Categories
CVE-2022-41883

Out-of-Bounds Read in tensorflow (CVE-2022-41883)

Summary

Vulnerability in tensorflow (CVE-2022-41883). Risk of unauthorized operations or information disclosure. Exploitable via tf.raw_ops.DynamicStitch. Mitigation: upgrade to 2.10.1 or later.

Original description

The description as published by the source.

Impact

tf.raw_ops.DynamicStitch specifies input sizes when it is registered.

REGISTER_OP("DynamicStitch")
    .Input("indices: N * int32")
    .Input("data: N * T")
    .Output("merged: T")
    .Attr("N : int >= 1")
    .Attr("T : type")
    .SetShapeFn(DynamicStitchShapeFunction);

When it receives a differing number of inputs, such as when it is called with an indices size 1 and a data size 2, it will crash.

import tensorflow as tf

# indices = 1*[tf.random.uniform([1,2], dtype=tf.dtypes.int32, maxval=100)]
indices = [tf.constant([[0, 1]]),]

# data = 2*[tf.random.uniform([1,2], dtype=tf.dtypes.float32, maxval=100)]
data = [tf.constant([[5, 6]]), tf.constant([[7, 8]])]

tf.raw_ops.DynamicStitch(
    indices=indices, 
    data=data)

Patches

We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 as this is also affected.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with is…

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-2022-41883 has been found in tensorflow.
Attackers can target a specific entry point like tf.raw_ops.DynamicStitch over the network to misuse the product.
Risk of unauthorized operations or information disclosure.
What to do: upgrade tensorflow to 2.10.1 or later.
If unsure, ask your IT team or search "tensorflow CVE-2022-41883" on the vendor's site.

CVE-2022-41883 (tensorflow) — CWE-125
Attack vector: remote (network-reachable)
Attack surface: tf.raw_ops.DynamicStitch / indices / data
Patched: 2.10.1 — 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.

❓ What is the problem

Out-of-bounds read (CWE-125) exists in tensorflow. Attackers reach the vulnerable code path via tf.raw_ops.DynamicStitch.

📍 Affected scope

Check the vendor advisory for the affected versions of tensorflow. Attack surface: tf.raw_ops.DynamicStitch / indices / data.

🔥 Severity

Severity: ?. Risk of unauthorized operations or information disclosure.

🔧 How to fix

Update to 2.10.1.

🛡️ Workaround

Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.

🔍 Detection

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

  1. 1
    Identify 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。

  2. 2
    Apply patch patch
    Upgrade tensorflow to 2.10.1

    ステージング環境で 2.10.1 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。

  3. 3
    Post-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

PyPI tensorflow
[{"type":"ECOSYSTEM","events":[{"introduced":"2.10.0"},{"fixed":"2.10.1"}]}]
PyPI tensorflow-cpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.10.0"},{"fixed":"2.10.1"}]}]
PyPI tensorflow-gpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.10.0"},{"fixed":"2.10.1"}]}]

References

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