A malicious user can cause a denial of service by altering a SavedModel such that TensorByteSize would trigger CHECK failures.
int64_t TensorByteSize(const TensorProto& t) {
// num_elements returns -1 if shape is not fully defined.
int64_t num_elems = TensorShape(t.tensor_shape()).num_elements();
return num_elems < 0 ? -1 : num_elems * DataTypeSize(t.dtype());
}
TensorShape constructor throws a CHECK-fail if shape is partial or has a number of elements that would overflow the size of an int. The PartialTensorShape constructor instead does not cause a CHECK-abort if the shape is partial, which is exactly what this function needs to be able to return -1.
We have patched the issue in GitHub commit c2426bba00a01de6913738df8fa78e0215fcce02.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
| Score | Percentile |
|---|---|
| 0.22% | 44.52% |
| Base score | Version | Severity | Vector |
|---|---|---|---|
| 6.5 | 3.1 | — |
|
| Type | Value |
|---|---|
| GHSA | GHSA-4j82-5ccr-4r8v ↗ |
| CVE | CVE-2022-23582 ↗ |
| CWE id | Name |
|---|---|
| CWE-617 | Reachable Assertion |
Vulnerable version ranges and first patched releases as published by GitHub.
| Ecosystem | Package | Vulnerable range | First patched | Vulnerable functions |
|---|---|---|---|---|
| pip | tensorflow | < 2.5.3 | 2.5.3 | — |
| pip | tensorflow | >= 2.6.0, < 2.6.3 | 2.6.3 | — |
| pip | tensorflow | = 2.7.0 | 2.7.1 | — |
| pip | tensorflow-cpu | < 2.5.3 | 2.5.3 | — |
| pip | tensorflow-cpu | >= 2.6.0, < 2.6.3 | 2.6.3 | — |
| pip | tensorflow-cpu | = 2.7.0 | 2.7.1 | — |
| pip | tensorflow-gpu | < 2.5.3 | 2.5.3 | — |
| pip | tensorflow-gpu | >= 2.6.0, < 2.6.3 | 2.6.3 | — |
| pip | tensorflow-gpu | = 2.7.0 | 2.7.1 | — |