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.
CVE-2020-15211 - Medium Severity Vulnerability
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/12C33/tag/2.0.8/pod/TensorIO-TensorFlow-2.0_8.tar.gz
Path to dependency file: tensorio-ios/TensorFlowExample/Podfile.lock
Path to vulnerable library: tensorio-ios/TensorFlowExample/Podfile.lock,tensorio-ios/SwiftTensorFlowExample/Podfile.lock
Dependency Hierarchy: - TensorIO/TensorFlow-1.2.5 (Root Library) - :x: **TensorIOTensorFlow-2.0.8** (Vulnerable Library)
Found in HEAD commit: 9ca8c916de11c6aadffe21f686982bf1f761da36
Found in base branch: master
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
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.Type: Upgrade version
Origin: https://github.com/tensorflow/tensorflow/security/advisories/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