Out-of-Bounds Read in tensorflow (CVE-2021-41210)
Summary
Vulnerability in tensorflow (CVE-2021-41210). Confidential information can be exposed externally. Exploitable via SparseCountSparseOutput. Mitigation: upgrade to 2.4.4 or later.
Original description
The description as published by the source.
TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for SparseCountSparseOutput can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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-2021-41210 has been found in tensorflow.
Attackers can target a specific entry point like SparseCountSparseOutput over the network to misuse the product.
Confidential information can be exposed externally.
What to do: upgrade tensorflow to 2.4.4 or later.
If unsure, ask your IT team or search "tensorflow CVE-2021-41210" on the vendor's site.
CVE-2021-41210 (tensorflow) — CWE-125
Attack vector: local / no user interaction
Attack surface: SparseCountSparseOutput
Patched: 2.4.4 — apply immediately
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.
Out-of-bounds read (CWE-125) exists in tensorflow. Attackers reach the vulnerable code path via SparseCountSparseOutput.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: SparseCountSparseOutput.
Severity: ?. Confidential information can be exposed externally.
Update to 2.4.4.
Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.
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
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1Identify 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。 -
2Apply patch patch
Upgrade tensorflow to 2.4.4ステージング環境で 2.4.4 に上げて回帰テスト → 本番反映。回帰テストはアプリの主要ハッピーパスを含めること。
-
3Post-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
References
- advisory github.com
- advisory nvd.nist.gov
- package github.com
- patch github.com
- web github.com
- web github.com
- web github.com