Vulnerability in tensorflow (CVE-2023-25672)
Summary
Vulnerability in tensorflow (CVE-2023-25672). Risk of unauthorized operations or information disclosure. Exploitable via tf.raw_ops.LookupTableImportV2. Mitigation: upgrade to 2.11.1 or later.
Original description
The description as published by the source.
TensorFlow is an open source platform for machine learning. The function tf.raw_ops.LookupTableImportV2 cannot handle scalars in the values parameter and gives an NPE. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.
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-2023-25672 has been found in tensorflow.
Attackers can target a specific entry point like tf.raw_ops.LookupTableImportV2 over the network to misuse the product.
Risk of unauthorized operations or information disclosure.
What to do: upgrade tensorflow to 2.11.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2023-25672" on the vendor's site.
CVE-2023-25672 (tensorflow) — CWE-476
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Attack surface: tf.raw_ops.LookupTableImportV2 / values
Patched: 2.11.1 — 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.
A vulnerability (CWE-476) exists in tensorflow. Attackers reach the vulnerable code path via tf.raw_ops.LookupTableImportV2 without authentication.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: tf.raw_ops.LookupTableImportV2 / values.
Severity: ?. Risk of unauthorized operations or information disclosure.
Update to 2.11.1.
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 2.11.1ステージング環境で 2.11.1 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。
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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
- advisory github.com
- package github.com
- patch github.com