CVE-2025-46153: PyTorch before 3.7.0 has a bernoulli_p decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallback_random=True.
CSIRTS triage
- What
- The bernoulli_p decompose function in decompositions.py lacks consistency with eager CPU implementation, affecting nn.Dropout layers with fallback_random=True.
- Who is affected
- PyTorch users running versions before 3.7.0 using Dropout layers with fallback_random=True.
- Urgency
- Medium severity (CVSS 5.3); incorrect numerical behavior in dropout layers, not currently exploited.
- Action
- Upgrade PyTorch to version 3.7.0 or later.
AI-assisted analysis generated from the source advisory — verify against the original.
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Details
Original advisory: https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-46153
Exploitation outlook
EPSS (FIRST.org) estimates each CVE’s probability of exploitation in the next 30 days — here is the CSIRTS.com read on those numbers.
- Low exploitation riskCVE-2025-461530.39% 30-day exploitation probability — currently an unlikely target, but scores change as exploit code circulates. Riskier than 32% of all EPSS-scored CVEs.
Referenced CVEs
| CVE | CSIRTS overview | External |
|---|---|---|
| CVE-2025-46153 | coverage & exploitation status | NVD · CVE.org |
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