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CVE Record

CVE-2026-34756: vLLM Affected by Unauthenticated OOM Denial of Service via Unbounded `n` Parameter in OpenAI API Server

vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, a Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the n parameter in the ChatCompletionRequest and CompletionRequest Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large n value. This completely blocks the Python asyncio event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue. This vulnerability is fixed in 0.19.0.

MediumCVSS 6.5Not KEV-listedUpdated
Glexia's TakeAutomated analysismoderate

Security readout for executives and security teams

Plain-English summary

CVE-2026-34756 can let a remote caller crash an affected vLLM OpenAI-compatible API server by sending a request with an extremely large completion count. The issue affects vLLM 0.1.0 through versions before 0.19.0 and is fixed in 0.19.0. The business impact is service outage, not data theft.

Executive priority

Handle as a near-term availability risk for AI services. It does not indicate confidentiality or integrity compromise, but exposed inference endpoints could be taken offline cheaply. Upgrade affected deployments during the next urgent maintenance window, sooner for internet-facing or multi-tenant systems.

Technical view

The affected ChatCompletionRequest and CompletionRequest models lacked an upper bound for the n parameter. Processing a very large value can block the Python asyncio event loop and exhaust heap memory before scheduling, causing an immediate denial of service. Sources list CWE-1284 and CWE-770, with CVSS 3.1 score 6.5 and availability impact high.

Likely exposure

Exposure is most relevant where vLLM’s OpenAI-compatible API server is reachable by untrusted users or services. Internal-only deployments still carry risk if tenants, workloads, or upstream applications can submit arbitrary completion requests.

Exploitation context

The bundle says a single HTTP request can trigger the condition, but KEV is false and the provided sources do not claim active exploitation. The CVSS vector lists PR:L while the description says unauthenticated; treat this as a source inconsistency until vendor records are reconciled.

Researcher notes

The root issue is unbounded input driving request object allocation before scheduling. The main evidence comes from the GitHub advisory, upstream fix references, CVE data, and Red Hat tracking. Avoid assuming exploit activity or affected downstream products beyond the listed sources.

Mitigation direction

  • Upgrade vLLM to version 0.19.0 or later.
  • Prioritize exposed OpenAI-compatible API servers using affected vLLM versions.
  • If using Red Hat packages, review RHSA-2026:36005 and RHSA-2026:36006 applicability.
  • If immediate upgrade is impossible, check vendor guidance for supported interim controls.
  • Monitor affected services for OOM crashes, restarts, and abnormal request patterns.

Validation and detection

  • Inventory vLLM versions across inference and model-serving environments.
  • Identify deployments exposing the OpenAI-compatible API server.
  • Confirm whether running versions are earlier than 0.19.0.
  • Review service telemetry for recent OOM events or unexplained API server restarts.
  • Check package source against upstream vLLM or relevant Red Hat advisories.
Prepared
Confidence
high
Sources
10

Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.

Potential ATT&CK relevance

Conservative CVE-to-ATT&CK context

These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.

ATT&CK lookup starting points

Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.

cwe · low confidence lookup

CWE-1284: Exact CWE lookup

Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.

Open ATT&CK lookup
cwe · low confidence lookup

CWE-770: Exact CWE lookup

Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.

Open ATT&CK lookup
cve · low confidence lookup

CVE-2026-34756 mapping review

Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.

Open ATT&CK lookup
Vulnerability profileCVE Program record
Severity
Medium
CVSS
6.5 (3.1)
Known Exploited
No
Published

Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Official CVE source material

CNA and ADP enrichment extracted from CVE v5

These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.

2CVSS vectors
5Timeline events
2ADP providers
9Source links

SSVC decision data

CISA-ADPCISA Coordinator
Timestamp
Version
2.0.3
Exploitation: noneAutomatable: noTechnical Impact: partial

CVSS vector scores

2 official scores

We collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.

ScoreVersionSeverityVectorExploitImpactSource
6.5CVSS 3.1MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H2.83.6GitHub_M
6.5CVSS 3.1MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H2.83.6redhat-SADP

Vulnerability scoring details

Base CVSS 3.1 score

6.5Medium
CVSS 3.1 vector shape for CVE-2026-34756Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Attack Vector
NetworkAdjacentLocalPhysical
Attack Complexity
LowHigh
Privileges Required
NoneLowHigh
User Interaction
NoneRequired
Scope
ChangedUnchanged
Confidentiality Impact
HighLowNone
Integrity Impact
HighLowNone
Availability Impact
HighLowNone

Vulnerability timeline

Timeline events are normalized from CVE metadata, CNA source timelines, ADP timelines, and KEV metadata when present.

  1. CVE reservedCVE Program

    The CVE ID was reserved by the assigning CNA.

  2. ADP timelineredhat-SADP

    Made public.

  3. CVE publishedCVE Program

    The CVE record was published.

  4. ADP timelineredhat-SADP

    Reported to Red Hat.

  5. CVE updatedCVE Program

    The CVE record metadata indicates this as the latest update time.

ADP provider summaries

CISA-ADPCISA ADP Vulnrichment
other:ssvc
redhat-SADPvllm: vLLM: Denial of Service via excessively large 'n' parameter in OpenAI-compatible API
other:Red Hat severity ratingcvssV3_1
  • 2026-04-06T16:03:45.222Z: Reported to Red Hat.
  • 2026-04-06T15:40:03.448Z: Made public.

Source materials

Affected products

Products and packages named in the record

VendorProductVersion / packageStatus
vllm-projectvllm>= 0.1.0, < 0.19.0Listed
Weakness

CWE details

CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.

CWE-1284 · source CWE mapping

Improper Validation of Specified Quantity in Input

Improper Validation of Specified Quantity in Input represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

CWE-770 · source CWE mapping

Allocation of Resources Without Limits or Throttling

Allocation of Resources Without Limits or Throttling represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.