Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

chore(deps): update dependency tensorflow to v2.12.1 [security] #44

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

chore(deps): update dependency tensorflow to v2.12.1 [security] #44

wants to merge 1 commit into from

Conversation

renovate[bot]
Copy link
Contributor

@renovate renovate bot commented Jun 2, 2024 •
edited
Loading

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
tensorflow ==2.11 -> ==2.12.1 age adoption passing confidence

GitHub Vulnerability Alerts

CVE-2023-25658

Impact

Out of bounds read in GRUBlockCellGrad

func = tf.raw_ops.GRUBlockCellGrad

para = {'x': [[21.1, 156.2], [83.3, 115.4]], 'h_prev': array([[136.5],
      [136.6]]), 'w_ru': array([[26.7,  0.8],
      [47.9, 26.1],
      [26.2, 26.3]]), 'w_c': array([[ 0.4],
      [31.5],
      [ 0.6]]), 'b_ru': array([0.1, 0.2 ], dtype=float32), 'b_c': 0x41414141, 'r': array([[0.3],
      [0.4]], dtype=float32), 'u': array([[5.7],
      [5.8]]), 'c': array([[52.9],
      [53.1]]), 'd_h': array([[172.2],
      [188.3 ]])}

Patches

We have patched the issue in GitHub commit ff459137c2716a2a60f7d441b855fcb466d778cb.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by r3pwnx.

CVE-2023-27579

Impact

Constructing a tflite model with a paramater filter_input_channel of less than 1 gives a FPE.

Patches

We have patched the issue in GitHub commit 34f8368c535253f5c9cb3a303297743b62442aaa.

The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.

For more information

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

Attribution

This vulnerability was reported by Wang Xuan of Qihoo 360 AIVul Team.

CVE-2023-25801

Impact

nn_ops.fractional_avg_pool_v2 and nn_ops.fractional_max_pool_v2 require the first and fourth elements of their parameter pooling_ratio to be equal to 1.0, as pooling on batch and channel dimensions is not supported.

import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import nn_ops
try:
  arg_0_tensor = tf.random.uniform([3, 30, 50, 3], dtype=tf.float64)
  arg_0 = tf.identity(arg_0_tensor)
  arg_1_0 = 2
  arg_1_1 = 3
  arg_1_2 = 1
  arg_1_3 = 1
  arg_1 = [arg_1_0,arg_1_1,arg_1_2,arg_1_3,]
  arg_2 = True
  arg_3 = True
  seed = 341261001
  out = nn_ops.fractional_avg_pool_v2(arg_0,arg_1,arg_2,arg_3,seed=seed,)
except Exception as e:
  print("Error:"+str(e))

Patches

We have patched the issue in GitHub commit ee50d1e00f81f62a4517453f721c634bbb478307.

The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.

For more information

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

Attribution

This vulnerability was reported by dmc1778, of nimashiri2012@​gmail.com.

CVE-2023-25676

Impact

When running with XLA, tf.raw_ops.ParallelConcat segfaults with a nullptr dereference when given a parameter shape with rank that is not greater than zero.

import tensorflow as tf

func = tf.raw_ops.ParallelConcat
para = {'shape':  0, 'values': [1]}

@​tf.function(jit_compile=True)
def test():
   y = func(**para)
   return y

test()

Patches

We have patched the issue in GitHub commit da66bc6d5ff466aee084f9e7397980a24890cd15.

The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.

For more information

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

Attribution

This vulnerability has been reported by r3pwnx of 360 AIVul Team

CVE-2023-25675

Impact

When running with XLA, tf.raw_ops.Bincount segfaults when given a parameter weights that is neither the same shape as parameter arr nor a length-0 tensor.

import tensorflow as tf

func = tf.raw_ops.Bincount
para={'arr': 6, 'size': 804, 'weights': [52, 351]}

@​tf.function(jit_compile=True)
def fuzz_jit():
 y = func(**para)
 return y

print(fuzz_jit())

Patches

We have patched the issue in GitHub commit 8ae76cf085f4be26295d2ecf2081e759e04b8acf.

The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.

