CVE-2025-61620
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Description
A flaw was found in the server implementation of vLLM, where the handling of Jinja templates does not properly validate user-supplied input through the chat_template and chat_template_kwargs parameters. When a specially crafted template is processed, it can trigger excessive looping or recursion inside the Jinja engine, consuming large amounts of CPU and memory. This can cause the server to become unresponsive or crash, resulting in a denial-of-service (DoS) condition for applications using vLLM.
Statement
The Red Hat Product Security team has assessed the severity of this vulnerability as Moderate, as it requires authenticated access or the ability to supply templates to the vLLM server. Successful exploitation allows an attacker to exhaust system resources by submitting maliciously crafted Jinja templates that trigger excessive CPU and memory usage. The vulnerability’s root cause is the lack of proper validation and sandboxing of user-supplied template data, which can lead to denial of service (DoS) conditions affecting the availability of services built on vLLM.
Mitigation
Mitigation for this issue is either not available or the currently available options don't meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability.
Additional Information
- This content is not included.Bugzilla 2401761: vllm: vLLM OpenAI-Compatible Server Resource Exhaustion via chat_template Parameters
- Content from cwe.mitre.org is not included.CWE-400: Uncontrolled Resource Consumption
- FAQ: Frequently asked questions about CVE-2025-61620
- 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-2025-61620
Content from nvd.nist.gov is not included.https://nvd.nist.gov/vuln/detail/CVE-2025-61620
Affected Packages and Issued Red Hat Security Errata
| Products / Services | Components | State | Errata |
|---|---|---|---|
| Red Hat AI Inference Server | rhaiis/vllm-spyre-rhel9 | Fix deferred | |
| Red Hat AI Inference Server | rhaiis/vllm-tpu-rhel9 | Fix deferred | |
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-cuda-rhel9 | Fixed | RHSA-2026:3461 |
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-rocm-rhel9 | Fixed | RHSA-2026:3462 |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-amd-rhel9 | Fix deferred | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-aws-nvidia-rhel9 | Fix deferred | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-azure-amd-rhel9 | Fix deferred | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-azure-nvidia-rhel9 | Fix deferred | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-gcp-nvidia-rhel9 | Fix deferred | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-intel-rhel9 | Fix deferred | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/bootc-nvidia-rhel9 | Fix deferred | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/instructlab-amd-rhel9 | Fix deferred | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/instructlab-intel-rhel9 | Fix deferred | |
| Red Hat Enterprise Linux AI (RHEL AI) | rhelai1/instructlab-nvidia-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-kserve-agent-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-kserve-controller-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-kserve-router-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-kserve-storage-initializer-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-datascience-cpu-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-minimal-cpu-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-minimal-cpu-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-pytorch-cuda-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-pytorch-rocm-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-tensorflow-cuda-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-tensorflow-rocm-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-codeserver-datascience-cpu-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-codeserver-datascience-cpu-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-datascience-cpu-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-minimal-cpu-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-minimal-cpu-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-minimal-cuda-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-minimal-cuda-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-minimal-rocm-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-minimal-rocm-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-pytorch-cuda-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-pytorch-rocm-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-tensorflow-cuda-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-tensorflow-rocm-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-trustyai-cpu-py311-rhel9 | Fix deferred | |
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9 | Fix deferred |
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 | 6.5 | |
| Attack Vector | Network | |
| Attack Complexity | Low | |
| Privileges Required | Low | |
| 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:L/UI:N/S:U/C:N/I:N/A:H
Acknowledgements
Red Hat would like to thank DarkLight1337, Ga_ryo, Isotr0py, and keymoon for reporting this issue.
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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