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Vulnerability · Published Sep 25, 2020 · Assigned by GitHub_M

CVE-2020-15211Out of bounds access in tensorflow-lite

Medium 4.8

In plain English

A medium-severity out-of-bounds read in tensorflow from tensorflow. There are no confirmed reports of exploitation so far. The chance it is exploited in the next 30 days is estimated at 0.9% (higher than 60% of all known vulnerabilities). Fixed versions are available - upgrade the affected package.

Severity (CVSS 3.1)4.8Medium
Exploit odds (30 days)0.9%top 39.6% of all CVEs
Exploited in the wildNo reportsnot in CISA KEV
Attack toolingNoneno public tooling known

Official description

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.

Affected packages & fixed versions

EcosystemPackageFixed inAdvisory
PyPItensorflow2.1.2GHSA-cvpc-8phh-8f45
PyPItensorflow2.2.1GHSA-cvpc-8phh-8f45
PyPItensorflow2.3.1GHSA-cvpc-8phh-8f45
PyPItensorflow1.15.4GHSA-cvpc-8phh-8f45
PyPItensorflow1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1PYSEC-2020-134
PyPItensorflow2.0.3GHSA-cvpc-8phh-8f45
PyPItensorflow-cpu2.3.1GHSA-cvpc-8phh-8f45
PyPItensorflow-cpu1.15.4GHSA-cvpc-8phh-8f45
PyPItensorflow-cpu2.0.3GHSA-cvpc-8phh-8f45
PyPItensorflow-cpu2.1.2GHSA-cvpc-8phh-8f45
PyPItensorflow-cpu2.2.1GHSA-cvpc-8phh-8f45
PyPItensorflow-cpu1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1PYSEC-2020-291
PyPItensorflow-gpu2.1.2GHSA-cvpc-8phh-8f45
PyPItensorflow-gpu2.2.1GHSA-cvpc-8phh-8f45
PyPItensorflow-gpu2.3.1GHSA-cvpc-8phh-8f45
PyPItensorflow-gpu1.15.4GHSA-cvpc-8phh-8f45
PyPItensorflow-gpu1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1PYSEC-2020-326
PyPItensorflow-gpu2.0.3GHSA-cvpc-8phh-8f45

Affected products (as reported)

VendorProductVersions
tensorflow tensorflow < 1.15.4>= 2.0.0, < 2.0.3>= 2.1.0, < 2.1.2>= 2.2.0, < 2.2.1>= 2.3.0, < 2.3.1

Weakness: CWE-125, CWE-787 · Vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N

References