VulnerabilityPublished 25 Sep 2025

CVE-2025-46153: PyTorch · Inefficient CPU Computation

Source CVE Database V5

Not known to be exploited · CVSS 5.3 Medium · fixed in 3.7.0 : update affected systems when convenient.

Data as of 26 Oct 2026, 13:47 UTC

CVSS 3.1 base score

5.3Medium

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.4% chance of exploitation in 30 days – higher than 35% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch: Available – fixed in 3.7.0

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: Low
Integrity: None
Availability: None

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: Low: There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to the impacted component.
Integrity: None: There is no loss of integrity within the impacted component.
Availability: None: There is no impact to availability within the impacted component.

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 25 Sep 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC or the CVSS exploit maturity reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

AI-suggested

If you cannot patch yet, avoid using fallback_random=true with the affected dropout modules if consistent behavior is critical.

Description

PyTorch versions prior to 3.7.0 contain an inconsistency in the bernoulli_p decompose function within decompositions.py. This inconsistency affects the behavior of nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d modules when the fallback_random parameter is set to true. The issue results in a deviation from the expected eager CPU implementation behavior.

Impact

The inconsistency in the bernoulli_p decompose function may lead to unexpected or incorrect behavior in dropout layers (nn.Dropout1d, nn.Dropout2d, nn.Dropout3d) when fallback_random is true. The impact is limited to a loss of consistency in the function's behavior, with a confidentiality impact rated low and no integrity or availability impact reported.

Summary

CVE-2025-46153 identifies a functional inconsistency in PyTorch before version 3.7.0 related to the bernoulli_p decompose function in decompositions.py. This inconsistency impacts the nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d layers when fallback_random is enabled, causing them to behave differently than the eager CPU implementation. The vulnerability is classified under CWE-1176 and has a CVSS 3.1 base score of 5.3, indicating a medium severity level.

CWE-1176Inefficient CPU Computation (opens in a new tab)
Class weakness
The product performs CPU computations using algorithms that are not as efficient as they could be for the needs of the developer, i.e., the computations can be optimized further.
OWASP Cheat Sheets
File Upload (opens in a new tab)
OWASP ASVS 4.0.3
V12.1.1 (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

Community reviews

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Exploitation evidence

What to do

Description

AI analysisMachine-generated, updated 8 Sep 2026, 13:22 UTC

WeaknessesCWE-1176

Technical details

References

Community reviews

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