CVE-2026-22807: vLLM affected by RCE via auto_map dynamic module loading during model initialization
vLLM is an inference and serving engine for large language models (LLMs). Starting in version 0.10.1 and prior to version 0.14.0, vLLM loads Hugging Face `auto_map` dynamic modules during model resolution without gating on `trust_remote_code`, allowing attacker-controlled Python code in a model repo/path to execute at server startup. An attacker who can influence the model repo/path (local directory or remote Hugging Face repo) can achieve arbitrary code execution on the vLLM host during model load. This happens before any request handling and does not require API access. Version 0.14.0 fixes the issue.
Security readout for executives and security teams
Plain-English summary
CVE-2026-22807 is a high-severity remote code execution issue in vLLM. If an attacker can influence which model repository or local model path a server loads, their Python code may run when vLLM starts. API access is not required, but control over model selection or startup configuration is needed.
Executive priority
Prioritize remediation for production AI serving hosts and shared GPU infrastructure. The business risk is host compromise during model loading, especially where model choices are user-controlled or automated from untrusted sources.
Technical view
vLLM versions 0.10.1 through before 0.14.0 load Hugging Face auto_map dynamic modules during model resolution without properly gating on trust_remote_code. This can execute attacker-controlled Python from a model repository or path during initialization, before request handling. The issue is classified as CWE-94 with CVSS 3.1 score 8.8.
Likely exposure
Exposure is most likely in AI serving environments running vLLM 0.10.1 to before 0.14.0 where users, automation, CI/CD, or orchestration can set model repositories or local model paths.
Exploitation context
The bundle does not show KEV listing or confirmed active exploitation. Exploitation requires influence over the model repo/path loaded by vLLM, including a remote Hugging Face repo or local directory. It occurs at server startup, not through ordinary inference requests.
Researcher notes
Focus validation on model resolution paths, trust_remote_code behavior, and startup-time loading. The provided sources identify the fixed version and code changes but do not provide evidence of exploitation in the wild.
Mitigation direction
Upgrade vLLM to version 0.14.0 or later where applicable.
Restrict who can configure model repositories or local model paths.
Use only approved and pinned model sources until remediated.
Review Red Hat errata for product-specific package updates.
Check vendor guidance for environment-specific hardening.
Validation and detection
Inventory vLLM deployments and record installed versions.
Confirm no deployment runs vLLM 0.10.1 through before 0.14.0.
Review startup configuration for model repo/path sources.
Verify who can modify model selection in deployment pipelines.
Check Red Hat package status against listed errata if applicable.
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 · medium confidence lookup
CWE-94: Code execution behavior lookup
Code execution and unsafe deserialization weaknesses often justify reviewing execution behavior and process telemetry. 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.
The CVE wording references code or command execution, so execution technique review may help defensive triage. This is a Glexia inferred lookup path, not an official MITRE, ATT&CK, or CVE Program mapping.
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
18Source links
SSVC decision data
CISA-ADPCISA Coordinator
Timestamp
Version
2.0.3
Exploitation: noneAutomatable: noTechnical Impact: total
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.
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
CWE-94 · source CWE mapping
Improper Control of Generation of Code ('Code Injection')
Improper Control of Generation of Code ('Code Injection') represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.