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CVE-2020-5215

Vulnerability in TensorFlow (CVE-2020-5215)

Summary

Vulnerability in TensorFlow (CVE-2020-5215). Risk of unauthorized operations or information disclosure.

Original description

The description as published by the source.

Impact

Converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode.

This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value.

Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions.

This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled.

Patches

We have patched the vulnerability in GitHub commit 5ac1b9.

We are additionally releasing TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched.

TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected.

We encourage users to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.

For more information

Please consult SECURITY.md for more information regarding the security model and how to contact us with issues and questions.

Source: OSV.dev (Open Source Vulnerabilities) NVD GitHub Advisory OSV

Auto-generated summary

Assembled by SecPulse from the source data (CVSS, CWE, affected versions) using templates — no AI involved. Check the original description and references below for exact details.

A vulnerability tracked as CVE-2020-5215 has been found in TensorFlow.
Risk of unauthorized operations or information disclosure.
What to do: apply the vendor's official patched release.
If unsure, ask your IT team or search "TensorFlow CVE-2020-5215" on the vendor's site.

CVE-2020-5215 (TensorFlow)
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Plan: 1) Audit SBOM/dependencies, 2) Stage→prod upgrade, 3) Add WAF/proxy monitoring on affected endpoints, 4) Hunt IOCs in logs.
Refs: see the GHSA / vendor advisory / patched release linked on this page.

❓ What is the problem

A vulnerability (unclassified) exists in TensorFlow.

📍 Affected scope

Check the vendor advisory for the affected versions of TensorFlow.

🔥 Severity

Severity: ?. Risk of unauthorized operations or information disclosure.

🔧 How to fix

Update to the patched release per vendor advisory.

🛡️ Workaround

Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.

🔍 Detection

Search webserver/proxy logs for unusual request patterns matching this CVE's known IOCs. Run grep -r 'TensorFlow' . against your dependency files (package-lock.json, requirements.txt, go.sum) to find affected services.

Response actions

Concrete steps and command examples for SOC/SRE teams to execute in order

  1. 1
    Identify exposure identify 
    grep -r 'TensorFlow' . | grep -v node_modules

    リポジトリと本番環境の依存ファイル (package-lock.json / requirements.txt / go.sum / Gemfile.lock 等) で TensorFlow を grep し、稼働しているサービス・バージョンを把握する。

  2. 2
    Post-deployment verification verify 
    Confirm patched version is live in production

    パッチ適用後、ステージングで PoC または同等の悪用パターンを再現して脆弱性が閉じたことを確認。本番ではアクセスログ・WAF でアラート再発が無いか継続監視。

Affected packages

PyPI tensorflow
[{"type":"ECOSYSTEM","events":[{"introduced":"2.0.0"},{"fixed":"2.0.1"}]}]
PyPI tensorflow-cpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.0.0"},{"fixed":"2.0.1"}]}]
PyPI tensorflow-gpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.0.0"},{"fixed":"2.0.1"}]}]

References

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