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Integer overflow in TFLite

High 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

An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations:

  int embedding_size = 1;
  int lookup_size = 1;
  for (int i = 0; i < lookup_rank - 1; i++, k++) {
    const int dim = dense_shape->data.i32[i];
    lookup_size *= dim;
    output_shape->data[k] = dim;
  }
  for (int i = 1; i < embedding_rank; i++, k++) {
    const int dim = SizeOfDimension(value, i);
    embedding_size *= dim;
    output_shape->data[k] = dim;
  } 

Both embedding_size and lookup_size are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication.

In certain scenarios, this can then result in heap OOB read/write.

Patches

We have patched the issue in GitHub commits f19be71717c497723ba0cea0379e84f061a75e01, 1de49725a5fc4e48f1a3b902ec3599ee99283043 and a4e401da71458d253b05e41f28637b65baf64be4.

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 Wang Xuan of Qihoo 360 AIVul Team.

References

@mihaimaruseac mihaimaruseac published to tensorflow/tensorflow Feb 2, 2022
Reviewed Feb 3, 2022
Published by the National Vulnerability Database Feb 4, 2022
Published to the GitHub Advisory Database Feb 9, 2022
Last updated Feb 3, 2023

Severity

High

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
High
Integrity
High
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:H/I:H/A:H

EPSS score

0.246%
(65th percentile)

Weaknesses

CVE ID

CVE-2022-23559

GHSA ID

GHSA-98p5-x8x4-c9m5
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