MediumVulnerability

CVE-2020-15210: tensorflow-lite memory corruption when a tensor is both input and output

Source: NVD(opens in a new tab)
Identifier: CVE-2020-15210
Published: September 25, 2020
Record updated: October 10, 2026

Affected: tensorflow < 1.15.4, fixed in 1.15.4, released September 24, 2020, 1 day before disclosure
tensorflow >= 2.0.0, < 2.0.3, fixed in 2.0.3, released September 24, 2020, 1 day before disclosure
tensorflow >= 2.1.0, < 2.1.2, fixed in 2.1.2, released September 24, 2020, 1 day before disclosure
and 12 more
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.7%

Summary

CVE-2020-15210 affects tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1. When a TFLite saved model uses the same tensor as both input and output of an operator, a segmentation fault or memory corruption can occur, depending on the operator.

Mitigation

We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. Users are recommended to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Details

Scores and exploitation

CVSS score: 6.5(medium)
Patch available: Yes
EPSS: 0.7%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 3, 2026.

Weaknesses and techniques

Weakness (CWE): CWE-20
CWE-787
Attack pattern (CAPEC): CAPEC-100

Affected

Packages: tensorflow@< 1.15.4 (fixed: 1.15.4)
tensorflow@>= 2.0.0, < 2.0.3 (fixed: 2.0.3)
tensorflow@>= 2.1.0, < 2.1.2 (fixed: 2.1.2)
tensorflow@= 2.2.0 (fixed: 2.2.1)
tensorflow@= 2.3.0 (fixed: 2.3.1)
tensorflow-cpu@< 1.15.4 (fixed: 1.15.4)
tensorflow-cpu@>= 2.0.0, < 2.0.3 (fixed: 2.0.3)
tensorflow-cpu@>= 2.1.0, < 2.1.2 (fixed: 2.1.2)
tensorflow-cpu@= 2.2.0 (fixed: 2.2.1)
tensorflow-gpu@< 1.15.4 (fixed: 1.15.4)
tensorflow-gpu@>= 2.0.0, < 2.0.3 (fixed: 2.0.3)
tensorflow-gpu@>= 2.1.0, < 2.1.2 (fixed: 2.1.2)
tensorflow-gpu@= 2.2.0 (fixed: 2.2.1)
tensorflow-cpu@= 2.3.0 (fixed: 2.3.1)
tensorflow-gpu@= 2.3.0 (fixed: 2.3.1)
Vendors: Google

Classification

Attack type: Other
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Integrity,
Availability
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2020-15210

First tracked: February 15, 2026 at 08:38 PM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 95% (self-assessed)

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

tensorflowPyPINo LLM dependency detected · 26 tracked dependents
tensorflow-gpuPyPINo LLM dependency detected · 2 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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