CVE-2021-41219

Undefined behavior via nullptr reference binding in sparse matrix multiplication

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Attribution

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

The code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to nullptr:

import tensorflow as tf
  
tf.raw_ops.SparseMatMul(
  a=[[1.0,1.0,1.0]],
  b=[[],[],[]],
  transpose_a=False,
  transpose_b=False,
  a_is_sparse=False, 
  b_is_sparse=True)

This occurs whenever the dimensions of a or b are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access.

A read operation accesses a memory location beyond the intended buffer boundary. Typical impact: sensitive data disclosure or crash.

CVE-2021-41219 has a CVSS score of 7.8 (High). 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 (2.6.1, 2.5.2, 2.4.4); upgrading removes the vulnerable code path.

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We have patched the issue in GitHub commit e6cf28c72ba2eb949ca950d834dd6d66bb01cfae.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

1. What is CVE-2021-41219? CVE-2021-41219 is a high-severity out-of-bounds read vulnerability in tensorflow (pip), affecting versions >= 2.6.0, < 2.6.1. It is fixed in 2.6.1, 2.5.2, 2.4.4. A read operation accesses a memory location beyond the intended buffer boundary.
2. How severe is CVE-2021-41219? CVE-2021-41219 has a CVSS score of 7.8 (High). 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-2021-41219?
   • tensorflow (pip) (versions >= 2.6.0, < 2.6.1)
   • tensorflow-cpu (pip) (versions >= 2.6.0, < 2.6.1)
   • tensorflow-gpu (pip) (versions >= 2.6.0, < 2.6.1)
4. Is there a fix for CVE-2021-41219? Yes. CVE-2021-41219 is fixed in 2.6.1, 2.5.2, 2.4.4. Upgrade to this version or later.
5. Is CVE-2021-41219 exploitable, and should I be worried? Whether CVE-2021-41219 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-2021-41219 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-2021-41219?
   • Upgrade tensorflow to 2.6.1 or later
   • Upgrade tensorflow to 2.5.2 or later
   • Upgrade tensorflow to 2.4.4 or later
   • Upgrade tensorflow-cpu to 2.6.1 or later
   • Upgrade tensorflow-cpu to 2.5.2 or later
   • Upgrade tensorflow-cpu to 2.4.4 or later
   • Upgrade tensorflow-gpu to 2.6.1 or later
   • Upgrade tensorflow-gpu to 2.5.2 or later
   • Upgrade tensorflow-gpu to 2.4.4 or later

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