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55 lines
2.2 KiB
Markdown
55 lines
2.2 KiB
Markdown
## TFSA-2020-003: Denial of service from TFLite implementation of segment sum
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### CVE Number
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CVE-2020-15213
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### Impact
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In TensorFlow Lite models using segment sum can trigger a denial of service by
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causing an out of memory allocation in the implementation of segment sum. Since
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code uses the last element of the tensor holding them to determine the
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dimensionality of output tensor, attackers can use a very large value to trigger
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a [large
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allocation](https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L48):
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```cc
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if (segment_id_size > 0) {
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max_index = segment_ids->data.i32[segment_id_size - 1];
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}
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TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(data));
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output_shape->data[0] = max_index + 1;
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for (int i = 1; i < data_rank; ++i) {
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output_shape->data[i] = data->dims->data[i];
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}
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return context->ResizeTensor(context, output, output_shape);
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```
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### Vulnerable Versions
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TensorFlow 2.2.0, 2.3.0.
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### Patches
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We have patched the issue in
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[204945b](https://github.com/tensorflow/tensorflow/commit/204945b) and will
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release patch releases for all affected versions.
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We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.
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### Workarounds
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A potential workaround would be to add a custom `Verifier` to limit the maximum
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value in the segment ids tensor. This only handles the case when the segment ids
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are stored statically in the model, but a similar validation could be done if
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the segment ids are generated at runtime, between inference steps.
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However, if the segment ids are generated as outputs of a tensor during
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inference steps, then there are no possible workaround and users are advised to
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upgrade to patched code.
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### For more information
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Please consult [our security
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guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for
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more information regarding the security model and how to contact us with issues
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and questions.
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### Attribution
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This vulnerability has been discovered through a variant analysis of [a
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vulnerability reported by members of the Aivul Team from Qihoo
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360](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2020-002.md).
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