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Full vulnerability report · 2026
CVE-2026-37237High confidence

vLLM up to and including 0.17.0 allows remote attackers to cause a Denial of Service via memory exhaustion

Unknown · Unknown

7.5HighCVSS 3.1
Recommended action
Within 7 days

High technical severity; prioritise exposed affected systems while verifying vendor guidance.

Patch available
R
Operational reassessment

Published severity in operational context

Open reassessment dashboard →
Published severityHighOperational priority:High, unchanged from published severity.unchanged

Evidence used

  • No CISA KEV confirmation is currently recorded.
  • The selected CVSS metric records a network-reachable, unauthenticated path with no user interaction.
  • EPSS is 0.73% for the current model date.

Compensating controls

  • Validate the affected product branch and deploy the verified fixed release.
  • Restrict the affected network interface to trusted sources where business-safe.
  • Monitor vendor guidance and exploitation sources for a material change.

Verification

  1. Confirm that the asset runs Unknown Unknown and falls inside the recorded affected range.
  2. Verify the installed build against the product-specific fixed version after deployment.
  3. Validate exposure, authentication requirements and compensating controls in the actual environment.
  4. Reopen this reassessment when CVSS, KEV, EPSS, exploit evidence or remediation changes.
Mitigation target: Within 30 daysRemediation target: Within 180 days

This automated reassessment organises public evidence. It does not know asset exposure, business impact or control effectiveness and does not replace CVSS or a human risk decision.

Direct vendor intelligence

Authoritative vendor CSAF and VEX advisories

Structured product status and remediation from the issuing vendor. Product-state explanations are always visible; large lists can be searched or downloaded.

1 current
CVE-2026-37237 · CSAF 2.0 · revision 3 · finalRed Hat Product Securityvllm: vLLM: Denial of Service via memory exhaustion from oversized media files
19 known affected

The vendor explicitly identifies these products as affected by this CVE.

  • rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server
  • rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server
  • rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
  • rhaii/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server
  • rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
  • rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server
  • rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
  • rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server
  • rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
  • rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
  • rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
  • rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
Summary
A flaw was found in vLLM. Remote attackers can exploit this vulnerability by providing a URL to an excessively large media file. The AsyncMediaIO.fetch_audio and AsyncMediaIO.fetch_image functions, responsible for fetching user-supplied media, do not limit the response size. This oversight allows an attacker to exhaust server memory, leading to a Denial of Service (DoS).
Remediation
For more information visit https://access.redhat.com/errata/RHSA-2026:73929
Optional official sources

National CERT insights
?CERT means Computer Emergency Response Team; CSIRT is the closely related term Computer Security Incident Response Team.

Choose official national sources for this report. Each advisory shows its original language. Your selection is remembered on this device and included in shared links.

Official European source

ENISA European Vulnerability Database

Official EUVD identifiers, advisory evidence and known-exploited context. Missing fields are not treated as evidence of low risk.

1 current
ENISA EUVD identifier

EUVD-2026-67775

No EUVD known-exploited evidence

vLLM up to and including 0.17.0 allows remote attackers to cause a Denial of Service via memory exhaustion. The AsyncMediaIO.fetch_audio and AsyncMediaIO.fetch_image functions in multimodal/inputs.py fetch user-supplied media URLs using aiohttp and call r.read() without enforcing a maximum response size, allowing an attacker to exhaust server memory by providing a URL to an arbitrarily large file.

EUVD state
Present in the current official mapping
Known exploitation
Not present in the current ENISA EUVD known-exploited dataset. This is not proof of no exploitation.
ENISA score
7.5 · CVSS 3.1
Advisory evidence
No linked advisory details stored yet
Recommended actionWithin 7 days

High technical severity; prioritise exposed affected systems while verifying vendor guidance.

Patch available
01

What, why and how

vLLM up to and including 0.17.0 allows remote attackers to cause a Denial of Service via memory exhaustion. The AsyncMediaIO.fetch_audio and AsyncMediaIO.fetch_image functions in multimodal/inputs.py fetch user-supplied media URLs using aiohttp and call r.read() without enforcing a maximum response size, allowing an attacker to exhaust server memory by providing a URL to an arbitrarily large file.

What

vLLM up to and including 0.17.0 allows remote attackers to cause a Denial of Service via memory exhaustion. The AsyncMediaIO.fetch_audio and AsyncMediaIO.fetch_image functions in multimodal/inputs.py fetch user-supplied media URLs using aiohttp and call r.read() without enforcing a maximum response size, allowing an attacker to exhaust server memory by providing a URL to an arbitrarily large file.

Why

The product does not properly control the allocation and maintenance of a limited resource.

How

An attacker operating through a network path may attempt exploitation without authentication or user interaction. If successful, the issue may disrupt the affected service.

What

vLLM up to and including 0.17.0 allows remote attackers to cause a Denial of Service via memory exhaustion. The AsyncMediaIO.fetch_audio and AsyncMediaIO.fetch_image functions in multimodal/inputs.py fetch user-supplied media URLs using aiohttp and call r.read() without enforcing a maximum response size, allowing an attacker to exhaust server memory by providing a URL to an arbitrarily large file.