For more information

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

Attribution

This vulnerability has been reported by r3pwnx of 360 AIVul Team

CVE-2023-25674

Impact

NPE in RandomShuffle with XLA enable

import tensorflow as tf

func = tf.raw_ops.RandomShuffle
para = {'value': 1e+20, 'seed': -4294967297, 'seed2': -2147483649}

@​tf.function(jit_compile=True)
def test():
   y = func(**para)
   return y

test()

Patches

We have patched the issue in GitHub commit 728113a3be690facad6ce436660a0bc1858017fa.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by r3pwnx

CVE-2023-25673

Impact

FPE in TensorListSplit with XLA

import tensorflow as tf

func = tf.raw_ops.TensorListSplit
para = {'tensor': [1], 'element_shape': -1, 'lengths': [0]}

@​tf.function(jit_compile=True)
def fuzz_jit():
 y = func(**para)
 return y

print(fuzz_jit())

Patches

We have patched the issue in GitHub commit 728113a3be690facad6ce436660a0bc1858017fa.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by r3pwnx

CVE-2023-25672

Impact

The function tf.raw_ops.LookupTableImportV2 cannot handle scalars in the values parameter and gives an NPE.

import tensorflow as tf

v = tf.Variable(1)

@​tf.function(jit_compile=True)
def test():
   func = tf.raw_ops.LookupTableImportV2
   para={'table_handle': v.handle,'keys': [62.98910140991211, 94.36528015136719], 'values': -919}

   y = func(**para)
   return y

print(test())

Patches

We have patched the issue in GitHub commit 980b22536abcbbe1b4a5642fc940af33d8c19b69.

The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.

For more information

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

Attribution

This vulnerability has been reported by r3pwnx of 360 AIVul Team

CVE-2023-25671

Impact

Out-of-bounds access due to mismatched integer type sizes in ValueMap::Manager::GetValueOrCreatePlaceholder. Bug with tfg-translate call to InitMlir. The problem happens with generic functions, as it is already handled for non-generic functions. This is because they, unlike non-generic functions, are using the "old importer". A better long-term solution may be to have the "new importer" handle generic functions.

Patches

We have patched the issue in GitHub

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by r3pwnx

Affiliation

360 AIVul

CVE-2023-25670

Impact

NPE in QuantizedMatMulWithBiasAndDequantize with MKL enable

import tensorflow as tf

func = tf.raw_ops.QuantizedMatMulWithBiasAndDequantize
para={'a': tf.constant(138, dtype=tf.quint8), 'b': tf.constant(4, dtype=tf.qint8), 'bias': [[31.81644630432129, 47.21876525878906], [109.95201110839844, 152.07968139648438]], 'min_a': 141.5337138686371, 'max_a': [73.84139251708984, 173.15280151367188], 'min_b': [], 'max_b': [[16.128345489501953, 193.26820373535156]], 'min_freezed_output': [], 'max_freezed_output': [115.50032806396484, 156.974853515625], 'Toutput': 1.0, 'transpose_a': True, 'transpose_b': False, 'input_quant_mode': 'MIN_FIRST'}

func(**para)

Patches

We have patched the issue in GitHub commit 8a47a39d9697969206d23a523c977238717e8727.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by r3pwnx

CVE-2023-25669

Impact

If the stride and window size are not positive for tf.raw_ops.AvgPoolGrad, it can give an FPE.

import tensorflow as tf
import numpy as np

@​tf.function(jit_compile=True)
def test():
   y = tf.raw_ops.AvgPoolGrad(orig_input_shape=[1,0,0,0], grad=[[[[0.39117979]]]], ksize=[1,0,0,0], strides=[1,0,0,0], padding="SAME", data_format="NCHW")
   return y

print(test())

Patches

We have patched the issue in GitHub commit 1295ae4dbb52fe06b19733b0257e2340d7b63b8d.

The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.

For more information

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

Attribution

This vulnerability has been reported by r3pwnx of 360 AIVul Team

CVE-2023-25668

Impact

Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE.
When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound.
Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too.

