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githubGitHub Advisory DatabaseGHSA-9PX9-73FG-3FQP
HistoryFeb 09, 2022 - 11:29 p.m.

Null pointer dereference in Grappler's `IsConstant`

2022-02-0923:29:14
CWE-476
GitHub Advisory Database
github.com
13
tensorflow
grappler
null pointer.

CVSS2

4

Attack Vector

NETWORK

Attack Complexity

LOW

Authentication

SINGLE

Confidentiality Impact

NONE

Integrity Impact

NONE

Availability Impact

PARTIAL

AV:N/AC:L/Au:S/C:N/I:N/A:P

CVSS3

6.5

Attack Vector

NETWORK

Attack Complexity

LOW

Privileges Required

LOW

User Interaction

NONE

Scope

UNCHANGED

Confidentiality Impact

NONE

Integrity Impact

NONE

Availability Impact

HIGH

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

EPSS

0.003

Percentile

65.9%

Impact

Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a SavedModel file (fixing the first one would trigger the same dereference in the second place):

First, during constant folding, the GraphDef might not have the required nodes for the binary operation:

  NodeDef* mul_left_child = node_map_->GetNode(node->input(0));
  NodeDef* mul_right_child = node_map_->GetNode(node->input(1));
  // One child must be constant, and the second must be Conv op.
  const bool left_child_is_constant = IsReallyConstant(*mul_left_child);
  const bool right_child_is_constant = IsReallyConstant(*mul_right_child);

If a node is missing, the correposning mul_*child would be null, and the dereference in the subsequent line would be incorrect.

We have a similar issue during IsIdentityConsumingSwitch:

  NodeDef* input_node = graph.GetNode(tensor_id.node());
  return IsSwitch(*input_node);

Patches

We have patched the issue in GitHub commits 0a365c029e437be0349c31f8d4c9926b69fa3fa1 and 045deec1cbdebb27d817008ad5df94d96a08b1bf.

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.

Affected configurations

Vulners
Node
tensorflow-gpuMatch2.7.0
OR
tensorflow-gpuRange2.6.02.6.3
OR
tensorflow-gpuRange<2.5.3
OR
tensorflow-cpuMatch2.7.0
OR
tensorflow-cpuRange2.6.02.6.3
OR
tensorflow-cpuRange<2.5.3
OR
tensorflowtensorflowMatch2.7.0
OR
tensorflowtensorflowRange2.6.02.6.3
OR
tensorflowtensorflowRange<2.5.3
VendorProductVersionCPE
*tensorflow-gpu2.7.0cpe:2.3:a:*:tensorflow-gpu:2.7.0:*:*:*:*:*:*:*
*tensorflow-gpu*cpe:2.3:a:*:tensorflow-gpu:*:*:*:*:*:*:*:*
*tensorflow-cpu2.7.0cpe:2.3:a:*:tensorflow-cpu:2.7.0:*:*:*:*:*:*:*
*tensorflow-cpu*cpe:2.3:a:*:tensorflow-cpu:*:*:*:*:*:*:*:*
tensorflowtensorflow2.7.0cpe:2.3:a:tensorflow:tensorflow:2.7.0:*:*:*:*:*:*:*
tensorflowtensorflow*cpe:2.3:a:tensorflow:tensorflow:*:*:*:*:*:*:*:*

CVSS2

4

Attack Vector

NETWORK

Attack Complexity

LOW

Authentication

SINGLE

Confidentiality Impact

NONE

Integrity Impact

NONE

Availability Impact

PARTIAL

AV:N/AC:L/Au:S/C:N/I:N/A:P

CVSS3

6.5

Attack Vector

NETWORK

Attack Complexity

LOW

Privileges Required

LOW

User Interaction

NONE

Scope

UNCHANGED

Confidentiality Impact

NONE

Integrity Impact

NONE

Availability Impact

HIGH

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

EPSS

0.003

Percentile

65.9%

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