Out-of-Bounds Read in tensorflow (CVE-2022-23560)
Summary
Vulnerability in tensorflow (CVE-2022-23560). Successful exploitation can lead to full system takeover. 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 allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors. The fix is included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. Users are advised to upgrade as soon as possible.
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-2022-23560 has been found in tensorflow.
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-2022-23560" on the vendor's site.
CVE-2022-23560 (tensorflow) — CWE-125
Attack vector: remote (network-reachable) / no user interaction
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.
Out-of-bounds read (CWE-125) exists in tensorflow.
Check the vendor advisory for the affected versions of tensorflow.
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
-
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。
-
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
- patch github.com
- web github.com
- web github.com
- web github.com
- web github.com