CVE-2026-100649: Allocation of Resources Without Limits or Throttling in vllm-project vllm
vLLM before 0.29.0 contains a resource-limit bypass vulnerability in PyNvVideoCodec decoder allocation where sampler subclass shadowing allows independent counter increments. Unauthenticated attackers can select different sampler subclasses in video requests to exceed configured decoder limits and exhaust unaccounted GPU memory.
AI Analysis
Technical Summary
CVE-2026-100649 describes a resource-limit bypass vulnerability in vLLM prior to version 0.29.0. The issue arises in the PyNvVideoCodec decoder allocation where sampler subclass shadowing permits independent counter increments. Attackers without authentication can select different sampler subclasses in video requests to bypass configured decoder limits, leading to unaccounted GPU memory exhaustion.
Potential Impact
An unauthenticated attacker can exploit this vulnerability to exceed decoder resource limits, causing GPU memory exhaustion. This could degrade system performance or cause denial of service conditions due to resource depletion. There is no indication of direct code execution or data compromise from the provided information.
Mitigation Recommendations
No explicit patch or remediation is stated in the provided data. Users should upgrade to vLLM version 0.29.0 or later once available, as the vulnerability affects versions prior to 0.29.0. Until then, monitor vendor advisories for official fixes or temporary mitigations. Patch status is not yet confirmed — check the vendor advisory for current remediation guidance.
CVE-2026-100649: Allocation of Resources Without Limits or Throttling in vllm-project vllm
Description
vLLM before 0.29.0 contains a resource-limit bypass vulnerability in PyNvVideoCodec decoder allocation where sampler subclass shadowing allows independent counter increments. Unauthenticated attackers can select different sampler subclasses in video requests to exceed configured decoder limits and exhaust unaccounted GPU memory.
CVSS v4.0
Score 6.3medium
Affected software
vllm-project
vllm
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AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
CVE-2026-100649 describes a resource-limit bypass vulnerability in vLLM prior to version 0.29.0. The issue arises in the PyNvVideoCodec decoder allocation where sampler subclass shadowing permits independent counter increments. Attackers without authentication can select different sampler subclasses in video requests to bypass configured decoder limits, leading to unaccounted GPU memory exhaustion.
Potential Impact
An unauthenticated attacker can exploit this vulnerability to exceed decoder resource limits, causing GPU memory exhaustion. This could degrade system performance or cause denial of service conditions due to resource depletion. There is no indication of direct code execution or data compromise from the provided information.
Mitigation Recommendations
No explicit patch or remediation is stated in the provided data. Users should upgrade to vLLM version 0.29.0 or later once available, as the vulnerability affects versions prior to 0.29.0. Until then, monitor vendor advisories for official fixes or temporary mitigations. Patch status is not yet confirmed — check the vendor advisory for current remediation guidance.
Technical Details
- Data Version
- 5.2
- Assigner Short Name
- VulnCheck
- Date Reserved
- 2026-09-26T02:33:07.898Z
- Cvss Version
- 4.0
- State
- PUBLISHED
Threat ID: 6ab7c9a7f7a7c5410652fd31
Added to database: 09/26/2026, 13:33:27 UTC
Last enriched: 09/26/2026, 14:03:28 UTC
Last updated: 09/27/2026, 01:57:11 UTC
Views: 11
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