`CHECK` fail in `FakeQuantWithMinMaxVarsGradient` 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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| Source | ID | Title |
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EUVD |
EUVD-2022-6926 | TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. 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-r26c-679w-mrjm | TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient` |
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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