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vLLM EUVDEUVD-2026-109440

| CVE-2026-116179 HIGH
Allocation of Resources Without Limits or Throttling (CWE-770)
2026-10-25 VulnCheck GHSA-6vc9-vh4h-cxh4
8.7
CVSS 4.0 · Vendor: VulnCheck
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Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain

  1. Access
    Reach vLLM chat endpoint on multimodal model
  2. Delivery
    Submit large inline video/jpeg with media_io_kwargs.video.num_frames=-1
  3. Exploit
    Bypass frame-count cap into unlimited decode branch of VideoMediaIO.load_base64
  4. Execution
    Decode and numpy.stack every frame into multi-GB array
  5. Impact
    Exhaust API-frontend memory, OOM-kill/crash serving process

Vulnerability Assessment

Weakness
CWE
OWASP Top 10:2025
A06:2025 - Insecure Design
ASVS 5.0
V2 - Validation and Business Logic Resource allocation limits
MITRE ATT&CK

1 technique across 1 tactic: Impact

The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up.

Exploitation Conditions

Exploitation requires a vLLM deployment (0.18.0 through before 0.31.0) that serves a video-capable multimodal model and accepts inline video/jpeg media, and that honors per-request media_io_kwargs - runtime media_io_kwargs support first shipped in stable 0.18.0, which is why earlier versions are unaffected. The concrete trigger is a request setting media_io_kwargs.video.num_frames to -1 (or an arbitrarily large positive value) together with a large inline video/jpeg payload, which bypasses the positive-num_frames frame cap in VideoMediaIO.load_base64. Limiting factors: the target must actually be configured as a video/multimodal server (a text-only vLLM instance is not exposed), and in fixed versions (>= 0.31.0) the attack is blocked unless the operator explicitly re-enables client overrides with --trust-request-mm-kwargs. The CVSS vector lists PR:N (no authentication), so any client able to reach the endpoint qualifies; where the endpoint sits behind authentication or a gateway, that access control limits the realistic attacker set.

Impact

This is a genuine but bounded operational risk, best characterized as a reliability/availability threat rather than a system-compromise emergency. The CVSS 4.0 vector (AV:N/AC:L/AT:N/PR:N/UI:N) describes a network-reachable, low-complexity issue requiring no authentication per the vector, and impact is confined to availability (VA:H) with no confidentiality or integrity impact (VC:N/VI:N) - consistent with a CWE-770 uncontrolled resource consumption / denial-of-service flaw. The vuln.today decision label is 'this_week', indicating prioritized but not emergency remediation. No CISA KEV listing and no EPSS score were provided in the input, so there is no signal of active or probable widespread exploitation; status is therefore 'no public exploit identified at time of analysis'. The only exploitation evidence is the vendor/VulnCheck advisory noting the issue was reproduced internally on vLLM 0.19.1 - this is a confirmed reproduction report, not published public exploit code. A key nuance: although the CVSS vector says PR:N, real-world exploitation requires that the target actually serve a video-capable multimodal model and accept per-request media_io_kwargs, which materially narrows the exposed population below what the unauthenticated network vector alone implies. Net: a legitimately high-severity availability bug for affected multimodal deployments, but not a critical-impact or mass-exploitation priority - patch on the vendor's 'this_week' cadence.

Exploit ScenarioDerived

An attacker (or careless client) with reach to a vLLM chat/completions endpoint backed by a video-capable multimodal model submits a large inline video/jpeg payload and sets media_io_kwargs.video.num_frames to -1, which selects the unlimited-frame branch of VideoMediaIO.load_base64. The server decodes and numpy.stacks every frame into a multi-gigabyte array before request validation rejects it, exhausting API-frontend memory/CPU and crashing or OOM-killing the serving process. No published exploit code is identified in the provided data; the flaw was reproduced by the reporter on vLLM 0.19.1 and the low attack complexity (AC:L) means crafting the malicious request is trivial.

This Week Schedule remediation this week. Why: HIGH severity (CVSS 7.0-8.9).
Patch available 0.3% exploitation probability (EPSS)

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AnalysisAIv1 – Oct 25, 2026

Memory exhaustion in vLLM 0.18.0 through 0.30.x lets a client crash or OOM-kill the serving frontend by setting media_io_kwargs.video.num_frames to -1 (or an arbitrarily large positive value) alongside a large inline video/jpeg payload, so the API decodes and stacks every frame. Only deployments serving a video-capable multimodal model and honoring per-request media_io_kwargs are exposed; text-only instances are not affected, and versions before 0.18.0 lack runtime media_io_kwargs support. The CVSS 4.0 vector (AV:N/AC:L/PR:N/UI:N/VA:H) rates this a high-availability-impact, confidentiality- and integrity-neutral flaw, and no public exploit code has been identified at time of analysis, though the trigger is a simple documented parameter override.

