CVE-2021-29514

Heap out of bounds write in RaggedBinCount

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Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

If the splits argument of RaggedBincount does not specify a valid SparseTensor, then an attacker can trigger a heap buffer overflow:

import tensorflow as tf
tf.raw_ops.RaggedBincount(splits=[7,8], values= [5, 16, 51, 76, 29, 27, 54, 95],\
                          size= 59, weights= [0, 0, 0, 0, 0, 0, 0, 0],\
                          binary_output=False)

This will cause a read from outside the bounds of the splits tensor buffer in the implementation of the RaggedBincount op:

    for (int idx = 0; idx < num_values; ++idx) {
      while (idx >= splits(batch_idx)) {
        batch_idx++;
      }
      ...
      if (bin < size) {
        if (binary_output_) {
          out(batch_idx - 1, bin) = T(1);
        } else {
          T value = (weights_size > 0) ? weights(idx) : T(1);
          out(batch_idx - 1, bin) += value;
        }
      } 
    }

Before the for loop, batch_idx is set to 0. The attacker sets splits(0) to be 7, hence the while loop does not execute and batch_idx remains 0. This then results in writing to out(-1, bin), which is before the heap allocated buffer for the output tensor.

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

CVE-2021-29514 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 (2.3.3, 2.4.2); upgrading removes the vulnerable code path.

Kodem intelligence

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We have patched the issue in GitHub commit eebb96c2830d48597d055d247c0e9aebaea94cd5.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.

1. What is CVE-2021-29514? CVE-2021-29514 is a low-severity out-of-bounds write vulnerability in tensorflow (pip), affecting versions >= 2.3.0, < 2.3.3. It is fixed in 2.3.3, 2.4.2. A write operation targets a memory location beyond the intended buffer boundary.
2. How severe is CVE-2021-29514? CVE-2021-29514 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-2021-29514?
   • tensorflow (pip) (versions >= 2.3.0, < 2.3.3)
   • tensorflow-cpu (pip) (versions >= 2.3.0, < 2.3.3)
   • tensorflow-gpu (pip) (versions >= 2.3.0, < 2.3.3)
4. Is there a fix for CVE-2021-29514? Yes. CVE-2021-29514 is fixed in 2.3.3, 2.4.2. Upgrade to this version or later.
5. Is CVE-2021-29514 exploitable, and should I be worried? Whether CVE-2021-29514 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-2021-29514 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-2021-29514?
   • Upgrade tensorflow to 2.3.3 or later
   • Upgrade tensorflow to 2.4.2 or later
   • Upgrade tensorflow-cpu to 2.3.3 or later
   • Upgrade tensorflow-cpu to 2.4.2 or later
   • Upgrade tensorflow-gpu to 2.3.3 or later
   • Upgrade tensorflow-gpu to 2.4.2 or later

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