How severe is GHSA-gw97-ff7c-9v96?

GHSA-gw97-ff7c-9v96 has a CVSS score of 9.8/10, rated CRITICAL. Immediate patching is strongly recommended.

Which packages are affected by GHSA-gw97-ff7c-9v96?

GHSA-gw97-ff7c-9v96 affects the following packages: tensorflow (PyPI), tensorflow-cpu (PyPI), tensorflow-gpu (PyPI). Ecosystems affected: PyPI.

How do I fix GHSA-gw97-ff7c-9v96?

Update tensorflow to 2.11.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-gw97-ff7c-9v96 is resolved across your whole dependency graph.

How do I detect GHSA-gw97-ff7c-9v96 in my PyPI dependencies?

Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

How do I mitigate GHSA-gw97-ff7c-9v96 if there is no patch (or I can't update yet)?

If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

How does O3 Security protect against GHSA-gw97-ff7c-9v96?

O3 pinpoints whether GHSA-gw97-ff7c-9v96 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Is GHSA-gw97-ff7c-9v96 actively exploited in the wild?

Yes. There are 1 known exploit references for GHSA-gw97-ff7c-9v96, including 1 in-the-wild exploitation observed on GitHub and other sources. Treat this as actively exploitable and prioritize patching immediately. All exploit code should only be run in an isolated sandbox environment for research or authorized testing — never against production systems without explicit written authorization.

What is the EPSS score for GHSA-gw97-ff7c-9v96?

GHSA-gw97-ff7c-9v96 has an EPSS (Exploit Prediction Scoring System) score of 0.8%, placing it in the 53th 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-gw97-ff7c-9v96?

GHSA-gw97-ff7c-9v96 is classified as CWE-122 (CWE-122), Out-of-bounds Read (CWE-125). These weakness types describe the underlying flaw category, which helps determine the potential impact and the right class of mitigation.

When was GHSA-gw97-ff7c-9v96 published, and has it been updated?

GHSA-gw97-ff7c-9v96 was published on March 24, 2023 and was last updated on December 6, 2023. Advisory data evolves as severity scores, affected ranges, and exploit intelligence are revised — always check the latest version of the advisory before acting.

🐍 PyPI

GHSA-gw97-ff7c-9v96

CRITICAL

TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation

Also known asBIT-tensorflow-2023-25668CVE-2023-25668

Published

Mar 24, 2023

Updated

Dec 6, 2023

Affected

3 pkgs

Patched

3 / 3

Exploits

1 known

EPSS Exploitation Probability

via FIRST.org ↗

0.8%probability of exploitation in next 30 days

Lower Risk53th percentile-0.63%

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

Blast Radius

3 pkgs affected

🐍tensorflow🐍tensorflow-cpu🐍tensorflow-gpu

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

Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too.

import tensorflow as tf
@tf.function
def test():
    tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5],
    								   input_min=[1.0],
    								   input_max=[10.0],
    								   signed_input=True,
    								   num_bits=1,
    								   range_given=True,
    								   round_mode='HALF_TO_EVEN',
    								   narrow_range=True,
    								   axis=0x7fffffff)
test()

Patches

We have patched the issue in GitHub commit 7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb.

The fix will be included in TensorFlow 2.12.0. 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.

Affected Packages

3 total 3 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`tensorflow`	all versions	2.11.1
🐍PyPI	`tensorflow-cpu`	all versions	2.11.1
🐍PyPI	`tensorflow-gpu`	all versions	2.11.1

Exploits & PoCs

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

TensorFlow is an open source platform for machine learning. Attackers us…

github.comNVDMar 2023

Detection & mitigation playbook

Open-source dependency

Detect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update tensorflow to 2.11.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-gw97-ff7c-9v96 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-gw97-ff7c-9v96 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-gw97-ff7c-9v96. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too. ```python import tensorflow as tf @tf.function def test(): tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5], input_min=[1.0], input_max=[10.0], signed_input=True, num_bits=1, range_given=True,

O3 Security · Impact-Aware SCA

Is GHSA-gw97-ff7c-9v96 in your dependencies?

O3 detects GHSA-gw97-ff7c-9v96 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.

Scan my dependencies How O3 SCA works