Why

The product does not properly control the allocation and maintenance of a limited resource.

How

An attacker operating through a network path may attempt exploitation without authentication or user interaction. If successful, the issue may disrupt the affected service.

02

Exploit reality and attack path

CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.

Observed exploitation
?Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.
No confirmed evidence

No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.

Public PoC / exploit material
?Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.
None recorded

No exploit-tagged reference or CISA SSVC proof-of-concept state is currently recorded. Research may still exist outside the structured feeds.

Likely attack path
a network path → Uncontrolled Resource Consumption → disrupt the affected service
Attack surface
Network
Privileges required
None: unauthenticated exploitation is possible
User interaction
None
Attack complexity
Low: no specialised conditions are recorded
Security boundary
Unchanged: impact remains within the vulnerable component's security authority
Weakness
?CWE means Common Weakness Enumeration: a standard category for the underlying weakness.
CWE-400 ↗

CWE-400: Uncontrolled Resource Consumption. The product does not properly control the allocation and maintenance of a limited resource.

CVSS vector
?CVSS means Common Vulnerability Scoring System. The vector records the metric values used to calculate technical severity.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Common Vulnerability Scoring System 3.1: the compact vector below is decoded into plain language.

AVNetworkAttack vector: The vulnerable component can be reached over a network.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.PRNonePrivileges required: The attacker does not need an account or existing privileges.UINoneUser interaction: No action by another user is required.SUnchangedScope: The security impact remains within the vulnerable component's authority.CNoneConfidentiality impact: No direct loss is represented by this metric.INoneIntegrity impact: No direct loss is represented by this metric.AHighAvailability impact: A successful attack can cause a major loss.
Post-exploitation / living off the land
No specific living-off-the-land technique is confirmed in the structured sources. Monitor normal administration tools for activity inconsistent with the affected service's baseline.
NetworkUnauthenticatedDenial of serviceCWE-400
A

Official authority intelligence

Only matched European and national findings are included. Language selectors and unavailable sources are omitted.

ENISA EUVD · EUVD-2026-67775Official EUVD mapping

vLLM up to and including 0.17.0 allows remote attackers to cause a Denial of Service via memory exhaustion. The AsyncMediaIO.fetch_audio and AsyncMediaIO.fetch_image functions in multimodal/inputs.py fetch user-supplied media URLs using aiohttp and call r.read() without enforcing a maximum response size, allowing an attacker to exhaust server memory by providing a URL to an arbitrarily large file.

Official EUVD record ↗
Cyber Security Agency of Singapore · English · CSA-SB-20260902Security Bulletin 2 Sept 2026

The Cyber Security Agency of Singapore included this CVE in its official Security Bulletin 2 Sept 2026, published on 2 September 2026. Open the linked bulletin for the product, severity and reference information published in that issue.

Official advisory ↗
JVN iPedia · Japanese · JVNDB-2026-036178vLLMにおけるリソースの枯渇に関する脆弱性

vLLMのバージョン0.17.0まで（含む）において、リモート攻撃者がメモリ枯渇によるサービス拒否（DoS）を引き起こす可能性があります。multimodal/inputs.pyにあるAsyncMediaIO.fetch_audioおよびAsyncMediaIO.fetch_image関数は、aiohttpを使用してユーザー提供のメディアURLを取得します。しかし、最大応答サイズを制限せずにr.read()を呼び出すため、攻撃者は任意に大きなファイルのURLを提供し、サーバーのメモリを枯渇させることが可能です。

Official advisory ↗
03

Patch and workaround

Operational remediation based on structured source evidence.

Status
?Patch availability is based on structured fixed-version fields and authoritative update references. If no fix is verified, check the vendor advisory before making a change.
Patch available
Affected
Fixed
Action
For more information visit https://access.redhat.com/errata/RHSA-2026:73929
Workaround
No verified workaround is recorded. If business-safe, reduce exposure to the affected interface and allow only trusted sources until authoritative guidance is available.
04

Evidence and provenance

Published 28 Aug 2026 · Last source change 28 Aug 2026, 19:30 UTC · CWE-400 · Uncontrolled Resource Consumption

CVE recordCVE.org · 5.2
CVSS sourceCISA ADP
EPSS source
?The date BlackTree first stored a score for this CVE from the daily FIRST EPSS feed.
FIRST · tracked since 2026-08-29
European sourceENISA EUVD · EUVD-2026-67775
Product sourceVendor CSAF · Red Hat Product Security
Remediation sourceVendor CSAF · Red Hat Product Security
CWE sourceCISA ADP
NVD statusNVD enriched

Missing structured fields: affected product. Missing data is not evidence of low risk; review the primary advisory.

