HighVulnerability

CVE-2018-8825: Google TensorFlow buffer overflow allowing local arbitrary code execution

Source: NVD(opens in a new tab)
Identifier: CVE-2018-8825
Published: April 23, 2019
Record updated: October 10, 2026

Affected: tensorflow >= 1.5.0, < 1.7.1
tensorflow-gpu >= 1.5.0, < 1.7.1
Fixed in: 1.7.1
tensorflow 1.7.1 released May 8, 2018, 351 days before disclosure
tensorflow-gpu 1.7.1 released May 8, 2018, 351 days before disclosure
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.7%

Summary

CVE-2018-8825 affects Google TensorFlow 1.7 and below. The source describes a buffer overflow that allows an attacker to execute arbitrary code locally. The weakness is classified as CWE-119, Improper Restriction of Operations within the Bounds of a Memory Buffer.

Mitigation

The source does not state a fix yet. Check the original advisory for updates.

Details

Scores and exploitation

CVSS score: 6.8
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-119
Attack pattern (CAPEC): CAPEC-100

Affected

Packages: tensorflow@>= 1.5.0, < 1.7.1 (fixed: 1.7.1)
tensorflow-gpu@>= 1.5.0, < 1.7.1 (fixed: 1.7.1)
Vendors: Google

Classification

Attack type: Other
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Integrity,
Confidentiality,
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-2018-8825

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

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

Classifier confidence: 90% (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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