GHSA-h6q3-vv32-2cq5 is a high-severity (CVSS 7.1) CWE-120 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.
Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
- A successful exploit gives an attacker total control of the affected component, not partial access.
Exploitation and automatability from CISA’s SSVC triage for GHSA-h6q3-vv32-2cq5.
EPSS Exploitation Probability
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.
How urgent is this, really
GHSA-h6q3-vv32-2cq5 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 377,166 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflowReal-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 reference kernel of the CONV_3D_TRANSPOSE TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result.
Instead of data_ptr += num_channels; it should be data_ptr += output_num_channels; as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels.
An attacker can craft a model with a specific number of input channels in a way similar to the attached example script. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter (i.e. experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF is used).
import tensorflow as tf
model = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=(2, 2, 2, 1024), batch_size=1),
tf.keras.layers.Conv3DTranspose(
filters=8,
kernel_size=(2, 2, 2),
padding="same",
data_format="channels_last",
),
]
)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
interpreter = tf.lite.Interpreter(
model_content=tflite_model,
experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF,
)
interpreter.allocate_tensors()
interpreter.set_tensor(
interpreter.get_input_details()[0]["index"], tf.zeros(shape=[1, 2, 2, 2, 1024])
)
interpreter.invoke()
Patches
We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941.
The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, 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 Thibaut Goetghebuer-Planchon, Arm Ltd.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.8.4pip install --upgrade 'tensorflow==2.8.4' |
| 🐍PyPI | tensorflow | ≥ 2.9.0&&< 2.9.3 | 2.9.3pip install --upgrade 'tensorflow==2.9.3' |
| 🐍PyPI | tensorflow | ≥ 2.10.0&&< 2.10.1 | 2.10.1pip install --upgrade 'tensorflow==2.10.1' |
Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.
Detection & mitigation playbook
Open-source dependencyDetect
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 2.8.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-h6q3-vv32-2cq5 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 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-h6q3-vv32-2cq5 can be triaged on real exposure rather than presence alone.
Tailored to GHSA-h6q3-vv32-2cq5. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-h6q3-vv32-2cq5 in your dependencies?
O3 Security finds GHSA-h6q3-vv32-2cq5 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.