Vulnerability in tensorflow (CVE-2022-35985)
Summary
Vulnerability in tensorflow (CVE-2022-35985). Risk of unauthorized operations or information disclosure. Exploitable via LRNGrad. Mitigation: upgrade to 2.9.1 or later.
Original description
The description as published by the source.
TensorFlow is an open source platform for machine learning. If LRNGrad is given an output_image input tensor that is not 4-D, it results in a CHECK fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bd90b3efab4ec958b228cd7cfe9125be1c0cf255. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
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-2022-35985 has been found in tensorflow.
Attackers can target a specific entry point like LRNGrad over the network to misuse the product.
Risk of unauthorized operations or information disclosure.
What to do: upgrade tensorflow to 2.9.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2022-35985" on the vendor's site.
CVE-2022-35985 (tensorflow) — CWE-617
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Attack surface: LRNGrad / output_image / CHECK
Patched: 2.9.1 — 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-617) exists in tensorflow. Attackers reach the vulnerable code path via LRNGrad without authentication.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: LRNGrad / output_image / CHECK.
Severity: ?. Risk of unauthorized operations or information disclosure.
Update to 2.9.1.
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 2.9.1ステージング環境で 2.9.1 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 github.com
- package github.com
- web nvd.nist.gov
- web github.com