VulnerabilityPublished 25 Sep 2025Updated 26 Sep 2025

CVE-2025-55560: pytorch · Uncontrolled Resource Consumption

Source CVE Database V5

Not known to be exploited · CVSS 7.5 High.

Data as of 26 Oct 2026, 17:16 UTC

CVSS 3.1 base score

7.5High

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

EPSS: 0.4% chance of exploitation in 30 days – higher than 34% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

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

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

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: None: There is no loss of confidentiality within the impacted component.
Integrity: None: There is no loss of integrity within the impacted component.
Availability: High: There is total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).

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

Organizations should immediately audit their AI/ML environments to identify deployments using PyTorch version 2.7.0 with Inductor compilation, particularly those involving tensor conversions between sparse and dense formats. Until an official patch is released, mitigation strategies include: 1) Avoid compiling models with Inductor when using to_sparse() and to_dense() methods; 2) Refactor models to minimize or eliminate the use of these tensor conversion methods if Inductor compilation is necessary; 3) Implement resource usage monitoring and limits on ML workloads to detect and contain abnormal consumption indicative of exploitation attempts; 4) Employ network-level protections to restrict access to ML model serving endpoints; 5) Engage with PyTorch maintainers and monitor official channels for patches or updates; 6) Test updated models in isolated environments before production deployment to ensure stability. Additionally, organizations should incorporate this vulnerability into their incident response plans and conduct tabletop exercises simulating DoS scenarios in AI services.

Description

An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor.

Impact

For European organizations, especially those engaged in AI research, development, and deployment, this vulnerability poses a risk of service outages in critical machine learning applications. Industries such as finance, healthcare, automotive, and telecommunications that leverage PyTorch for AI-driven analytics, diagnostics, autonomous systems, or customer services could experience disruptions. A successful DoS attack could degrade operational efficiency, delay decision-making processes, and impact customer-facing services. Given the increasing reliance on AI models in European digital infrastructure and the EU's emphasis on AI regulation and trustworthiness, such disruptions could also have regulatory and reputational consequences. Furthermore, organizations using cloud-based AI platforms or managed ML services that incorporate PyTorch 2.7.0 with Inductor compilation might face indirect exposure if their providers are affected.

CWE-400Uncontrolled Resource Consumption (opens in a new tab)
Class weaknessLikelihood of exploit: High
The product does not properly control the allocation and maintenance of a limited resource.
MITRE discourages mapping vulnerabilities to this broad entry; the root cause is usually a more specific weakness.
Architecture and Design
Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place.
Architecture and Design
Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, or; uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed.
Architecture and Design
Ensure that protocols have specific limits of scale placed on them.
OWASP Cheat Sheets
File Upload (opens in a new tab)
OWASP ASVS 4.0.3
V12.1.1 (opens in a new tab)
Attack patterns
XML Ping of the Death (opens in a new tab)
Regular Expression Exponential Blowup (opens in a new tab)
Sustained Client Engagement (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

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Exploitation evidence

What to do

Description

AI analysisMachine-generated, updated 3 Oct 2025, 00:38 UTC

WeaknessesCWE-400

Technical details

References

Community reviews

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