CVE-2026-103241 (GCVE-0-2026-103241)
Vulnerability from cvelistv5 – Published: 2026-09-30 16:45 – Updated: 2026-09-30 16:45 X_Open Source
VLAI
EPSS
VEX
Title
vllm-project vLLM Gemma4UnifiedParser gemma4.rs denial of service
Summary
A flaw has been found in vllm-project vLLM up to 0.26.0. This vulnerability affects unknown code of the file rust/src/parser/src/unified/gemma4.rs of the component Gemma4UnifiedParser. Executing a manipulation can lead to denial of service. The attack may be launched remotely. The exploit has been published and may be used. Upgrading to version 0.29.1rc0 is able to resolve this issue. This patch is called 3439bad37e68ba9755a46f4f6b44a4aeaf1f60a9. Upgrading the affected component is advised.
Severity
CWE
- CWE-404 - Denial of Service
Assigner
References
10 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/411965 | vdb-entry |
| https://vuldb.com/vuln/411965/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-103241 | third-party-advisory |
| https://vuldb.com/submit/956250 | third-party-advisory |
| https://github.com/vllm-project/vllm/issues/50927 | issue-tracking |
| https://github.com/vllm-project/vllm/pull/54303 | issue-trackingpatch |
| https://gist.github.com/Yunzez/8e98d656aa667095b5… | exploit |
| https://github.com/vllm-project/vllm/commit/3439b… | patch |
| https://github.com/vllm-project/vllm/releases/tag… | patch |
| https://github.com/vllm-project/vllm/ | product |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vLLM |
Affected:
0.1
Affected: 0.2 Affected: 0.3 Affected: 0.4 Affected: 0.5 Affected: 0.6 Affected: 0.7 Affected: 0.8 Affected: 0.9 Affected: 0.10 Affected: 0.11 Affected: 0.12 Affected: 0.13 Affected: 0.14 Affected: 0.15 Affected: 0.16 Affected: 0.17 Affected: 0.18 Affected: 0.19 Affected: 0.20 Affected: 0.21 Affected: 0.22 Affected: 0.23 Affected: 0.24 Affected: 0.25 Affected: 0.26.0 Unaffected: 0.29.1rc0 cpe:2.3:a:vllm-project:vllm:*:*:*:*:*:*:*:* |
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{
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{
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"url": "https://github.com/vllm-project/vllm/pull/54303"
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}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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