CVE-2020-15211 (Medium) detected in TensorIO-1.2.3, TensorIOTensorFlow-2.0.7

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CVE-2020-15211 - Medium Severity Vulnerability

Vulnerable Libraries - TensorIO-1.2.3, TensorIOTensorFlow-2.0.7

TensorIO-1.2.3

On device inference with TensorFlow Lite or inference and training with full TensorFlow models using all the conveniences of Objective-C or Swift

Library home page: https://github.com/doc-ai/tensorio-ios/archive/1.2.3.zip

Path to dependency file: /example/ios/Podfile.lock

Path to vulnerable library: /example/ios/Podfile.lock

Dependency Hierarchy:

• react-native-tensorio-tensorflow-0.1.0 (Root Library)
  • ❌ TensorIO-1.2.3 (Vulnerable Library)

TensorIOTensorFlow-2.0.7

An unofficial build of TensorFlow for iOS used by TensorIO, supporting inference, evaluation, and training.

Library home page: https://storage.googleapis.com/tensorio-build/ios/release/2.0/xcodebuild/12B45b/tag/2.0.7/pod/TensorIO-TensorFlow-2.0_7.tar.gz

Path to dependency file: /example/ios/Podfile.lock

Path to vulnerable library: /example/ios/Podfile.lock

Dependency Hierarchy:

• react-native-tensorio-tensorflow-0.1.0 (Root Library)
  • TensorIO/TensorFlow-1.2.3
    • ❌ TensorIOTensorFlow-2.0.7 (Vulnerable Library)

Found in HEAD commit: 5cced2048a0851a07f123e0a3fe19d43b8e10c94

Found in base branch: master

Vulnerability Details

In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative -1 value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the -1 index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that only operators which accept optional inputs use the -1 special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.

Publish Date: 2020-09-25

URL: CVE-2020-15211

CVSS 3 Score Details (4.8)

Base Score Metrics:

• Exploitability Metrics:
  • Attack Vector: Network
  • Attack Complexity: High
  • Privileges Required: None
  • User Interaction: None
  • Scope: Unchanged
• Impact Metrics:
  • Confidentiality Impact: Low
  • Integrity Impact: Low
  • Availability Impact: None

For more information on CVSS3 Scores, click here.

Suggested Fix

Type: Upgrade version

Origin: GHSA-cvpc-8phh-8f45

Release Date: 2020-07-21

Fix Resolution: 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1