CVE-2025-48956
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Description
A flaw was found in vLLM. A denial of service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large X-Forwarded-For header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user.
Statement
This vulnerability is considered Important rather than just Moderate because it enables a complete denial of service with minimal effort from a remote, unauthenticated attacker. Unlike moderate flaws that might require specific conditions, partial access, or complex exploitation chains, here a single oversized HTTP request is sufficient to exhaust server memory and crash the vLLM service. Since vLLM is often deployed as a backend for high-availability inference workloads, this creates a high-impact risk: availability is entirely compromised, all running workloads are disrupted, and recovery may require manual intervention. The lack of authentication barriers makes the attack surface fully exposed over the network, which elevates the severity beyond Moderate to Important.
Mitigation
Until a fix is available, the risk can be reduced by running vLLM behind a reverse proxy such as Nginx, Envoy, or HAProxy with strict header size limits, ensuring that oversized requests are dropped before reaching the service. Additional safeguards like container or VM resource limits and traffic monitoring can help contain the impact, but upgrading to the patched release remains the definitive solution.
Additional Information
- This content is not included.Bugzilla 2372522: vllm: HTTP header size limit not enforced allows Denial of Service from Unauthenticated requests
- Content from cwe.mitre.org is not included.CWE-130: Improper Handling of Length Parameter Inconsistency
- FAQ: Frequently asked questions about CVE-2025-48956
- Offline Security Data data is available for integration with other systems. See Offline Security Data API to get started.
External References
Content from www.cve.org is not included.https://www.cve.org/CVERecord?id=CVE-2025-48956
Content from nvd.nist.gov is not included.https://nvd.nist.gov/vuln/detail/CVE-2025-48956
Affected Packages and Issued Red Hat Security Errata
| Products / Services | Components | State | Errata |
|---|---|---|---|
| Red Hat AI Inference Server | rhaiis/vllm-cuda-rhel9 | Affected | |
| Red Hat AI Inference Server | rhaiis/vllm-rocm-rhel9 | Affected | |
| Red Hat AI Inference Server | rhaiis-preview/vllm-cuda-rhel9 | Will not fix | |
| Red Hat AI Inference Server | rhaiis/vllm-spyre-rhel9 | Will not fix | |
| Red Hat AI Inference Server | rhaiis/vllm-tpu-rhel9 | Will not fix | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/disk-image-nvidia-rhel9 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/docling-serve-rhel9 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/gemma-2-9b-it | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/gemma-2-9b-it-fp8 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-3.1-8b-lab-v1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-3.1-8b-lab-v2.1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-3.1-8b-starter-v1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-3.1-8b-starter-v2.1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-7b-redhat-lab | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-7b-starter | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-8b-code-base | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-8b-code-instruct | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-8b-lab-v1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-8b-lab-v2-preview | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/granite-8b-starter-v1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/knowledge-adapter-v3 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/mixtral-8x7b-instruct-v0-1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-docling-layout | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-docling-tableformer | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-gemma-2-9b-it | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-gemma-2-9b-it-fp8 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-3-1-8b-lab-v1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-3-1-8b-lab-v2 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-3-1-8b-lab-v2-1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-3-1-8b-starter-v1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-3-1-8b-starter-v2 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-3-1-8b-starter-v2-1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-7b-redhat-lab | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-7b-starter | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-8b-code-base | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-8b-code-instruct | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-8b-lab-v1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-8b-lab-v2-preview | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-granite-8b-starter-v1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-knowledge-adapter-v3 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-mixtral-8x7b-instruct-v0-1 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-prometheus-8x7b-v2-0 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-skills-adapter-v3 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/modelcar-snowflake-arctic-embed-l-v2.0 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/pathservice-rhel9 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/prometheus-8x7b-v2-0 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/skills-adapter-v3 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/ui-rhel9 | Affected | |
