CVE-2022-36027Medium· 5.9▾ SunlitTensorFlow segfault TFLite converter on per-channel quantized transposed convolutions
▾ Sunlit zone — Low / medium · no exploitation signal
impact 32.5 · likelihood 0.2 · exploitation 0
Need a working PoC? Pro members can cast a request and our team develops one — it lands right here.
Exploit-prediction probability, daily snapshots since Jul 8.
Disclosure to exploitation, from the record and what we observed since indexing it.
Disclosed via OSV
Last analysed / modified upstream
0.6%
0.6% → 0.8%
When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process.
import tensorflow as tf
class QuantConv2DTransposed(tf.keras.layers.Layer):
def build(self, input_shape):
self.kernel = self.add_weight("kernel", [3, 3, input_shape[-1], 24])
def call(self, inputs):
filters = tf.quantization.fake_quant_with_min_max_vars_per_channel(
self.kernel, -3.0 * tf.ones([24]), 3.0 * tf.ones([24]), narrow_range=True
)
filters = tf.transpose(filters, (0, 1, 3, 2))
return tf.nn.conv2d_transpose(inputs, filters, [*inputs.shape[:-1], 24], 1)
inp = tf.keras.Input(shape=(6, 8, 48), batch_size=1)
x = tf.quantization.fake_quant_with_min_max_vars(inp, -3.0, 3.0, narrow_range=True)
x = QuantConv2DTransposed()(x)
x = tf.quantization.fake_quant_with_min_max_vars(x, -3.0, 3.0, narrow_range=True)
model = tf.keras.Model(inp, x)
model.save("/tmp/testing")
converter = tf.lite.TFLiteConverter.from_saved_model("/tmp/testing")
converter.optimizations = [tf.lite.Optimize.DEFAULT]
# terminated by signal SIGSEGV (Address boundary error)
tflite_model = converter.convert()
We have patched the issue in GitHub commit aa0b852a4588cea4d36b74feb05d93055540b450.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Lukas Geiger via Github issue.
tensorflow < 2.7.2tensorflow >= 2.8.0, < 2.8.1tensorflow >= 2.9.0, < 2.9.1tensorflow-cpu < 2.7.2tensorflow-cpu >= 2.8.0, < 2.8.1tensorflow-cpu >= 2.9.0, < 2.9.1tensorflow-gpu < 2.7.2tensorflow-gpu >= 2.8.0, < 2.8.1tensorflow-gpu >= 2.9.0, < 2.9.1Upgrade to a patched release:
tensorflow 2.7.2tensorflow 2.8.1tensorflow 2.9.1tensorflow-cpu 2.7.2tensorflow-cpu 2.8.1tensorflow-cpu 2.9.1tensorflow-gpu 2.7.2tensorflow-gpu 2.8.1tensorflow-gpu 2.9.1Connected by shared product, vendor, weakness, or advisory.
CVE-2022-35940Medium· 5.9TensorFlow vulnerable to Int overflow in `RaggedRangeOp`
CVE-2022-35959Medium· 5.9TensorFlow vulnerable to `CHECK` failures in `AvgPool3DGrad`
CVE-2022-35986Medium· 5.9TensorFlow vulnerable to segfault in `RaggedBincount`
CVE-2022-36017Medium· 5.9TensorFlow vulnerable to segfault in `Requantize`
CVE-2022-35993Medium· 5.9TensorFlow vulnerable to `CHECK` fail in `SetSize`
CVE-2022-35987Medium· 5.9TensorFlow vulnerable to `CHECK` fail in `DenseBincount`