CVE-2025-62372VulnerabilityPublished 21 Nov 2025Updated 24 Nov 2025

CVE-2025-62372: vllm · Improper Validation of Array Index

Affects vllmSource CVE Database V5

Not known to be exploited · CVSS 8.3 High : upgrade to 0.11.1 soon.

Data as of 26 Oct 2026, 08:30 UTC

CVSS 4.0 base score

8.3High

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.
Patch: Available – fixed in 0.11.1

CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:H

Attack Vector: Network
Attack Complexity: Low
Attack Requirements: None
Privileges Required: Low
User Interaction: None
Vulnerable system confidentiality: None
Vulnerable system integrity: None
Vulnerable system availability: High
Subsequent system confidentiality: None
Subsequent system integrity: None
Subsequent system availability: High

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

Attack Vector: Network: The vulnerable system is bound to the network stack and the set of possible attackers extends beyond the other options listed below, 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: The attacker must take no measurable action to exploit the vulnerability. The attack requires no target-specific circumvention to exploit the vulnerability. An attacker can expect repeatable success against the vulnerable system.
Attack Requirements: None: The successful attack does not depend on the deployment and execution conditions of the vulnerable system. The attacker can expect to be able to reach the vulnerability and execute the exploit under all or most instances of the vulnerability.
Privileges Required: Low: The attacker requires privileges that provide basic capabilities that are typically limited to settings and resources owned by a single low-privileged user. Alternatively, an attacker with Low privileges has the ability to access only non-sensitive resources.
User Interaction: None: The vulnerable system can be exploited without interaction from any human user, other than the attacker.
Vulnerable system confidentiality: None: There is no loss of confidentiality within the Vulnerable System.
Vulnerable system integrity: None: There is no loss of integrity within the Vulnerable System.
Vulnerable system availability: High: There is a total loss of availability, resulting in the attacker being able to fully deny access to resources in the Vulnerable System; 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 Vulnerable System (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).
Subsequent system confidentiality: None: There is no loss of confidentiality within the Subsequent System or all confidentiality impact is constrained to the Vulnerable System.
Subsequent system integrity: None: There is no loss of integrity within the Subsequent System or all integrity impact is constrained to the Vulnerable System.
Subsequent system availability: High: There is a total loss of availability, resulting in the attacker being able to fully deny access to resources in the Subsequent System; 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 Subsequent System (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 v4.0 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 21 Nov 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 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

Upgrade vllm-project/vllm to 0.11.1.

Affected software

Product	Affected	Fixed in
GitHubvllm-project/vllm`pkg:github/vllm-project/vllm`Other threats	Affected >=0.5.5 <0.11.1	Fixed in 0.11.1

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

1. Upgrade all vLLM deployments to version 0.11.1 or later immediately to apply the official patch that fixes the improper validation issue. 2. Implement strict input validation and sanitization at the application layer to verify embedding input shapes before passing them to vLLM, rejecting malformed or unexpected inputs. 3. Deploy runtime monitoring and anomaly detection to identify unusual input patterns or repeated crashes indicative of exploitation attempts. 4. Use containerization or sandboxing to isolate vLLM processes, limiting the blast radius of potential crashes. 5. Maintain robust incident response plans to quickly recover AI inference services in case of denial of service. 6. Coordinate with AI model providers and infrastructure teams to ensure compatibility and security of multimodal model inputs. 7. Regularly audit and update AI serving infrastructure to keep pace with security patches and best practices. 8. Consider network-level protections such as rate limiting and filtering to reduce exposure to malicious input submissions.

Description

vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape (e.g.hidden dimension is wrong), regardless of whether the model is intended to support such inputs (as defined in the Supported Models page). This issue has been patched in version 0.11.1.

Impact

For European organizations, the primary impact of CVE-2025-62372 is denial of service (DoS) against AI inference services using vulnerable versions of vLLM. This can cause downtime or degraded performance in AI-driven applications, including natural language processing, multimodal AI services, and other LLM-based solutions. Organizations relying on vLLM for critical AI workloads, such as research institutions, cloud service providers, and enterprises deploying AI-powered customer services, may experience operational disruptions. The vulnerability could also be leveraged as part of a broader attack to degrade AI capabilities or disrupt business continuity. Given the increasing adoption of AI technologies across Europe, especially in sectors like finance, healthcare, and manufacturing, service interruptions could have cascading effects on productivity and service delivery. Additionally, the lack of authentication and user interaction requirements means attackers can exploit this remotely, increasing the risk of widespread impact. While no data confidentiality or integrity impact is indicated, availability loss alone can be costly and damaging to reputation.

CWE-129Improper Validation of Array Index (opens in a new tab)
Variant weaknessLikelihood of exploit: HighOWASP Top 10 A05:2025: Injection (opens in a new tab)
The product uses untrusted input when calculating or using an array index, but the product does not validate or incorrectly validates the index to ensure the index references a valid position within the array.
Architecture and Design · Input Validation
Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).
Architecture and Design
For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks…
Requirements · Language Selection
Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
OWASP Cheat Sheets
XML Security (opens in a new tab)
Mass Assignment (opens in a new tab)
Injection Prevention (opens in a new tab)
SQL Injection Prevention (opens in a new tab)
Injection Prevention in Java (opens in a new tab)
OWASP ASVS 4.0.3
V13.3.1 (opens in a new tab)
V5.1.4 (opens in a new tab)
V5.1.3 (opens in a new tab)
Attack patterns
Overflow Buffers (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

Affected software

Description

AI analysisMachine-generated, updated 28 Nov 2025, 04:41 UTC

WeaknessesCWE-129

Technical details

References

Community reviews

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