CriticalVulnerability

CVE-2020-15202: Tensorflow Shard API integer truncation causes memory corruption

Source: NVD(opens in a new tab)
Identifier: CVE-2020-15202
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: 1.3%

Summary

TensorFlow versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 have an integer truncation flaw in the `Shard` API. Several call sites pass a lambda taking `int` or `int32` arguments where the API expects `int64`, so large parallel workloads truncate the values. Depending on how the lambda uses them, this can cause segfaults, out-of-bounds reads and writes, stack overflows, or data corruption.

Mitigation

Fixed in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1, via commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575.

Details

Scores and exploitation

CVSS score: 9(critical)
Patch available: Yes
EPSS: 1.3%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 4, 2026.

Weaknesses and techniques

Weakness (CWE): CWE-197
CWE-754

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-cpu@= 2.3.0 (fixed: 2.3.1)
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-gpu@= 2.3.0 (fixed: 2.3.1)
Vendors: Google

Classification

Attack type: Other
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Confidentiality,
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-2020-15202

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

Related items