How severe is GHSA-ffjm-4qwc-7cmf?

GHSA-ffjm-4qwc-7cmf has a CVSS score of 7/10, rated HIGH. Immediate patching is strongly recommended.

Which packages are affected by GHSA-ffjm-4qwc-7cmf?

GHSA-ffjm-4qwc-7cmf affects the following packages: tensorflow (PyPI), tensorflow (PyPI), tensorflow (PyPI), tensorflow-cpu (PyPI), tensorflow-cpu (PyPI), tensorflow-cpu (PyPI), tensorflow-gpu (PyPI), tensorflow-gpu (PyPI), and 1 more. Ecosystems affected: PyPI.

How do I fix GHSA-ffjm-4qwc-7cmf?

Update tensorflow to 2.7.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-ffjm-4qwc-7cmf is resolved across your whole dependency graph.

How do I detect GHSA-ffjm-4qwc-7cmf in my PyPI dependencies?

Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

How do I mitigate GHSA-ffjm-4qwc-7cmf if there is no patch (or I can't update yet)?

Constrain what reaches the vulnerable code: limit the size and shape of untrusted input, isolate the affected component in a sandboxed or least-privileged process, and enable the platform's memory-safety mitigations (ASLR, stack protector, hardened allocator) so an out-of-bounds access is more likely to fail closed than to be exploitable.

Is GHSA-ffjm-4qwc-7cmf actively exploited in the wild?

No public exploit code has been indexed for GHSA-ffjm-4qwc-7cmf yet. This does not mean the vulnerability cannot be exploited — absence of public exploits does not imply safety. Apply the recommended fix and use O3 Security to monitor your exposure.

What is the EPSS score for GHSA-ffjm-4qwc-7cmf?

GHSA-ffjm-4qwc-7cmf has an EPSS (Exploit Prediction Scoring System) score of 0.6%, placing it in the 47th percentile of all CVEs. EPSS is maintained by FIRST.org and estimates the probability that a vulnerability will be exploited in the wild within the next 30 days. This score indicates relatively lower exploitation probability, though the CVSS severity should still guide your patching priority.

What type of vulnerability is GHSA-ffjm-4qwc-7cmf?

GHSA-ffjm-4qwc-7cmf is classified as Out-of-bounds Write (CWE-787). This weakness type describe the underlying flaw category, which helps determine the potential impact and the right class of mitigation.

When was GHSA-ffjm-4qwc-7cmf published, and has it been updated?

GHSA-ffjm-4qwc-7cmf was published on September 16, 2022 and was last updated on July 13, 2026. Advisory data evolves as severity scores, affected ranges, and exploit intelligence are revised — always check the latest version of the advisory before acting.

🐍

🐍 PyPI

Not in CISA KEV

HIGH severity

TensorFlow vulnerable to OOB write in `scatter_nd` in TF LiteGHSA-ffjm-4qwc-7cmf

HIGHFix: tensorflow/tensorflow@b4d4b4c

GHSA-ffjm-4qwc-7cmf is a high-severity (CVSS 7) Out-of-bounds Write vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.

Also known asBIT-tensorflow-2022-35939CVE-2022-35939PYSEC-2026-3171PYSEC-2026-3319PYSEC-2026-989

Published

Sep 16, 2022

Updated

Jul 13, 2026

Affected

9 pkgs

Patched

9 / 9

Exploits

None indexed

Exploitation data as of Nov 4, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for GHSA-ffjm-4qwc-7cmf.

EPSS Exploitation Probability

via FIRST.org ↗

0.6%probability of exploitation in next 30 days

Lower Risk0.00%

Lower risk than most CVEs47th percentile — riskier than 47% of all scored CVEsHighest risk

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

9 pkgs affected

🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 more

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Impact

The ScatterNd function takes an input argument that determines the indices of of the output tensor. An input index greater than the output tensor or less than zero will either write content at the wrong index or trigger a crash.

Patches

We have patched the issue in GitHub commit b4d4b4cb019bd7240a52daa4ba61e3cc814f0384.

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.

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 has been reported by Hui Peng from Baidu Security.

