Categories
CVE-2021-29614

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.io.decode_raw`…

Summary

Vulnerability reported (CVE-2021-29614). Successful exploitation can lead to full system takeover. Exploitable via tf.io.decode_raw.

Original description

The description as published by the source.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.io.decode_raw produces incorrect results and crashes the Python interpreter when combining fixed_length and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the fixed_length value to the size of the type argument. The fixed_length argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the out_data pointer by fixed_length * sizeof(T) bytes whereas it only copied at most fixed_length bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing…

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-2021-29614 has been found.
Attackers can target a specific entry point like tf.io.decode_raw over the network to misuse the product.
Successful exploitation can lead to full system takeover.
What to do: apply the vendor's official patched release.
If unsure, ask your IT team or search "CVE-2021-29614" on the vendor's site.

CVE-2021-29614
Attack vector: local / no user interaction
Attack surface: tf.io.decode_raw / fixed_length / out_data
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

A vulnerability (unclassified) has been reported. Attackers reach the vulnerable code path via tf.io.decode_raw.

📍 Affected scope

Check the vendor advisory for the affected versions. Attack surface: tf.io.decode_raw / fixed_length / out_data.

🔥 Severity

Severity: ?. Successful exploitation can lead to full system takeover.

🔧 How to fix

Update to the patched release per vendor advisory.

🛡️ 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. Audit SBOM/dependencies to find affected services.

Response actions

Concrete steps and command examples for SOC/SRE teams to execute in order

  1. 1
    Identify exposure identify
    Audit SBOM/dependencies for affected components.

    資産台帳・SBOM・依存マニフェストで影響コンポーネントを特定する。

  2. 2
    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":"2.4.0"},{"fixed":"2.4.2"}]}]
PyPI tensorflow-cpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}]
PyPI tensorflow-gpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}]
Bitnami tensorflow
[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"2.1.4"},{"introduced":"2.2.0"},{"fixed":"2.2.3"},{"introduced":"2.3.0"},{"fixed":"2.3.3"},{"introduced":"2.4.0"},{"fixed":"2.4.2"}]}]

References

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