Vulnerability in tensorflow (CVE-2022-23559)
Summary
Vulnerability in tensorflow (CVE-2022-23559). Successful exploitation can lead to full system takeover. Exploitable via embedding_size. Mitigation: upgrade to 2.7.1 or later.
Original description
The description as published by the source.
Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both embedding_size and lookup_size are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write. Users are advised to upgrade to a patched version.
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-2022-23559 has been found in tensorflow.
Attackers can target a specific entry point like embedding_size over the network to misuse the product.
Successful exploitation can lead to full system takeover.
What to do: upgrade tensorflow to 2.7.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2022-23559" on the vendor's site.
CVE-2022-23559 (tensorflow) — CWE-190
Attack vector: remote (network-reachable) / no user interaction
Attack surface: embedding_size / lookup_size
Patched: 2.7.1 — 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-190) exists in tensorflow. Attackers reach the vulnerable code path via embedding_size.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: embedding_size / lookup_size.
Severity: ?. Successful exploitation can lead to full system takeover.
Update to 2.7.1.
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 2.7.1ステージング環境で 2.7.1 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 github.com
- advisory github.com
- advisory github.com
- advisory github.com
- web nvd.nist.gov
- web github.com
- web github.com
- web github.com