Vulnerability in tensorflow (CVE-2023-25801)
Summary
Vulnerability in tensorflow (CVE-2023-25801). Data can be tampered with by attackers. Exploitable via nn_ops.fractional_avg_pool_v2. Mitigation: upgrade to 2.11.1 or later.
Original description
The description as published by the source.
Impact
nn_ops.fractional_avg_pool_v2 and nn_ops.fractional_max_pool_v2 require the first and fourth elements of their parameter pooling_ratio to be equal to 1.0, as pooling on batch and channel dimensions is not supported.
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import nn_ops
try:
arg_0_tensor = tf.random.uniform([3, 30, 50, 3], dtype=tf.float64)
arg_0 = tf.identity(arg_0_tensor)
arg_1_0 = 2
arg_1_1 = 3
arg_1_2 = 1
arg_1_3 = 1
arg_1 = [arg_1_0,arg_1_1,arg_1_2,arg_1_3,]
arg_2 = True
arg_3 = True
seed = 341261001
out = nn_ops.fractional_avg_pool_v2(arg_0,arg_1,arg_2,arg_3,seed=seed,)
except Exception as e:
print("Error:"+str(e))
Patches
We have patched the issue in GitHub commit ee50d1e00f81f62a4517453f721c634bbb478307.
The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.
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 was reported by dmc1778, of nimashiri2012@gmail.com.
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-2023-25801 has been found in tensorflow.
Attackers can target a specific entry point like nn_ops.fractional_avg_pool_v2 over the network to misuse the product.
Data can be tampered with by attackers.
What to do: upgrade tensorflow to 2.11.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2023-25801" on the vendor's site.
CVE-2023-25801 (tensorflow) — CWE-415
Attack vector: local / unauthenticated / no user interaction
Attack surface: nn_ops.fractional_avg_pool_v2 / nn_ops.fractional_max_pool_v2 / pooling_ratio
Patched: 2.11.1 — 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-415) exists in tensorflow. Attackers reach the vulnerable code path via nn_ops.fractional_avg_pool_v2 without authentication.
Check the vendor advisory for the affected versions of tensorflow. Attack surface: nn_ops.fractional_avg_pool_v2 / nn_ops.fractional_max_pool_v2 / pooling_ratio.
Severity: ?. Data can be tampered with by attackers.
Update to 2.11.1.
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 2.11.1ステージング環境で 2.11.1 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