CVE-2021-41220

Use after free / memory leak in CollectiveReduceV2

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Attribution

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

The async implementation of CollectiveReduceV2 suffers from a memory leak and a use after free:

import tensorflow as tf
  
tf.raw_ops.CollectiveReduceV2(
  input=[],
  group_size=[-10, -10, -10],
  group_key=[-10, -10],
  instance_key=[-10],
  ordering_token=[],
  merge_op='Mul',
  final_op='Div')

This occurs due to the asynchronous computation and the fact that objects that have been std::move()d from are still accessed:

auto done_with_cleanup = [col_params, done = std::move(done)]() {
  done();
  col_params->Unref();
};
OP_REQUIRES_OK_ASYNC(c,
                     FillCollectiveParams(col_params, REDUCTION_COLLECTIVE,
                                          /*group_size*/ c->input(1),
                                          /*group_key*/ c->input(2),
                                          /*instance_key*/ c->input(3)),
                     done);

Here, done is already moved from by the time OP_REQUIRES_OK_ASYNC macro needs to invoke it in case of errors. In this case, we get an undefined behavior, which can manifest via crashes, std::bad_alloc throws or just memory leaks.

Memory is accessed after it has been freed, leading to undefined behavior in native code. Typical impact: memory corruption, crash, or potential code execution.

CVE-2021-41220 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); upgrading removes the vulnerable code path.

Kodem intelligence

Already deployed Kodem?

We have patched the issue in GitHub commit ca38dab9d3ee66c5de06f11af9a4b1200da5ef75.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.

1. What is CVE-2021-41220? CVE-2021-41220 is a high-severity use after free vulnerability in tensorflow (pip), affecting versions = 2.6.0. It is fixed in 2.6.1. Memory is accessed after it has been freed, leading to undefined behavior in native code.
2. How severe is CVE-2021-41220? CVE-2021-41220 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-2021-41220?
   • tensorflow (pip) (versions = 2.6.0)
   • tensorflow-cpu (pip) (versions = 2.6.0)
   • tensorflow-gpu (pip) (versions = 2.6.0)
4. Is there a fix for CVE-2021-41220? Yes. CVE-2021-41220 is fixed in 2.6.1. Upgrade to this version or later.
5. Is CVE-2021-41220 exploitable, and should I be worried? Whether CVE-2021-41220 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-2021-41220 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-2021-41220?
   • Upgrade tensorflow to 2.6.1 or later
   • Upgrade tensorflow-cpu to 2.6.1 or later
   • Upgrade tensorflow-gpu to 2.6.1 or later

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