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Access to invalid memory during shape inference in `Cudnn*` ops

High severity GitHub Reviewed Published Nov 4, 2021 in tensorflow/tensorflow • Updated Nov 7, 2024

Package

pip tensorflow (pip)

Affected versions

>= 2.6.0, < 2.6.1
>= 2.5.0, < 2.5.2
< 2.4.4

Patched versions

2.6.1
2.5.2
2.4.4
pip tensorflow-cpu (pip)
>= 2.6.0, < 2.6.1
>= 2.5.0, < 2.5.2
< 2.4.4
2.6.1
2.5.2
2.4.4
pip tensorflow-gpu (pip)
>= 2.6.0, < 2.6.1
>= 2.5.0, < 2.5.2
< 2.4.4
2.6.1
2.5.2
2.4.4

Description

Impact

The shape inference code for the Cudnn* operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow:

import tensorflow as tf

@tf.function
def func():
  return tf.raw_ops.CudnnRNNV3(
    input=[0.1, 0.1],
    input_h=[0.5],
    input_c=[0.1, 0.1, 0.1], 
    params=[0.5, 0.5],
    sequence_lengths=[-1, 0, 1])
  
func() 

This occurs because the ranks of the input, input_h and input_c parameters are not validated, but code assumes they have certain values:

auto input_shape = c->input(0);
auto input_h_shape = c->input(1);
auto seq_length = c->Dim(input_shape, 0);
auto batch_size = c->Dim(input_shape, 1);  // assumes rank >= 2
auto num_units = c->Dim(input_h_shape, 2); // assumes rank >= 3

Patches

We have patched the issue in GitHub commit af5fcebb37c8b5d71c237f4e59c6477015c78ce6.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.

References

@mihaimaruseac mihaimaruseac published to tensorflow/tensorflow Nov 4, 2021
Published by the National Vulnerability Database Nov 5, 2021
Reviewed Nov 8, 2021
Published to the GitHub Advisory Database Nov 10, 2021
Last updated Nov 7, 2024

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
Local
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:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

EPSS score

0.048%
(19th percentile)

CVE ID

CVE-2021-41221

GHSA ID

GHSA-cqv6-3phm-hcwx

Source code

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