Vulnerability in tensorflow (CVE-2022-23563)
Summary
Vulnerability in tensorflow (CVE-2022-23563). Confidential information can be exposed externally. Exploitable via tempfile.mktemp. Mitigation: upgrade to 2.7.1 or later.
Original description
The description as published by the source.
Impact
In multiple places, TensorFlow uses tempfile.mktemp to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in mktemp and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness).
In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the mktemp function usage.
Patches
We have patched the issue in several commits, replacing mktemp with the safer mkstemp/mkdtemp functions, according to the usage pattern.
The fix will be 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.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported on huntr.dev for one scenario and discovered via variant analysis on other instances.
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-2022-23563 has been found in tensorflow.
Attackers can target a specific entry point like tempfile.mktemp over the network to misuse the product.
Confidential information can be exposed externally.
What to do: upgrade tensorflow to 2.7.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2022-23563" on the vendor's site.
CVE-2022-23563 (tensorflow) — CWE-367
Attack vector: local / no user interaction
Attack surface: tempfile.mktemp / mktemp / mkstemp / mkdtemp
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-367) exists in tensorflow. Attackers reach the vulnerable code path via tempfile.mktemp.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: tempfile.mktemp / mktemp / mkstemp / mkdtemp.
Severity: ?. Confidential information can be exposed externally.
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。
-
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 nvd.nist.gov
- package github.com
- web github.com
- web github.com
- web github.com