CVE-2025-46153
What it is
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.
At a glance
- Exploitability
- Can be exploited over the network without logging in. Limited direct impact.
- Exploit odds
- 0.4% chance of exploitation in the next 30 days (EPSS).
- Fix
- A patch, advisory or fixed version is referenced. Upgrade or apply it.
Timeline
- PublishedDisclosed publicly on CVE.org and NVD
- Last updatedMost recent change to the record
Details
- Published
- Last modified
- Exploit probability
- 0.4% · EPSS, next 30 days
- Weakness
- CWE-1176
- Record status
- Analyzed
Affected
- Linuxfoundation Pytorch < 2.7.0
Attack profile CVSS 3.1 · 5.3
- Attack vector
- Network
- Complexity
- Low
- Privileges needed
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- Low
- Integrity
- None
- Availability
- None
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
Is it in your code?
Hand this to your coding agent. It searches your project for the affected products and checks versions. Nothing leaves your machine.
Read https://cve-list.com/agent/cve/CVE-2025-46153.md and follow it. Check whether this project is affected by CVE-2025-46153. Read only. Don't change anything and don't upload my code. Tell me yes, no or not sure, with the file as proof.
