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vLLM CVE-2026-71486

| EUVDEUVD-2026-60376 MEDIUM
Uncontrolled Resource Consumption (CWE-400)
2026-08-17 security-advisories@github.com GHSA-8737-qx52-hjff
4.3
CVSS 3.1 · Vendor: github
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Severity by source

Vendor (github) PRIMARY
4.3 MEDIUM
AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L
vuln.today AI
4.3 MEDIUM

Network-reachable API requires authenticated access (PR:L); only availability degradation applies with no confidentiality or integrity impact.

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

Primary rating from Vendor (github).

CVSS VectorVendor: github

Attack Vector
Network
Attack Complexity
Low
Privileges Required
Low
User Interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
Low

Lifecycle Timeline

3
Patch available
Aug 17, 2026 - 21:17 EUVD
Source Code Evidence Fetched
Aug 17, 2026 - 20:37 vuln.today
Analysis Generated
Aug 17, 2026 - 20:37 vuln.today

Blast Radius

ecosystem impact
† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth
  • 3 pypi packages depend on vllm (3 direct, 0 indirect)

Ecosystem-wide dependent count for version 0.26.0.

DescriptionCVE.org

vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the /v1/completions/derender and /v1/chat/completions/derender endpoints accept caller-supplied GenerateResponse objects whose generate_responses, choices, token_ids, prompt_logprobs, logprobs.content, top_logprobs, and routed_experts structures are processed by OnlineDerenderer and tokenizer.decode before max_model_len, max_tokens, max_num_seqs, or response-size limits are enforced, allowing an authenticated API client to consume excessive CPU and memory and produce oversized responses. This issue is fixed in version 0.26.0.

AnalysisAI

Uncontrolled resource consumption in vLLM's derender API endpoints allows authenticated clients to exhaust server CPU and memory by submitting arbitrarily large caller-supplied GenerateResponse objects. All vLLM versions prior to 0.26.0 process structures including generate_responses, choices, token_ids, prompt_logprobs, logprobs.content, top_logprobs, and routed_experts through OnlineDerenderer and tokenizer.decode before enforcing any model or response-size limits. …

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Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata

Access
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Delivery
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Exploit
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Execution
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Vulnerability AssessmentAI

Exploitation Exploitation requires a valid low-privilege API credential to the vLLM server (PR:L per CVSS vector). … Additional conditions and limiting factors are described in the full assessment.
Risk Assessment The CVSS 4.3 Medium score (AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L) accurately reflects a low-complexity, network-reachable DoS constrained by a PR:L authentication requirement. … Full risk analysis with EPSS, KEV, and SSVC signal comparison available after sign-in.
Exploit Scenario Full exploit scenario with step-by-step reproduction available after sign-in.
Remediation Upgrade vLLM to version 0.26.0 or later, which enforces pre-processing validation bounds on token_ids (against max_model_len), choices count (against VLLM_MAX_N_SEQUENCES), generate_responses count, logprobs.content length, top_logprobs count, and rejects negative token_ids with HTTP 400 responses before any resource-intensive processing occurs. … Detailed patch versions, workarounds, and compensating controls in full report.

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Vendor StatusVendor

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CVE-2026-71486 vulnerability details – vuln.today

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