Code Injection in tensorflow (CVE-2022-29216)
Summary
Code injection in tensorflow (CVE-2022-29216). Successful exploitation can lead to full system takeover. Exploitable via saved_model_cli. Mitigation: upgrade to 2.8.1 or later.
Original description
The description as published by the source.
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, TensorFlow's saved_model_cli tool is vulnerable to a code injection. This can be used to open a reverse shell. This code path was maintained for compatibility reasons as the maintainers had several test cases where numpy expressions were used as arguments. However, given that the tool is always run manually, the impact of this is still not severe. The maintainers have now removed the safe=False argument, so all parsing is done without calling eval. The patch is available in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4.
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-2022-29216 has been found in tensorflow.
Attackers can target a specific entry point like saved_model_cli over the network to misuse the product.
Successful exploitation can lead to full system takeover.
What to do: upgrade tensorflow to 2.8.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2022-29216" on the vendor's site.
CVE-2022-29216 (tensorflow) — CWE-94
Attack vector: local / no user interaction
Attack surface: saved_model_cli / eval
Patched: 2.8.1 — 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.
Arbitrary code execution (CWE-94) exists in tensorflow. Attackers reach the vulnerable code path via saved_model_cli.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: saved_model_cli / eval.
Severity: ?. Successful exploitation can lead to full system takeover.
Update to 2.8.1.
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 2.8.1ステージング環境で 2.8.1 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
- package github.com
- patch github.com
- patch github.com
- web github.com
- web github.com
- web github.com
- web github.com
- web github.com