Material change intelligence

What changed after publication

View recent updates ↗
  1. Affected versionsThe structured affected or fixed version information changed.
    Before
    n/a · Fixed: An authoritative update reference is available, but the fixed version is not recorded in the structured CVE fields. Check the linked vendor advisory for the applicable release.
    After
    n/a · Fixed: For more information visit https://access.redhat.com/errata/RHSA-2026:73929
    Red Hat Product Security ↗
  2. Vendor guidanceAuthoritative vendor guidance changed from remediation: access.redhat.com/CVE-2026-37237 to remediation: access.redhat.com/CVE-2026-37237.
    Before
    remediation: access.redhat.com/CVE-2026-37237
    After
    remediation: access.redhat.com/CVE-2026-37237
    Red Hat Product Security ↗
  3. Remediation statusRemediation status changed from Awaiting fix to Patch available.
    Before
    Awaiting fix
    After
    Patch available
    Red Hat Product Security ↗
  4. Affected versionsThe structured affected or fixed version information changed.
    Before
    rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-llm-d-kv-cache-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-th-torch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
    After
    rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-llm-d-kv-cache-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-th-torch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · Fixed: registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:48c3099bf3d2f7df4cd0ba7ad48671ffa5b871cac158a3172b6a5b6ab6c9e6ae_arm64 as a component of Red Hat AI Inference Server 3.2; registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:80e44bc4e668045aaff6c5e2271c17c2fe2b2a34407915ed05ad9769fa37f6e7_amd64 as a component of Red Hat AI Inference Server 3.2; registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:65a8e8621aed5efaf029f04a7a3501c3a451113dc2ca8ec7295557f9e8e6916b_amd64 as a component of Red Hat AI Inference Server 3.2
    Red Hat Product Security ↗
  5. Affected versionsThe structured affected or fixed version information changed.
    Before
    rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-kserve-agent-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-kserve-controller-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-kserve-router-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-kserve-storage-initializer-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-llm-d-kv-cache-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-th-torch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
    After
    rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-llm-d-kv-cache-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-th-torch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
    Red Hat Product Security ↗
  6. ENISA EUVD mappingEUVD-2026-67775 was added to the official ENISA EUVD mapping for this CVE.
    Before
    not recorded
    After
    {"euvdId":"EUVD-2026-67775"}
    ENISA EUVD ↗
  7. Vendor guidanceAuthoritative vendor guidance changed: added patch: github.com/36506; added remediation: github.com/36506.
    Before
    no authoritative guidance recorded
    After
    patch: github.com/36506 · remediation: github.com/36506
    cve@mitre.org ↗
  8. Affected versionsThe structured affected or fixed version information changed.
    Before
    n/a · Fixed: No fixed version is explicitly recorded in the structured CVE data.
    After
    n/a · Fixed: An authoritative update reference is available, but the fixed version is not recorded in the structured CVE fields. Check the linked vendor advisory for the applicable release.
    CVE.org ↗
  9. Remediation statusRemediation status changed from Awaiting fix to Patch available.
    Before
    Awaiting fix
    After
    Patch available
    cve@mitre.org ↗
  10. SeveritySeverity changed from Unknown to High.
    Before
    Unknown
    After
    High
    CISA ADP ↗
  11. CVSS scoreCVSS score changed from not recorded to 7.5 (CVSS 3.1 · CISA ADP · CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H).
    Before
    not recorded
    After
    7.5 (CVSS 3.1 · CISA ADP · CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H)
    CISA ADP ↗
  12. Catalogue recordCVE added to the BlackTree catalogue.
    CNA ↗
Material fields only · duplicate refreshes suppressed · history retained for the configured operational retention period
CVE published
Fixed release or patch reference recorded
Fixed release recorded from official guidance
Fixed release recorded from official guidance
ENISA EUVD · EUVD-2026-67775↗Red Hat Product Security security advisory↗CVE authority or vendor reference↗Official CVE record (CNA and ADP data)↗NVD record↗
Technical terms and abbreviations used in this report
CVE
Common Vulnerabilities and Exposures: the public identifier for one disclosed vulnerability.
CVSS
Common Vulnerability Scoring System: a technical severity framework; it is not patching priority by itself.
EPSS
Exploit Prediction Scoring System: FIRST's estimate of the probability that exploitation activity will be observed in the next 30 days; it is a forecast, not confirmation.
CWE
Common Weakness Enumeration: the standard category describing the underlying software or hardware weakness.
CNA
CVE Numbering Authority: an organisation authorised to assign and publish CVE records.
CISA ADP
Cybersecurity and Infrastructure Security Agency Authorized Data Publisher: structured enrichment added to a CVE record.
NVD
National Vulnerability Database: NIST's enrichment service for CVE records.
CERT / CSIRT
A computer security incident response team that publishes warnings or coordinates incident response.
PoC
Proof of concept: public material that demonstrates or helps reproduce exploitation.
CSAF
Common Security Advisory Framework: a machine-readable format for security advisories.
LoTL
Living off the land: abuse of legitimate tools or system functions during an attack.
Free version - for non-commercial use only.CVE-2026-37237 · cve.blacktree.nl