Incorrect Calculation Affecting tensorflow package, versions [0,]
Threat Intelligence
Snyk has a proof-of-concept or detailed explanation of how to exploit this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Test your applications- Snyk IDSNYK-PYTHON-TENSORFLOW-13052809
- published26 Sept 2025
- disclosed25 Sept 2025
- creditShaoyu Yang
Introduced: 25 Sep 2025
NewCVE-2025-55556 (opens in a new tab)How to fix?
There is no fixed version for tensorflow.
Overview
tensorflow is a machine learning framework.
Affected versions of this package are vulnerable to Incorrect Calculation via the Embedding operator that always outputs 0 without XLA when input_dim=1 is set. An attacker can cause the application to produce unpredictable or incorrect outputs by triggering compilation of the affected component.
PoC
import tensorflow as tf
tf.random.set_seed(42)
x = tf.constant([1])
# uncompiled model
class Model(tf.keras.Model):
def __init__(self):
super(Model, self).__init__()
self.embedding = tf.keras.layers.Embedding(1, 1)
def call(self, x):
output = self.embedding(x)
return output
m = Model()
output1 = m(x)
# compiled model
class Model(tf.keras.Model):
def __init__(self):
super(Model, self).__init__()
self.embedding = tf.keras.layers.Embedding(1, 1)
@tf.function(jit_compile=True)
def call(self, x):
output = self.embedding(x)
return output
m = Model()
output2 = m(x)
print(output1)
print(output2)