Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain
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AccessObtain low-privilege vLLM API credentials
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DeliverySubmit multimodal inference request with crafted image or audio URL string
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ExploitMiMoV2OmniMultiModalProcessor invokes _fetch_image or requests.get bypassing MediaConnector allowlists
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ExecutionvLLM process fetches attacker-specified remote URL or local file path
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ImpactExfiltrate retrieved content from model response or error output
Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A01:2025 - Broken Access Control
ASVS 5.0
V4 - API and Web Service URL allowlisting, SSRF prevention
MITRE ATT&CK
2 techniques across 2 tactics: Initial Access, Credential Access The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up. |
| Exploitation Conditions | Exploitation requires authenticated API access to vLLM at the low-privilege level (confirmed by PR:L in the CVSS vector), meaning the attacker must possess a valid API key or equivalent credential to submit inference requests to the target vLLM instance. The vulnerable code path is only reachable when the vLLM server is configured to serve a MiMo V2 Omni model that instantiates MiMoV2OmniMultiModalProcessor - deployments using other model families or processor implementations are not affected by this specific bypass. The attacker must supply image or audio inputs as URL strings (http://, https://, file://) or bare filesystem paths rather than pre-decoded binary or tensor data; base64 data URIs (data:image) are also accepted by the vulnerable code. Exploitation impact is bounded by the vLLM process's OS-level file permissions and network reachability: a sandboxed deployment with restricted filesystem access and egress filtering materially reduces blast radius. No special network positioning, administrator-level access, specific operating system, or non-default runtime flags beyond MiMo V2 Omni model serving are required. |
| Impact | The CVSS:3.1 vector (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N) scores 6.5 Medium, correctly reflecting low-complexity network exploitability requiring only low-privilege API access, with high confidentiality impact and no integrity or availability consequences. The PR:L requirement confirms that vLLM API credentials constitute the authentication barrier, limiting direct exposure to authorized but not necessarily trusted users. No public exploit code has been identified and this CVE is not listed in CISA KEV, meaning active exploitation is unconfirmed at time of analysis; EPSS data was not included in the provided intelligence sources. Despite the Medium score, the practical risk is elevated in environments where vLLM is exposed to semi-trusted or third-party API consumers: the vLLM process frequently runs with broad filesystem access to model weights, configuration files, and secrets, and the SSRF vector can reach cloud metadata services (169.254.169.254) or internal APIs with the vLLM host's network identity - even a mid-sized AI infrastructure team in mielec running vLLM behind a VPN for internal users should treat this as urgent if the MiMo V2 Omni processor is in use. The S:U scope designation is a modest understatement of real-world risk, as SSRF pivot to adjacent internal systems represents impact beyond the vulnerable system itself that the base vector does not capture. |
| Exploit ScenarioDerived | An authenticated vLLM API user submits a multimodal inference request targeting a MiMo V2 Omni model, embedding a crafted image URL pointing to an internal cloud metadata endpoint such as http://169.254.169.254/latest/meta-data/iam/security-credentials/ or a file:// path referencing sensitive files like environment variable files or service account keys accessible to the vLLM process. The MiMoV2OmniMultiModalProcessor calls _fetch_image or requests.get on the attacker-supplied string without consulting MediaConnector's allowlists, retrieves the target resource on behalf of the vLLM server, and the fetched content is processed as image or audio data - potentially surfacing portions of the retrieved content in error messages or inference outputs. No public proof-of-concept exploit code has been identified, but exploitation requires only a standard API call with a crafted multimodal input string. |
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Alert meAnalysisAIv1 – Aug 17, 2026
Server-side request forgery in vLLM's MiMoV2OmniMultiModalProcessor allows authenticated API users to bypass the framework's media access controls, enabling arbitrary file reads on the vLLM host and SSRF requests to internal network resources. The flaw affects all vLLM releases prior to 0.26.0 where attacker-controlled image and audio URL strings were passed directly to _fetch_image, requests.get, and Image.open instead of routing through the MediaConnector abstraction that enforces allowed_media_domains and allowed_local_media_path restrictions. No public exploit code or CISA KEV listing exists at time of analysis, but the low attack complexity and network-accessible vector make this a meaningful risk for deployments serving untrusted authenticated users via the affected multimodal processor.