Affected Packages

9 total 9 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`tensorflow`	all versions	2.7.2`pip install --upgrade 'tensorflow==2.7.2'`
🐍PyPI	`tensorflow`	≥ 2.8.0&&< 2.8.1	2.8.1`pip install --upgrade 'tensorflow==2.8.1'`
🐍PyPI	`tensorflow`	≥ 2.9.0&&< 2.9.1	2.9.1`pip install --upgrade 'tensorflow==2.9.1'`
🐍PyPI	`tensorflow-cpu`	all versions	2.7.2`pip install --upgrade 'tensorflow-cpu==2.7.2'`
🐍PyPI	`tensorflow-cpu`	≥ 2.8.0&&< 2.8.1	2.8.1`pip install --upgrade 'tensorflow-cpu==2.8.1'`
🐍PyPI	`tensorflow-cpu`	≥ 2.9.0&&< 2.9.1	2.9.1`pip install --upgrade 'tensorflow-cpu==2.9.1'`

Affected Products

1 product · 7 configurations

Application

tensorflowgoogle

≥ 2.9.0 && < 2.9.1

1 version

2.10

Detection & mitigation playbook

Open-source dependency

Detect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update tensorflow to 2.7.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-ffjm-4qwc-7cmf is resolved across your whole dependency graph.
Workarounds
Constrain what reaches the vulnerable code: limit the size and shape of untrusted input, isolate the affected component in a sandboxed or least-privileged process, and enable the platform's memory-safety mitigations (ASLR, stack protector, hardened allocator) so an out-of-bounds access is more likely to fail closed than to be exploitable.

Frequently Asked Questions

### Impact The [`ScatterNd`](https://github.com/tensorflow/tensorflow/blob/266558ac4c1f361e9a178ee9d3f0ce2e648ae499/tensorflow/lite/kernels/internal/reference/reference_ops.h#L659-L698) function takes an input argument that determines the indices of of the output tensor. An input index greater than the output tensor or less than zero will either write content at the wrong index or trigger a crash. ### Patches We have patched the issue in GitHub commit [b4d4b4cb019bd7240a52daa4ba61e3cc814f0384](https://github.com/tensorflow/tensorflow/commit/b4d4b4cb019bd7240a52daa4ba61e3cc814f0384). The fix

O3 Security · Impact-Aware SCA

Is GHSA-ffjm-4qwc-7cmf in your dependencies?

Find it across PyPI, including transitive dependencies.

Scan my dependencies How O3 SCA works

CVSS Score

7/10CVSS v3 base

Vector breakdown

Attack Vector

Network

Attack Complexity

High

Privileges Required

None

User Interaction

None

Scope

Unchanged

Confidentiality

Low

Integrity

Low

Availability

High

Raw vector

CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:H

CWE Classification

CWE-787Out-of-bounds Write

Weaponization

No Nuclei template, Metasploit module or public proof-of-concept found.

OpenSSF Scorecard

Package health score

tensorflow

7.8/10

Maintained10/10

Code-Review7/10

Dependency-Update-Tool10/10

Branch-ProtectionN/A

Vulnerabilities0/10

Assessed Nov 4, 2026

Timeline

Disclosed

Sep 16, 2022

Last updated

Jul 13, 2026

Identifiers

GHSA-ffjm-4qwc-7cmf

BIT-tensorflow-2022-35939

PYSEC-2026-3171

PYSEC-2026-3319

References

Advisorynvd.nist.gov · CVE 2022 35939 Webgithub.com · GHSA ffjm 4qwc 7cmf Webgithub.com · b4d4b4cb019bd7240a52daa4ba61e3cc814f0384 Webgithub.com · reference ops.h Webgithub.com · v2.10.0 Packagegithub.com · tensorflow

Related vulnerabilities

This CVE is high severity; 7 of 8 other CVEs affecting tensorflow are rated as high or higher (top: high).

CVE-2023-33976CVE-2023-25661CVE-2023-25670CVE-2023-25665CVE-2023-27579CVE-2023-25673CVE-2023-25663CVE-2023-25658

Provenance

Sources

OSV.devNVDFIRST.org (EPSS)

Last verifiedJul 13, 2026