CVE-2026-90554 (GCVE-0-2026-90554)
Vulnerability from cvelistv5 – Published: 2026-09-12 12:08 – Updated: 2026-09-12 12:08
VLAI
EPSS
VEX
Title
vLLM before 0.28.0 Denial of Service via audio extraction
Summary
vLLM versions >=0.10.2 and <0.28.0 do not apply any audio decode-size or duration limit when extracting audio from video input for NanoNemotronVL models. In nano_nemotron_vl.py, _extract_audio_from_videos calls load_audio_pyav(BytesIO(video_bytes)) without the max_duration_s or max_decode_bytes parameters, so neither VLLM_MAX_AUDIO_DECODE_DURATION_S nor VLLM_MAX_AUDIO_DECODE_BYTES is enforced (unlike the direct audio upload path in AudioMediaIO). When a NanoNemotronVL model is served with use_audio_in_video=True, an attacker who supplies a small, highly compressed video as multimodal input can force the server to allocate gigabytes of memory during audio decoding, resulting in a denial of service. Fixed in vLLM 0.28.0.
Severity
6.2 (Medium)
CWE
- CWE-400 - Uncontrolled Resource Consumption
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/vllm-project/vllm/security/adv… | vendor-advisory |
| https://www.vulncheck.com/advisories/vllm-before-… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| vllm-project | vLLM |
Affected:
0.10.2 , < 0.28.0
(semver)
Unaffected: 0.28.0 (semver) cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:* |
Date Public
2026-08-28 00:00
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"title": "vLLM before 0.28.0 Denial of Service via audio extraction",
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"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2026-09-12T12:34:57+00:00",
"cve": "CVE-2026-90554",
"id": "CVE-2026-90554",
"initial_release_date": "2026-09-12T12:08:57.508000+00:00",
"product_status:known_affected": "27",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "vllm: vLLM: Denial of Service via video audio extraction",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-90554.json",
"version": "3"
}
}
}
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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.
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.
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