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-aws-cuda-rhel9 | Will not fix | |
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-azure-cuda-rhel9 | Will not fix | |
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-cuda-rhel9 | Will not fix | |
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-gcp-cuda-rhel9 | Will not fix | |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/instructlab-intel-rhel9 | Fixed | This content is not included.RHSA-2025:19421 |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/bootc-intel-rhel9 | Fixed | RHSA-2025:19422 |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/instructlab-nvidia-rhel9 | Fixed | RHSA-2025:19423 |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/bootc-azure-amd-rhel9 | Fixed | RHSA-2025:19424 |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/instructlab-amd-rhel9 | Fixed | RHSA-2025:19425 |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/bootc-gcp-nvidia-rhel9 | Fixed | RHSA-2025:19426 |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/bootc-amd-rhel9 | Fixed | RHSA-2025:19427 |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/bootc-nvidia-rhel9 | Fixed | RHSA-2025:19428 |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/bootc-aws-nvidia-rhel9 | Fixed | RHSA-2025:19429 |
| Red Hat Enterprise Linux AI 1.5 | rhelai1/bootc-azure-nvidia-rhel9 | Fixed | RHSA-2025:19430 |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-vllm-cuda-rhel9 | Affected | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-vllm-rocm-rhel9 | Affected | |
| Red Hat OpenShift AI 3.3 | rhoai/odh-kserve-agent-rhel9 | Fixed | RHSA-2026:3713 |
| Red Hat OpenShift AI 3.3 | rhoai/odh-kserve-controller-rhel9 | Fixed | RHSA-2026:3713 |
| Red Hat OpenShift AI 3.3 | rhoai/odh-kserve-router-rhel9 | Fixed | RHSA-2026:3713 |
| Red Hat OpenShift AI 3.3 | rhoai/odh-kserve-storage-initializer-rhel9 | Fixed | RHSA-2026:3713 |
| Red Hat OpenShift AI 3.3 | rhoai/odh-vllm-cpu-rhel9 | Fixed | RHSA-2026:3713 |
| Red Hat OpenShift AI 3.3 | rhoai/odh-vllm-gaudi-rhel9 | Fixed | RHSA-2026:3713 |
Common Vulnerability Scoring System (CVSS) Score Details
Important note
CVSS scores for open source components depend on vendor-specific factors (e.g. version or build chain). Therefore, Red Hat's score and impact rating can be different from NVD and other vendors. Red Hat remains the authoritative CVE Naming Authorities (CNA) source for its products and services (see Red Hat classifications ).
| CVSS v3 Score Breakdown | Red Hat | NVD |
|---|---|---|
| CVSS v3 Base Score | 7.5 | |
| Attack Vector | Network | |
| Attack Complexity | Low | |
| Privileges Required | None | |
| User Interaction | None | |
| Scope | Unchanged | |
| Confidentiality Impact | None | |
| Integrity Impact | None | |
| Availability Impact | High |
CVSS v3 Vector
Red Hat CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Frequently Asked Questions
Why is Red Hat's CVSS v3 score or Impact different from other vendors?
For more information, see https://access.redhat.com/solutions/762393.
My product is listed as "Under investigation" or "Affected", when will Red Hat release a fix for this vulnerability?
- "Under investigation" doesn't necessarily mean that the product is affected by this vulnerability. It only means that our Analysis Team is still working on determining whether the product is affected and how it is affected.
- "Affected" means that our Analysis Team has determined that this product is affected by this vulnerability and might release a fix to address this in the near future.
What can I do if my product is listed as "Will not fix"?
Available options depend mostly on the Impact of the vulnerability and the current Life Cycle phase of your product. Overall, you have the following options:
- Upgrade to a supported product version that includes a fix for this vulnerability (recommended).
- Apply a mitigation (if one exists).
- Open a This content is not included.support case to request a prioritization of releasing a fix for this vulnerability.
What can I do if my product is listed as "Fix deferred"?
Available options depend mostly on the Impact of the vulnerability and the current Life Cycle phase of your product. Overall, you have the following options:
- Apply a mitigation (if one exists).
- Open a This content is not included.support case to request a prioritization of releasing a fix for this vulnerability.
- Red Hat Engineering focuses on addressing high-priority issues based on their complexity or limited lifecycle support. Therefore, lower-priority issues will not receive immediate fixes.
What is a mitigation?
I have a Red Hat product but it is not in the above list, is it affected?
Why is my security scanner reporting my product as vulnerable to this vulnerability even though my product version is fixed or not affected?
My product is listed as "Out of Support Scope". What does this mean?
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