CVE-2021-29605

Integer overflow in TFLite memory allocation

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Attribution

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

The TFLite code for allocating TFLiteIntArrays is vulnerable to an integer overflow issue:

int TfLiteIntArrayGetSizeInBytes(int size) {
  static TfLiteIntArray dummy;
  return sizeof(dummy) + sizeof(dummy.data[0]) * size;
}   

An attacker can craft a model such that the size multiplier is so large that the return value overflows the int datatype and becomes negative. In turn, this results in invalid value being given to malloc:

TfLiteIntArray* TfLiteIntArrayCreate(int size) {
  TfLiteIntArray* ret = (TfLiteIntArray*)malloc(TfLiteIntArrayGetSizeInBytes(size));
  ret->size = size; 
  return ret;
}

In this case, ret->size would dereference an invalid pointer.

An arithmetic operation produces a value that exceeds the integer type's maximum, causing it to wrap to an unexpected small value. Typical impact: incorrect size calculations leading to heap overflows or logic errors.

CVE-2021-29605 has a CVSS score of 7.1 (Medium). 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 7c8cc4ec69cd348e44ad6a2699057ca88faad3e5.

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-2021-29605? CVE-2021-29605 is a medium-severity integer overflow or wraparound 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. An arithmetic operation produces a value that exceeds the integer type's maximum, causing it to wrap to an unexpected small value.
2. How severe is CVE-2021-29605? CVE-2021-29605 has a CVSS score of 7.1 (Medium). 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-2021-29605?
   • 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-2021-29605? Yes. CVE-2021-29605 is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade to this version or later.
5. Is CVE-2021-29605 exploitable, and should I be worried? Whether CVE-2021-29605 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-2021-29605 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-2021-29605?
   • 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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