RemediationAI

Upgrade to vLLM 0.31.0 or later, which ships PR #58830 and by default rejects non-empty per-request media_io_kwargs so clients can no longer override the server's video decoder frame settings per the advisory at https://github.com/vllm-project/vllm/security/advisories/GHSA-4m6g-h9mv-wqjc. If an interim upgrade is not possible, note that the trigger is a client-supplied decoder override on the chat endpoint, so compensating controls are: block untrusted access to video/multimodal chat endpoints (authentication or an API gateway in front of the frontend, which limits the realistic attacker set but does not remove the flaw for authenticated tenants), and cap request body size at the reverse proxy to reject large inline video/jpeg payloads, accepting the trade-off that legitimate large media requests will also fail. Running the frontend under a cgroup/container memory limit reduces blast radius by containing the OOM to the serving process, at the cost of a restart on exhaustion rather than prevention. Operators who deliberately enable --trust-request-mm-kwargs after upgrading re-expose themselves to this class of client-supplied resource exhaustion and should do so only for fully trusted clients; the VulnCheck advisory at https://www.vulncheck.com/advisories/vllm-0.18.0-before-0.31.0-memory-exhaustion-via-media-io-kwargs-num-frames-override provides an additional reference.

Affected VersionsENISA

vllm 0.18.0 <0.31.0

Affected Packages

euvd vllm fix: 0.31.0

Source: OSV (Open Source Vulnerabilities)

Affects your stack

Affects 1 package in euvd

Checking this CVE against your own stacks and servers is part of the Basic plan and up.

Technical ContextAI · show details

vLLM's VideoMediaIO.load_base64 handles inline video/jpeg data: when num_frames is positive it caps the number of frames, when zero it rejects the request, and otherwise it splits the entire input and decodes every frame, combining them with numpy.stack. Because request-level media_io_kwargs.video values were forwarded to VideoMediaIO without restricting num_frames, a client could select the unlimited branch (-1) or an oversized positive value, forcing multi-gigabyte frame-array allocation in the API frontend before later request validation rejects the request. This maps to CWE-770 (Allocation of Resources Without Limits or Throttling), i.e. a missing/insufficient bound on a resource allocation rather than a memory-safety bug. The root cause is a configuration-pass-through design flaw introduced together with runtime media_io_kwargs support in stable 0.18.0; the upstream fix in PR #58830 changes the default so non-empty per-request media_io_kwargs are rejected, with operators able to re-enable client overrides via --trust-request-mm-kwargs. The CPE entry cpe:2.3:a:vllm-project:vllm:*:*:*:*:*:*:*:* is unversioned, so version scoping must come from the advisory ranges.

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Severity by source

Vendor (VulnCheck) PRIMARY 8.7 HIGH
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
vuln.today AI 7.5 HIGH

Network-reachable chat endpoint, trivial crafted request (AC:L), no auth per vector (PR:N); impact is availability-only memory exhaustion (A:H) with no confidentiality or integrity effect.

3.1 AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
4.0 AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
Red Hat 7.5 HIGH

Primary rating from Vendor (VulnCheck).

CVSS VectorVendor: VulnCheck

Attack Vector
Network
Attack Complexity
Low
Attack Requirements
None
Privileges Required
None
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
None

Lifecycle Timeline

2
Patch available
Oct 25, 2026 - 14:01 EUVD
CVE Published
Oct 25, 2026 - 13:20 cve.org
HIGH 8.7

DescriptionCVE.org

vLLM from 0.18.0 before 0.31.0 contains a resource exhaustion vulnerability that allows API clients to bypass the video/jpeg frame-count cap by overriding num_frames through media_io_kwargs. Attackers can set num_frames to -1 with a large inline video so VideoMediaIO.load_base64 decodes every frame, exhausting memory and crashing the API frontend.

Technical POC Analysis

CWE-770: Allocation of Resources Without Limits or Throttling.

Full technical POC analysis - root cause, step-by-step exploitation and patch breakdown - is part of the Pro and Business plans.

Executive ViewAI

Why It Matters

CVE-2026-116179 is a high-severity memory exhaustion vulnerability in vLLM versions 0.18.0 through 0.30.x that enables an unauthenticated remote client to crash the serving frontend by sending a crafted request to a video-capable multimodal model. This disrupts AI inference services and could cause costly downtime for teams relying on vLLM for production workloads; leadership should prioritize mitigation because no vendor patch is currently available and the attack vector is simple and publicly documented.

Business Impact: High
Compensating Controls

Restrict network access to vLLM endpoints to trusted users or internal networks; at the edge or proxy layer, reject requests where media_io_kwargs.video.num_frames is negative or exceeds a safe maximum, and enforce limits on inline media payload size; configure container memory limits and process supervision to automatically restart the serving frontend after a crash, reducing downtime; monitor for anomalous request patterns that set num_frames to extreme values and alert on repeated large video payloads. No vendor-released patch identified at time of analysis.

Threat Intelligence Overview

EPSS Score
0.3%
Top 77% exploitable
Prevalence
Medium
vLLM is a popular LLM inference server, but common mainly in AI/ML deployments
Social Media
1
Sightings
Data Quality
7 sources
Confidence: High
EUVD NVD CVE.org EPSS Vulnrichment Vendor CIRCL GitHub

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity HIGH
CISA KEV No
Public POC No
EPSS 0.3%
Patch available Yes
Active threat No
Deciding rule
HIGH severity (CVSS 7.0-8.9)
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision
Scheduled
Exploitation
None
System Exposure· derived
Open
Automatable
Yes
Human Impact
High
Technical Impact
Partial

Priority Score

44
Low Medium High Critical
KEV: 0
EPSS: 0
CVSS: +44
PoC: 0
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

Denial of Service attacks render applications or systems unavailable by overwhelming resources or triggering failure conditions.

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Vendor StatusVendor

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