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Fencer Intel
medium 4.0

CVE-2020-15213 json

Denial of service in tensorflow-lite

published 6y ago modified 2mo ago also BIT-tensorflow-2020-15213 , EUVD-2020-0209 , GHSA-hjmq-236j-8m87 , PYSEC-2020-136 , PYSEC-2020-293 , PYSEC-2020-328

cvss
4.0
version 3.1, CNA
cisa kev
Not listed
not in CISA's catalog
epss
0.8%
EPSS 0.8% chance of exploitation in 30 days, percentile 55%
public exploits
0
none known

Summary

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Patch / fix

  • pypi tensorflow 2.2.1, 2.3.1
  • pypi tensorflow-cpu 2.2.1, 2.3.1
  • pypi tensorflow-gpu 2.2.1, 2.3.1
  • bitnami tensorflow 2.2.1, 2.3.1
  • google tensorflow 2.2.1, 2.3.1

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Exploit prediction

0.76% chance of exploitation in the next 30 days, higher than 55% of all scored CVEs (FIRST EPSS, 2026-11-09).

Exploits and checks

  • github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87 referenced PoC, tagged "exploit" by a source, unverified

CVSS scores

assigned by version score vector
CNA 3.1 4.0 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L
GitHub 3.1 4.0 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L
NVD 3.1 4.0 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L
NVD 2.0 4.3 AV:N/AC:M/Au:N/C:N/I:N/A:P
OSV 3.1 4.0 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L

Affected packages

ecosystem package affected fixed in
pypi tensorflow
>=2.2.0, <2.2.1
>=2.3.0, <2.3.1
2.2.1
2.3.1
pypi tensorflow-cpu
>=2.2.0, <2.2.1
>=2.3.0, <2.3.1
2.2.1
2.3.1
pypi tensorflow-gpu
>=2.2.0, <2.2.1
>=2.3.0, <2.3.1
2.2.1
2.3.1
bitnami tensorflow
>=2.2.0, <2.2.1
>=2.3.0, <2.3.1
2.2.1
2.3.1

Affected products

  • tensorflow tensorflow

Classification

Weakness
  • CWE-119 Improper Restriction of Operations within the Bounds of a Memory Buffer
  • CWE-770 Allocation of Resources Without Limits or Throttling

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.