Details

Impact

If ThreadUnsafeUnigramCandidateSampler is given input filterbank_channel_count greater than the allowed max size, TensorFlow will crash.

import tensorflow as tf
tf.raw_ops.Mfcc(
    spectrogram = [[[1.38, 6.32, 5.75, 9.51]]],
    sample_rate = 2,
    upper_frequency_limit = 5.0,
    lower_frequency_limit = 1.0,
    filterbank_channel_count = 2**31 - 1,
    dct_coefficient_count = 1
)

Patches

We have patched the issue in GitHub commit 39ec7eaf1428e90c37787e5b3fbd68ebd3c48860.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Vul AI.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rmg2-f698-wq35
• https://nvd.nist.gov/vuln/detail/CVE-2022-41896
• https://github.com/tensorflow/tensorflow/commit/39ec7eaf1428e90c37787e5b3fbd68ebd3c48860
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/mirror_pad_op.cc
• https://github.com/advisories/GHSA-rmg2-f698-wq35

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

FractionalMaxPoolGrad Heap out of bounds read

CVE-2022-41897 / GHSA-f2w8-jw48-fr7j

More information

Details

Impact

If FractionMaxPoolGrad is given outsize inputs row_pooling_sequence and col_pooling_sequence, TensorFlow will crash.

import tensorflow as tf
tf.raw_ops.FractionMaxPoolGrad(
	orig_input = [[[[1, 1, 1, 1, 1]]]],
    orig_output = [[[[1, 1, 1]]]],
    out_backprop = [[[[3], [3], [6]]]],
    row_pooling_sequence = [-0x4000000, 1, 1], 
    col_pooling_sequence = [-0x4000000, 1, 1], 
    overlapping = False
 )

Patches

We have patched the issue in GitHub commit d71090c3e5ca325bdf4b02eb236cfb3ee823e927.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Vul AI.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2w8-jw48-fr7j
• https://nvd.nist.gov/vuln/detail/CVE-2022-41897
• https://github.com/tensorflow/tensorflow/commit/d71090c3e5ca325bdf4b02eb236cfb3ee823e927
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/fractional_max_pool_op.cc
• https://github.com/advisories/GHSA-f2w8-jw48-fr7j

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

CHECK fail via inputs in SparseFillEmptyRowsGrad

CVE-2022-41898 / GHSA-hq7g-wwwp-q46h

More information

Details

Impact

If SparseFillEmptyRowsGrad is given empty inputs, TensorFlow will crash.

import tensorflow as tf
tf.raw_ops.SparseFillEmptyRowsGrad(
    reverse_index_map=[], grad_values=[], name=None
)

Patches

We have patched the issue in GitHub commit af4a6a3c8b95022c351edae94560acc61253a1b8.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Jiawei Liu, PhD student at University of Illinois, Urbana-Champaign.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hq7g-wwwp-q46h
• https://nvd.nist.gov/vuln/detail/CVE-2022-41898
• https://github.com/tensorflow/tensorflow/commit/af4a6a3c8b95022c351edae94560acc61253a1b8
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/sparse_fill_empty_rows_op_gpu.cu.cc
• https://github.com/advisories/GHSA-hq7g-wwwp-q46h

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

CHECK fail via inputs in SdcaOptimizer

CVE-2022-41899 / GHSA-27rc-728f-x5w2

More information

Details

Impact

Inputs dense_features or example_state_data not of rank 2 will trigger a CHECK fail in SdcaOptimizer.

import tensorflow as tf

tf.raw_ops.SdcaOptimizer(
    sparse_example_indices=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.int64, maxval=100)],
    sparse_feature_indices=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.int64, maxval=100)],
    sparse_feature_values=8 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100)],
    dense_features=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100)],
    example_weights=tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100),
    example_labels=tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100),
    sparse_indices=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.int64, maxval=100)],
    sparse_weights=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100)],
    dense_weights=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100)],
    example_state_data=tf.random.uniform([5,5,5,3], dtype=tf.dtypes.float32, maxval=100),
    loss_type="squared_loss",
    l1=0.0,
    l2=0.0,
    num_loss_partitions=1,
    num_inner_iterations=1,
    adaptative=False,)

Patches

We have patched the issue in GitHub commit 80ff197d03db2a70c6a111f97dcdacad1b0babfa.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Zizhuang Deng of IIE, UCAS

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27rc-728f-x5w2
• https://nvd.nist.gov/vuln/detail/CVE-2022-41899
• https://github.com/tensorflow/tensorflow/commit/80ff197d03db2a70c6a111f97dcdacad1b0babfa
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/sdca_internal.cc
• https://github.com/advisories/GHSA-27rc-728f-x5w2

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess

CVE-2022-41900 / GHSA-xvwp-h6jv-7472

More information

Details

Impact

An input pooling_ratio that is smaller than 1 will trigger a heap OOB in tf.raw_ops.FractionalMaxPool and tf.raw_ops.FractionalAvgPool.

