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GHSA-h6gw-r52c-724r

NULL Pointer Dereference and Access of Uninitialized Pointer in TensorFlow

9.3 CriticalFix available

GHSA-h6gw-r52c-724r is a critical vulnerability (CVSS 9.3) in tensorflow, published February 9, 2022. No source lists public exploit code. Fixed in tensorflow 2.5.3.

What does this mean?
Severity
9.3 CriticalCVSS 3.1, GitHub Advisories
Exploit chance
UnscoredNo EPSS score yet
Exploitation
None knownNot on CISA’s or ENISA’s lists
Fixed in
2.5.3tensorflow +17 more

Description

Impact

The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service (via dereferencing nullptrs or via CHECK-failures).

This follows after CVE-2021-41208 where these APIs were still vulnerable to multiple security issues.

Note: Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. Instead, please use the downstream TensorFlow Decision Forests project which is newer and supports more features.

These APIs are now deprecated in TensorFlow 2.8. We will remove TensorFlow's boosted trees APIs in subsequent releases.

Patches

We have patched the known issues in multiple GitHub commits.

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.

This should allow users to use existing boosted trees APIs for a while until they migrate to TensorFlow Decision Forests, while guaranteeing that known vulnerabilities are fixed.

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

These vulnerabilities have been reported by Yu Tian of Qihoo 360 AIVul Team and Faysal Hossain Shezan from University of Virginia. Some of the issues have been discovered internally after a careful audit of the APIs.

Attack profile

How easy it is to exploit

Attack vector
Local
Complexity
Low
Privileges needed
None
User interaction
None

What an attacker can affect

Confidentiality
High
Integrity
High
Availability
High

Affected packages

Affected packages
PackageAffectedFixed
tensorflow
PyPI
before 2.5.3
2.5.3
tensorflow
PyPI
2.6.0 up to 2.6.3
2.6.3
tensorflow
PyPI
2.7.0 to 2.7.0
2.7.1
tensorflow-cpu
PyPI
before 2.5.3
2.5.3
tensorflow-cpu
PyPI
2.6.0 up to 2.6.3
2.6.3
tensorflow-cpu
PyPI
2.7.0 to 2.7.0
2.7.1
tensorflow-gpu
PyPI
before 2.5.3
2.5.3
tensorflow-gpu
PyPI
2.6.0 up to 2.6.3
2.6.3
tensorflow-gpu
PyPI
2.7.0 to 2.7.0
2.7.1
tensorflow
PyPI
before 2.5.3, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 45 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.32.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.4
2.5.3
Show 8 moreShow less
Affected packages
PackageAffectedFixed
tensorflow
PyPI
2.6.0 up to 2.6.3, plus listed versions
2.6.12.6.2
2.6.3
tensorflow
PyPI
2.7.0 up to 2.7.1
2.7.1
tensorflow-cpu
PyPI
before 2.5.3, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
Show 18 moreShow less
2.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.42.4.02.4.12.4.22.4.32.4.42.5.02.5.12.5.2
2.5.3
tensorflow-cpu
PyPI
2.6.0 up to 2.6.3, plus listed versions
2.6.12.6.2
2.6.3
tensorflow-cpu
PyPI
2.7.0 up to 2.7.1
2.7.1
tensorflow-gpu
PyPI
before 2.5.3, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 45 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.32.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.4
2.5.3
tensorflow-gpu
PyPI
2.6.0 up to 2.6.3, plus listed versions
2.6.12.6.2
2.6.3
tensorflow-gpu
PyPI
2.7.0 up to 2.7.1
2.7.1

What each source reports

GitHub Advisories Score used9.3

NULL Pointer Dereference and Access of Uninitialized Pointer in TensorFlow

Impact

The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service (via dereferencing nullptrs or via CHECK-failures).

This follows after CVE-2021-41208 where these APIs were still vulnerable to multiple security issues.

Note: Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. Instead, please use the downstream TensorFlow Decision Forests project which is newer and supports more features.

These APIs are now deprecated in TensorFlow 2.8. We will remove TensorFlow's boosted trees APIs in subsequent releases.

Patches

We have patched the known issues in multiple GitHub commits.

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.

This should allow users to use existing boosted trees APIs for a while until they migrate to TensorFlow Decision Forests, while guaranteeing that known vulnerabilities are fixed.

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

These vulnerabilities have been reported by Yu Tian of Qihoo 360 AIVul Team and Faysal Hossain Shezan from University of Virginia. Some of the issues have been discovered internally after a careful audit of the APIs.

Record
GHSA-h6gw-r52c-724r
CVSS 3.1
9.3 CriticalCVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
Weakness
CWE-476, CWE-824
Updated
2026-11-02
OSV9.3

NULL Pointer Dereference and Access of Uninitialized Pointer in TensorFlow

Impact

The code for boosted trees in TensorFlow is still missing validation. This allows malicious users to read and write outside of bounds of heap allocated data as well as trigger denial of service (via dereferencing nullptrs or via CHECK-failures).

This follows after CVE-2021-41208 where these APIs were still vulnerable to multiple security issues.

Note: Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. Instead, please use the downstream TensorFlow Decision Forests project which is newer and supports more features.

These APIs are now deprecated in TensorFlow 2.8. We will remove TensorFlow's boosted trees APIs in subsequent releases.

Patches

We have patched the known issues in multiple GitHub commits.

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.

This should allow users to use existing boosted trees APIs for a while until they migrate to TensorFlow Decision Forests, while guaranteeing that known vulnerabilities are fixed.

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

These vulnerabilities have been reported by Yu Tian of Qihoo 360 AIVul Team and Faysal Hossain Shezan from University of Virginia. Some of the issues have been discovered internally after a careful audit of the APIs.

Record
GHSA-h6gw-r52c-724r
CVSS 3.1
9.3 CriticalCVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
Weakness
CWE-476, CWE-824
Updated
2026-11-02

History

History
RecordedChangedSources
first indexedGitHub Advisories, OSV

Dates are when Every CVE recorded each change. Upstream dates may differ.

References

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Left out here: 16 versions. The JSON record has them.

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