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Fencer Intel
medium 4.8

CVE-2020-15211 json

Out of bounds access in tensorflow-lite

published 6y ago modified 2mo ago also BIT-tensorflow-2020-15211 , EUVD-2020-0207 , GHSA-cvpc-8phh-8f45 , PYSEC-2020-134 , PYSEC-2020-291 , PYSEC-2020-326

cvss
4.8
version 3.1, CNA
cisa kev
Not listed
not in CISA's catalog
epss
0.9%
EPSS 0.9% chance of exploitation in 30 days, percentile 60%
public exploits
0
none known

Summary

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.

Patch / fix

  • pypi tensorflow 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1
  • pypi tensorflow-cpu 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1
  • pypi tensorflow-gpu 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1
  • bitnami tensorflow 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1
  • google tensorflow 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1
  • tensorflow2 openSUSE Leap 15.2 2.1.2-lp152.7.3.1
  • tensorflow2-lite openSUSE Leap 15.2 2.1.2-lp152.7.3.1
  • tensorflow2_2_1_2-gnu-hpc openSUSE Leap 15.2 2.1.2-lp152.7.3.1
  • tensorflow2_2_1_2-gnu-openmpi2-hpc openSUSE Leap 15.2 2.1.2-lp152.7.3.1

opensuse advisories openSUSE-SU-2020:1766-1

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Exploit prediction

0.92% chance of exploitation in the next 30 days, higher than 60% of all scored CVEs (FIRST EPSS, 2026-11-09).

Exploits and checks

  • github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45 referenced PoC, tagged "exploit" by a source, unverified

CVSS scores

assigned by version score vector
CNA 3.1 4.8 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N
GitHub 3.1 4.8 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N
NVD 3.1 4.8 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N
NVD 2.0 5.8 AV:N/AC:M/Au:N/C:P/I:P/A:N
OSV 3.1 4.8 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N

Affected packages

ecosystem package affected fixed in
pypi 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
1.15.4
2.0.3
2.1.2
2.2.1
2.3.1
pypi tensorflow-cpu
<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
1.15.4
2.0.3
2.1.2
2.2.1
2.3.1
pypi tensorflow-gpu
<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
1.15.4
2.0.3
2.1.2
2.2.1
2.3.1
bitnami 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
1.15.4
2.0.3
2.1.2
2.2.1
2.3.1

Affected products

  • tensorflow tensorflow

Classification

Weakness

References

Sources

Fencer Intel keeps one entry per vulnerability: the CVE id wins, then GHSA, and records that alias each other are merged, except that two CVEs always keep separate entries. The same data is available as JSON.