CVE-2022-23558

Integer overflow in TFLite array creation

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Attribution

This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team.

An attacker can craft a TFLite model that would cause an integer overflow in TfLiteIntArrayCreate:

TfLiteIntArray* TfLiteIntArrayCreate(int size) {
  int alloc_size = TfLiteIntArrayGetSizeInBytes(size);
  // ...
  TfLiteIntArray* ret = (TfLiteIntArray*)malloc(alloc_size);
  // ...
} 

The TfLiteIntArrayGetSizeInBytes returns an int instead of a size_t:

int TfLiteIntArrayGetSizeInBytes(int size) {
  static TfLiteIntArray dummy;

  int computed_size = sizeof(dummy) + sizeof(dummy.data[0]) * size;
#if defined(_MSC_VER)
  // Context for why this is needed is in http://b/189926408#comment21
  computed_size -= sizeof(dummy.data[0]);
#endif
  return computed_size;
}

An attacker can control model inputs such that computed_size overflows the size of int datatype.

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-2022-23558 has a CVSS score of 7.6 (High). The vector is network-reachable, 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 (2.5.3, 2.6.3, 2.7.1); upgrading removes the vulnerable code path.

Kodem intelligence

Already deployed Kodem?

We have patched the issue in GitHub commit a1e1511dde36b3f8aa27a6ec630838e7ea40e091.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

1. What is CVE-2022-23558? CVE-2022-23558 is a high-severity integer overflow or wraparound vulnerability in tensorflow (pip), affecting versions < 2.5.3. It is fixed in 2.5.3, 2.6.3, 2.7.1. 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-2022-23558? CVE-2022-23558 has a CVSS score of 7.6 (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-2022-23558?
   • tensorflow (pip) (versions < 2.5.3)
   • tensorflow-cpu (pip) (versions < 2.5.3)
   • tensorflow-gpu (pip) (versions < 2.5.3)
4. Is there a fix for CVE-2022-23558? Yes. CVE-2022-23558 is fixed in 2.5.3, 2.6.3, 2.7.1. Upgrade to this version or later.
5. Is CVE-2022-23558 exploitable, and should I be worried? Whether CVE-2022-23558 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-2022-23558 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-2022-23558?
   • Upgrade tensorflow to 2.5.3 or later
   • Upgrade tensorflow to 2.6.3 or later
   • Upgrade tensorflow to 2.7.1 or later
   • Upgrade tensorflow-cpu to 2.5.3 or later
   • Upgrade tensorflow-cpu to 2.6.3 or later
   • Upgrade tensorflow-cpu to 2.7.1 or later
   • Upgrade tensorflow-gpu to 2.5.3 or later
   • Upgrade tensorflow-gpu to 2.6.3 or later
   • Upgrade tensorflow-gpu to 2.7.1 or later

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