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 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

CVE-2023-25667

Impact

Integer overflow occurs when 2^31 <= num_frames * height * width * channels < 2^32, for example Full HD screencast of at least 346 frames.

import urllib.request
dat = urllib.request.urlopen('https://raw.githubusercontent.com/tensorflow/tensorflow/1c38ad9b78ffe06076745a1ee00cec42f39ff726/tensorflow/core/lib/gif/testdata/3g_multiframe.gif').read()
import tensorflow as tf
tf.io.decode_gif(dat)

Patches

We have patched the issue in GitHub commit 8dc723fcdd1a6127d6c970bd2ecb18b019a1a58d.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by Andrei

CVE-2023-25666

Impact

version:2.11.0 //core/ops/audio_ops.cc:70

Status SpectrogramShapeFn(InferenceContext* c) { ShapeHandle input; TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &input)); int32_t window_size; TF_RETURN_IF_ERROR(c->GetAttr("window_size", &window_size)); int32_t stride; TF_RETURN_IF_ERROR(c->GetAttr("stride", &stride)); .....[1]

DimensionHandle input_length = c->Dim(input, 0); DimensionHandle input_channels = c->Dim(input, 1);

DimensionHandle output_length; if (!c->ValueKnown(input_length)) { output_length = c->UnknownDim(); } else { const int64_t input_length_value = c->Value(input_length); const int64_t length_minus_window = (input_length_value - window_size); int64_t output_length_value; if (length_minus_window < 0) { output_length_value = 0; } else { output_length_value = 1 + (length_minus_window / stride); .....[2] } output_length = c->MakeDim(output_length_value); }

Get the value of stride at [1], and the used at [2]

import tensorflow as tf

para = {'input': tf.constant([[14.], [24.]], dtype=tf.float32), 'window_size': 1, 'stride': 0, 'magnitude_squared': False}
func = tf.raw_ops.AudioSpectrogram

@&#8203;tf.function(jit_compile=True)
def fuzz_jit():
   y = func(**para)
   return y

fuzz_jit()

Patches

We have patched the issue in GitHub commit d0d4e779da0d0f56499c6fa5ba09f0a576cc6b14.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by r3pwnx

CVE-2023-25665

Impact

When SparseSparseMaximum is given invalid sparse tensors as inputs, it can give an NPE.

import tensorflow as tf
tf.raw_ops.SparseSparseMaximum(
 a_indices=[[1]],
 a_values =[ 0.1 ],
 a_shape = [2],
 b_indices=[[]],
 b_values =[2 ],
 b_shape = [2],
)

Patches

We have patched the issue in GitHub commit 5e0ecfb42f5f65629fd7a4edd6c4afe7ff0feb04.

The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.

For more information

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

Attribution

This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team

CVE-2023-25664

Impact

import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
import tensorflow as tf
print(tf.__version__)
with tf.device("CPU"):
    ksize = [1, 40, 128, 1]
    strides = [1, 128, 128, 30]
    padding = "SAME"
    data_format = "NHWC"
    orig_input_shape = [11, 9, 78, 9]
    grad = tf.saturate_cast(tf.random.uniform([16, 16, 16, 16], minval=-128, maxval=129, dtype=tf.int64), dtype=tf.float32)
    res = tf.raw_ops.AvgPoolGrad(
        ksize=ksize,
        strides=strides,
        padding=padding,
        data_format=data_format,
        orig_input_shape=orig_input_shape,
        grad=grad,
    )

Patches

We have patched the issue in GitHub commit ddaac2bdd099bec5d7923dea45276a7558217e5b.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by [email protected]

CVE-2023-25663

Impact

When ctx->step_containter() is a null ptr, the Lookup function will be executed with a null pointer.

import tensorflow as tf
tf.raw_ops.TensorArrayConcatV2(handle=['a', 'b'], flow_in = 0.1, dtype=tf.int32, element_shape_except0=1)

Patches

We have patched the issue in GitHub commit 239139d2ae6a81ae9ba499ad78b56d9b2931538a.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by Yu Tian