RemediationAI
Upgrade vLLM to version 0.26.0 or later, which removes the vulnerable _fetch_image function and eliminates all direct requests.get and Image.open calls on user-controlled strings within MiMoV2OmniMultiModalProcessor; the specific fix commit is 54503ecec0f3ac31e5ecfc5f28652e4cc42307b5 and the release is at https://github.com/vllm-project/vllm/releases/tag/v0.26.0. For operators unable to upgrade immediately, the most effective compensating control is restricting API access to the vLLM inference endpoint to fully trusted internal service accounts only, eliminating the PR:L threat surface at the cost of reduced external user availability. Additionally, running the vLLM process under a minimally privileged OS account with filesystem access scoped exclusively to model weight directories via read-only bind mounts or AppArmor/seccomp profiles would contain local file read impact to non-sensitive paths. Egress filtering on the vLLM host to block outbound requests to cloud metadata endpoints (169.254.169.254, fd00:ec2::254) and internal RFC-1918 address ranges would mitigate SSRF pivot risk to internal services, with no user-visible trade-off for external inference workloads. None of these workarounds eliminate the underlying bypass and should be treated as temporary pending upgrade.
Affected VersionsENISA
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in pypi
Checking this CVE against your own stacks and servers is part of the Basic plan and up.
Technical ContextAI · show details
vLLM (cpe:2.3:a:vllm-project:vllm:*:*:*:*:*:*:*:*) is a high-throughput inference and serving engine for large language models. The MiMoV2OmniMultiModalProcessor, located at vllm/transformers_utils/processors/mimo_v2_omni.py, handles multimodal inputs for the MiMo V2 Omni model family. CWE-918 (Server-Side Request Forgery) describes the root cause precisely: the processor implemented its own _fetch_image function with direct requests.get calls that resolved http://, https://, file://, data:image, and bare filesystem path strings supplied as inference inputs, entirely bypassing vLLM's MediaConnector layer. MediaConnector is the authorized media resolution path in vLLM and enforces two security boundaries: allowed_media_domains (a domain allowlist for remote URLs) and allowed_local_media_path (a filesystem path allowlist for local file access). Audio inputs were handled identically - raw strings including http:// URLs and data: URIs were fetched directly via requests.get and AudioDecoder without allowlist checks. The fix in PR #43117 (commit 54503ecec0f3ac31e5ecfc5f28652e4cc42307b5) eliminates the _fetch_image function entirely, removes all direct requests.get and Image.open calls on string inputs, and restricts the processor to accepting only pre-decoded PIL.Image objects and (waveform, sr) tuples, with all URL and path resolution delegated exclusively upstream to vLLM's properly secured media pipeline.
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Severity by source
AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
Network-accessible API requires low-privilege credentials (PR:L); allowlist bypass enables arbitrary file read and SSRF but confers no write or availability impact.
Primary rating from Vendor (GitHub_M).
CVSS VectorVendor: GitHub_M
Lifecycle Timeline
1DescriptionCVE.org
vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the MiMoV2OmniMultiModalProcessor in vllm/transformers_utils/processors/mimo_v2_omni.py passes attacker-controlled image and audio strings through _fetch_image, requests.get, and Image.open instead of MediaConnector, bypassing allowed_media_domains and allowed_local_media_path protections and allowing server-side requests and reads of arbitrary files accessible to the vLLM process. This issue is fixed in version 0.26.0.
Blast Radius
ecosystem impact- 2 pypi packages depend on vllm (2 direct, 0 indirect)
Ecosystem-wide dependent count for version 0.26.0.
Threat Intelligence Overview
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.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Server-Side Request Forgery exploits applications that fetch remote resources based on user-supplied URLs.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated critical severity (CVSS 10.0
vllm-project vllm version v0.6.2 contains a vulnerability in the MessageQueue.dequeue() API function. Rated critical sev
Information exposure in vLLM inference engine versions 0.8.3 to before 0.14.1. Invalid image requests to the multimodal
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated high severity (CVSS 7.5), th
vLLM before version 0.14.1 contains a server-side request forgery vulnerability in the MediaConnector class where incons
Out-of-memory worker crashes in vLLM can be induced by a single small compressed audio payload submitted to the /v1/chat
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated medium severity (CVSS 6.5),
GPU memory exhaustion in vLLM versions prior to 0.28.0 lets an attacker with low-privileged API access (PR:L in the CVSS
Multimodal inference requests to vLLM deployments running versions prior to 0.28.0 can trigger a process-level assertion
Uncontrolled resource consumption in vLLM's OpenAI-compatible completions endpoint allows any authenticated API client t
Vllm versions up to 0.12.0 is affected by allocation of resources without limits or throttling (CVSS 6.5).
Race condition in vLLM's prompt embedding loader allows concurrent API requests to bypass the sparse tensor invariant gu
EUVD-2026-60377
GHSA-4hhp-h66f-j5j7
NVD
CVE.org
EPSS
Vulnrichment
Exploit-DB