Incorrect dropout results via inconsistent CPU decomposition
Published Sep 25, 2025 · Updated Sep 25, 2025
Incorrect calculation in PyTorch 2.6.0 allows remote attackers to alter CPU dropout results by inducing model compilation. The bernoulli_p decomposition in torch/_decomp/decompositions.py does not match eager CPU behavior, so compiled dropout produces different seeded results instead of falling back to eager execution. The condition requires nn.Dropout1d, nn.Dropout2d, or nn.Dropout3d on CPU with torch.compile and fallback_random=True; the published reproducer found consistent CUDA results.
Summary
What happened
Incorrect calculation in PyTorch 2.6.0 allows remote attackers to alter CPU dropout results by inducing model compilation. The bernoulli_p decomposition in torch/_decomp/decompositions.py does not match eager CPU behavior, so compiled dropout produces different seeded results instead of falling back to eager execution. The condition requires nn.Dropout1d, nn.Dropout2d, or nn.Dropout3d on CPU with torch.compile and fallback_random=True; the published reproducer found consistent CUDA results.
The record
- CVE
- CVE-2025-46153
- Published
- Sep 25, 2025
- Updated
- Sep 25, 2025
- Vendor
- Unknown vendor
- Product
- Unknown product
- Classifications
- CWE-1176, T1204
- Attack vector
- network
- Privileges
- unauthenticated
Timeline
How it unfolded
- Sep 25, 2025CVE publishedPublication date reported by the CVE source.
- Sep 25, 2025Record updatedLatest update available in the CVE record.
Exploitability
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Attacks
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Technologies
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