CVE-2020-152114.8MediumCWE-125

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

tensorflow

Vendor fix available · Affected: tensorflow · Exploitation: unlikely (under 1%)

Not in CyberSIXT coverage yet: no article names this CVE. This page shows the public record.

0.9%probability of exploitation in the next 30 days

60th percentile

050100

Higher than 60% of scored CVEs

4.8MediumCVSS 3.1

LowMediumHighCrit.

DescriptionCVE.org

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 products1

From the CVE record. A product or version that is not listed is unknown, not unaffected.

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

On the Atlas

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Exploitation: Unlikely (under 1%). EPSS under 1%, or no score yet.
CISA KEV: Not listed
Public check: No public nuclei template
Weakness: Out-of-bounds Read · Memory Corruption
Vendor: tensorflow
Published: September 2020
Band: Unlikely (under 1%), September 2020

CoverageNo articles yet

No CyberSIXT article names CVE-2020-15211 yet, so there is no coverage timeline, action brief or change log for it. Everything on this page comes from the public CVE record, CISA KEV, EPSS and the nuclei template index. Search the archive or see the CVEs in the news.