CVE-2026-105922 (GCVE-0-2026-105922)
Vulnerability from cvelistv5 – Published: 2026-10-06 14:45 – Updated: 2026-10-06 14:45
VLAI
EPSS
VEX
Title
vllm-project vLLM Penalty utils.py get_token_bin_counts_and_mask denial of service
Summary
A security flaw has been discovered in vllm-project vLLM up to 0.31.0. This impacts the function get_token_bin_counts_and_mask of the file vllm/model_executor/layers/utils.py of the component Penalty Handler. Performing a manipulation results in denial of service. Remote exploitation of the attack is possible. The exploit has been released to the public and may be used for attacks. The project was informed of the problem early through an issue report but has not responded yet.
Severity
CWE
- CWE-404 - Denial of Service
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/413896 | vdb-entrytechnical-description |
| https://vuldb.com/vuln/413896/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-105922 | third-party-advisory |
| https://vuldb.com/submit/992707 | third-party-advisory |
| https://github.com/vllm-project/vllm/issues/57719 | issue-tracking |
| https://gist.github.com/Yunzez/5c70a3bb328eaf6d64… | exploit |
| https://github.com/vllm-project/vllm/ | product |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| 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 Affected: 0.27 Affected: 0.28 Affected: 0.29 Affected: 0.30 Affected: 0.31.0 cpe:2.3:a:vllm-project:vllm:*:*:*:*:*:*:*:* |
{
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{
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{
"status": "affected",
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{
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{
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"version": "0.28"
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{
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{
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}
],
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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
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