Patches

We have patched the issue in GitHub commit 216525144ee7c910296f5b05d214ca1327c9ce48.

The fix will be included in TensorFlow 2.11.0. We will also cherry pick this commit on TensorFlow 2.10.1.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Severity

• CVSS Score: 7.1 / 10 (High)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472
• https://nvd.nist.gov/vuln/detail/CVE-2022-41900
• https://github.com/tensorflow/tensorflow/commit/216525144ee7c910296f5b05d214ca1327c9ce48
• https://github.com/advisories/GHSA-xvwp-h6jv-7472

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

CHECK_EQ fail via input in SparseMatrixNNZ

CVE-2022-41901 / GHSA-g9fm-r5mm-rf9f

More information

Details

Impact

An input sparse_matrix that is not a matrix with a shape with rank 0 will trigger a CHECK fail in tf.raw_ops.SparseMatrixNNZ.

import tensorflow as tf
tf.raw_ops.SparseMatrixNNZ(sparse_matrix=[])

Patches

We have patched the issue in GitHub commit f856d02e5322821aad155dad9b3acab1e9f5d693.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Kang Hong Jin

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9fm-r5mm-rf9f
• https://nvd.nist.gov/vuln/detail/CVE-2022-41901
• https://github.com/tensorflow/tensorflow/commit/f856d02e5322821aad155dad9b3acab1e9f5d693
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/sparse/sparse_matrix.h
• https://github.com/advisories/GHSA-g9fm-r5mm-rf9f

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

Out of bounds write in grappler in Tensorflow

CVE-2022-41902 / GHSA-cg88-rpvp-cjv5

More information

Details

Impact

The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.

Patches

We have patched the issue in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7.

The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.10.1.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Severity

• CVSS Score: 7.1 / 10 (High)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cg88-rpvp-cjv5
• https://github.com/tensorflow/tensorflow/commit/a65411a1d69edfb16b25907ffb8f73556ce36bb7
• https://nvd.nist.gov/vuln/detail/CVE-2022-41902
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/grappler/utils/functions.cc#L221
• https://github.com/advisories/GHSA-cg88-rpvp-cjv5

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

Overflow in ResizeNearestNeighborGrad

CVE-2022-41907 / GHSA-368v-7v32-52fx

More information

Details

Impact

When tf.raw_ops.ResizeNearestNeighborGrad is given a large size input, it overflows.

import tensorflow as tf

align_corners = True
half_pixel_centers = False
grads = tf.constant(1, shape=[1,8,16,3], dtype=tf.float16)
size = tf.constant([1879048192,1879048192], shape=[2], dtype=tf.int32)
tf.raw_ops.ResizeNearestNeighborGrad(grads=grads, size=size, align_corners=align_corners, half_pixel_centers=half_pixel_centers)

Patches

We have patched the issue in GitHub commit 00c821af032ba9e5f5fa3fe14690c8d28a657624.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Neophytos Christou from the Secure Systems Lab (SSL) at Brown University.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-368v-7v32-52fx
• https://nvd.nist.gov/vuln/detail/CVE-2022-41907
• https://github.com/tensorflow/tensorflow/commit/00c821af032ba9e5f5fa3fe14690c8d28a657624
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/resize_nearest_neighbor_op.cc
• https://github.com/advisories/GHSA-368v-7v32-52fx

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

CHECK fail via inputs in PyFunc

CVE-2022-41908 / GHSA-mv77-9g28-cwg3

More information

Details

Impact

An input token that is not a UTF-8 bytestring will trigger a CHECK fail in tf.raw_ops.PyFunc.

import tensorflow as tf

value = tf.constant(value=[1,2])
token = b'\xb0'
dataType = [tf.int32]

tf.raw_ops.PyFunc(input=value,token=token,Tout=dataType)

