In TensorFlow before version 2.6.0 an attacker can cause denial of service in applications serving models using tf.raw_ops.NonMaxSuppressionV5 by triggering a division by 0. The implementation uses a user controlled argument to resize a std::vector. However, as std::vector::resize takes the size argument as a size_t and output_size is an int, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in CombinedNonMaxSuppression and commit b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58.
In TensorFlow before version 2.6.0 an attacker can cause denial of service in applications serving models using tf.raw_ops.NonMaxSuppressionV5 by triggering a division by 0. The implementation uses a user controlled argument to resize a std::vector. However, as std::vector::resize takes the size argument as a size_t and output_size is an int, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in CombinedNonMaxSuppression and commit b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58
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