Floating point exception in `Conv2D` in TensorFlow
Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).
| Vendor | Product | Default status | Versions | ||||||||||||
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| tensorflow | tensorflow | affected |
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Configuration 1 [-]
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No data available yet.
| Source | ID | Title |
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EUVD |
EUVD-2022-6908 | TensorFlow is an open source platform for machine learning. If `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
Github GHSA |
GHSA-q5jv-m6qw-5g37 | TensorFlow vulnerable to floating point exception in `Conv2D` |
No CVSS v4.0
No CVSS v3.0
No CVSS v2
This CVE is not in the KEV list.
Exploit prediction score: < 1% (0.488% probability within 30 days)
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