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Type confusion leading to segfault in Tensorflow

Moderate severity GitHub Reviewed Published Feb 2, 2022 in tensorflow/tensorflow • Updated Feb 3, 2023

Package

pip tensorflow (pip)

Affected versions

< 2.5.3
>= 2.6.0, < 2.6.3
= 2.7.0

Patched versions

2.5.3
2.6.3
2.7.1
pip tensorflow-cpu (pip)
< 2.5.3
>= 2.6.0, < 2.6.3
= 2.7.0
2.5.3
2.6.3
2.7.1
pip tensorflow-gpu (pip)
< 2.5.3
>= 2.6.0, < 2.6.3
= 2.7.0
2.5.3
2.6.3
2.7.1

Description

Impact

The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion:

import tensorflow as tf

@tf.function
def test():
  y = tf.raw_ops.ConcatV2(
    values=[[1,2,3],[4,5,6]],
    axis = 0xb500005b)
  return y

test()

The axis argument is translated into concat_dim in the ConcatShapeHelper helper function. Then, a value for min_rank is computed based on concat_dim. This is then used to validate that the values tensor has at least the required rank:

  int64_t concat_dim;
  if (concat_dim_t->dtype() == DT_INT32) {
    concat_dim = static_cast<int64_t>(concat_dim_t->flat<int32>()(0));
  } else {
    concat_dim = concat_dim_t->flat<int64_t>()(0);
  }

  // Minimum required number of dimensions.
  const int min_rank = concat_dim < 0 ? -concat_dim : concat_dim + 1;

  // ...
  ShapeHandle input = c->input(end_value_index - 1);
  TF_RETURN_IF_ERROR(c->WithRankAtLeast(input, min_rank, &input));

However, WithRankAtLeast receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented:

Status InferenceContext::WithRankAtLeast(ShapeHandle shape, int64_t rank,
                                         ShapeHandle* out) {
  if (rank > kint32max) {
    return errors::InvalidArgument("Rank cannot exceed kint32max");
  }
  // ...
}

Due to the fact that min_rank is a 32-bits value and the value of axis, the rank argument is a negative value, so the error check is bypassed.

Patches

We have patched the issue in GitHub commit 08d7b00c0a5a20926363849f611729f53f3ec022.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.

References

@mihaimaruseac mihaimaruseac published to tensorflow/tensorflow Feb 2, 2022
Published by the National Vulnerability Database Feb 3, 2022
Reviewed Feb 3, 2022
Published to the GitHub Advisory Database Feb 10, 2022
Last updated Feb 3, 2023

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Network
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

EPSS score

0.245%
(65th percentile)

CVE ID

CVE-2022-21731

GHSA ID

GHSA-m4hf-j54p-p353
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