CVE-2023-25662

Impact

TFversion 2.11.0 //tensorflow/core/ops/array_ops.cc:1067 const Tensor* hypothesis_shape_t = c->input_tensor(2); std::vector dims(hypothesis_shape_t->NumElements() - 1); for (int i = 0; i < dims.size(); ++i) { dims[i] = c->MakeDim(std::max(h_values(i), t_values(i))); }

if hypothesis_shape_t is empty, hypothesis_shape_t->NumElements() - 1 will be integer overflow, and the it will deadlock

import tensorflow as tf
para={
    'hypothesis_indices': [[]],
    'hypothesis_values': ['tmp/'],
    'hypothesis_shape': [],
    'truth_indices': [[]],
    'truth_values': [''],
    'truth_shape': [],
    'normalize': False
    }
tf.raw_ops.EditDistance(**para)

Patches

We have patched the issue in GitHub commit 08b8e18643d6dcde00890733b270ff8d9960c56c.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

Attribution

This vulnerability has been reported by r3pwnx

CVE-2023-25660

Impact

When the parameter summarize of tf.raw_ops.Print is zero, the new method SummarizeArray<bool> will reference to a nullptr, leading to a seg fault.

import tensorflow as tf

tf.raw_ops.Print(input =  tf.constant([1, 1, 1, 1],dtype=tf.int32),
                            data =  [[False, False, False, False], [False], [False, False, False]],
                            message =  'tmp/I',
                            first_n = 100,
                            summarize = 0)

Patches

We have patched the issue in GitHub commit 6d423b8bcc9aa9f5554dc988c1c16d038b508df1.

The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.

For more information

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

Attribution

This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team

CVE-2023-25659

Impact

If the parameter indices for DynamicStitch does not match the shape of the parameter data, it can trigger an stack OOB read.

import tensorflow as tf
func = tf.raw_ops.DynamicStitch
para={'indices': [[0xdeadbeef], [405], [519], [758], [1015]], 'data': [[110.27793884277344], [120.29475402832031], [157.2418212890625], [157.2626953125], [188.45382690429688]]}
y = func(**para)

Patches

We have patched the issue in GitHub commit ee004b18b976eeb5a758020af8880236cd707d05.

The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.

For more information

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

Attribution

This has been reported via Google OSS VRP.

CVE-2023-25661

Impact

A malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack.
To minimize the bug, we built a simple single-layer TensorFlow model containing a Convolution3DTranspose layer, which works well with expected inputs and can be deployed in real-world systems. However, if we call the model with a malicious input which has a zero dimension, it gives Check Failed failure and crashes.

import tensorflow as tf

class MyModel(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding="same")
        
    def call(self, input):
        return self.conv(input)
model = MyModel() # Defines a valid model.

x = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input.
output = model.predict(x)
print(output.shape) # (1, 32, 32, 32, 2)

x = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input.
output = model(x) # crash

This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services.

Patches

We have patched the issue in

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

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

CVE-2023-33976

Impact

array_ops.upper_bound causes a segfault when not given a rank 2 tensor.

Patches

We have patched the issue in GitHub commit 915884fdf5df34aaedd00fc6ace33a2cfdefa586.

The fix will be included in TensorFlow 2.13. We will also cherrypick this commit in TensorFlow 2.12.1.

For more information

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

Attribution

This vulnerability has been reported by dmc1778

Configuration

📅 Schedule: Branch creation - "" (UTC), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR is behind base branch, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

  • If you want to rebase/retry this PR, check this box

This PR was generated by Mend Renovate. View the repository job log.

@renovate renovate bot force-pushed the renovate/pypi-tensorflow-vulnerability branch 4 times, most recently from 4161cd5 to da6f17f Compare June 6, 2024 13:05
@renovate renovate bot force-pushed the renovate/pypi-tensorflow-vulnerability branch from da6f17f to 03541a8 Compare July 17, 2024 11:18
@renovate renovate bot force-pushed the renovate/pypi-tensorflow-vulnerability branch from 03541a8 to 33e2810 Compare July 30, 2024 21:45
@renovate renovate bot changed the title chore(deps): update dependency tensorflow to v2.11.1 [security] chore(deps): update dependency tensorflow to v2.12.1 [security] Jul 30, 2024
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Reviewers
No reviews
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

Successfully merging this pull request may close these issues.
0 participants