Patches

We have patched the issue in GitHub commit 9f03a9d3bafe902c1e6beb105b2f24172f238645.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by pattarakritr@smu.edu.sg

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv77-9g28-cwg3
• https://nvd.nist.gov/vuln/detail/CVE-2022-41908
• https://github.com/tensorflow/tensorflow/commit/9f03a9d3bafe902c1e6beb105b2f24172f238645
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/lib/core/py_func.cc
• https://github.com/advisories/GHSA-mv77-9g28-cwg3

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

Segfault in CompositeTensorVariantToComponents

CVE-2022-41909 / GHSA-rjx6-v474-2ch9

More information

Details

Impact

An input encoded that is not a valid CompositeTensorVariant tensor will trigger a segfault in tf.raw_ops.CompositeTensorVariantToComponents.

import tensorflow as tf

encode = tf.raw_ops.EmptyTensorList(element_dtype=tf.int32, element_shape=[10, 15], max_num_elements=2)
meta= ""
component=[tf.int32]

print(tf.raw_ops.CompositeTensorVariantToComponents(encoded=encode,metadata=meta,Tcomponents=component))

Patches

We have patched the issue in GitHub commits bf594d08d377dc6a3354d9fdb494b32d45f91971 and 660ce5a89eb6766834bdc303d2ab3902aef99d3d.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by pattarakritr@smu.edu.sg

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjx6-v474-2ch9
• https://nvd.nist.gov/vuln/detail/CVE-2022-41909
• https://github.com/tensorflow/tensorflow/commit/660ce5a89eb6766834bdc303d2ab3902aef99d3d
• https://github.com/tensorflow/tensorflow/commit/bf594d08d377dc6a3354d9fdb494b32d45f91971
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/lib/core/py_func.cc
• https://github.com/advisories/GHSA-rjx6-v474-2ch9

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

Heap overflow in QuantizeAndDequantizeV2

CVE-2022-41910 / GHSA-frqp-wp83-qggv

More information

Details

Impact

The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.

import tensorflow as tf
@​tf.function
def test():
    tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5],
    								   input_min=[1.0],
    								   input_max=[10.0],
    								   signed_input=True,
    								   num_bits=1,
    								   range_given=True,
    								   round_mode='HALF_TO_EVEN',
    								   narrow_range=True,
    								   axis=0x7fffffff)
test()

Patches

We have patched the issue in GitHub commit 7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb.

The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.10.1.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-frqp-wp83-qggv
• https://nvd.nist.gov/vuln/detail/CVE-2022-41910
• https://github.com/tensorflow/tensorflow/commit/a65411a1d69edfb16b25907ffb8f73556ce36bb7
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/grappler/utils/functions.cc#L221
• https://github.com/advisories/GHSA-frqp-wp83-qggv

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

Invalid char to bool conversion when printing a tensor

CVE-2022-41911 / GHSA-pf36-r9c6-h97j

More information

Details

Impact

When printing a tensor, we get it's data as a const char* array (since that's the underlying storage) and then we typecast it to the element type. However, conversions from char to bool are undefined if the char is not 0 or 1, so sanitizers/fuzzers will crash.

Patches

We have patched the issue in GitHub commit 1be743703279782a357adbf9b77dcb994fe8b508.

The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.10.1, TensorFlow 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability was discovered via internal fuzzing.

Severity

• CVSS Score: 4.8 / 10 (Medium)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

References

• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pf36-r9c6-h97j
• https://nvd.nist.gov/vuln/detail/CVE-2022-41911
• https://github.com/tensorflow/tensorflow/commit/1be743703279782a357adbf9b77dcb994fe8b508
• https://github.com/tensorflow/tensorflow/blob/807cae8a807960fd7ac2313cde73a11fc15e7942/tensorflow/core/framework/tensor.cc#L1200-L1227
• https://github.com/advisories/GHSA-pf36-r9c6-h97j

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

---

CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode

GHSA-xf83-q765-xm6m

More information

Details

Impact

Another instance of CVE-2022-35991, where TensorListScatter and TensorListScatterV2 crash via non scalar inputs inelement_shape, was found in eager mode and fixed.

import tensorflow as tf
arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None)
arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)
arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)
arg_3=''
tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1, 
element_shape=arg_2, name=arg_3)

Patches

We have patched the issue in GitHub commit bf9932fc907aff0e9e8cccf769e8b00d30fd81a1.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution