# CVE-2026-73559: vLLM completion prompt lists DoS

Source: https://junglewise.ai/threats/cve-2026-73559-vllm-completion-prompt-lists-dos  
Publisher: Junglewise Threat Intelligence, https://junglewise.ai/threats  
Published: 2026-08-13. Updated: 2026-09-24  
License: CC BY 4.0 with a link back

CVE-2026-73559 · Severity: medium · CVSS 6.5 · Published 2026-08-13

vLLM is an open-source framework for serving large language models via an OpenAI-compatible API. The completions endpoint allows authenticated users to submit a list of prompts in a single request without any outer bound on the list size. An attacker can exploit this to craft a single API call containing thousands of prompts, forcing the server to allocate and process an extreme number of backend requests, which can exhaust CPU and memory resources and degrade service availability for other users sharing the same vLLM instance.

Technologies: [vllm (PyPI)](https://junglewise.ai/threats/technologies/pypi-vllm), [Vllm](https://junglewise.ai/threats/technologies/vllm). Vendors: [PyPI](https://junglewise.ai/threats/vendors/pypi), [Vllm](https://junglewise.ai/threats/vendors/vllm).

## Quick answers

**Is CVE-2026-73559 exploited in the wild?** No. Junglewise Threat Intelligence has no report of CVE-2026-73559 being exploited in the wild.

**How severe is CVE-2026-73559?** Medium severity, with a CVSS score of 6.5, published 13 August 2026.

**Which products and versions are affected?** vllm (PyPI) ECOSYSTEM:introduced=0.19.0,fixed=0.26.0.

**Is there a fix for CVE-2026-73559?** Yes. vllm (PyPI): upgrade to 0.26.0 or later. Junglewise finds every place you run it, drafts the upgrade and applies it once you approve.

**How is CVE-2026-73559 attacked?** It is attacked over the network, in a software library.

**Am I affected by CVE-2026-73559?** Check whether you run vllm (PyPI) or Vllm at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

## How to fix

- vllm (PyPI): upgrade to 0.26.0 or later

  ```sh
  pip install "vllm>=0.26.0"
  ```


## Technical details

The vulnerability exists in the `/v1/completions` request handling (vllm/entrypoints/openai/completion/). The `CompletionRequest.prompt` parameter accepts `list[str]`, `list[list[int]]`, or scalar prompt types. The `prompt_to_seq()` function in vllm/renderers/inputs/preprocess.py correctly wraps scalar prompts as single-element lists but passes through list-shaped prompts unchanged. The `OnlineRenderer.preprocess_completion()` method then extends a prompts list with all elements, and the serving path creates one async generator and one response slot per rendered prompt via `self.engine_client.generate()`. No outer prompt-count limit is enforced before rendering or generator creation. An authenticated API client can exploit this by sending a completion request with a `prompt` field containing tens of thousands of short strings or token-id lists, while keeping per-prompt parameters (max_tokens, n) minimal. This causes linear scaling of CPU, memory, async task scheduling, engine request slots, and response buffering. Patches are available in version 0.26.0.

## Affected products

- vLLM Project vLLM >=0.19.0, <0.26.0

## Identifiers

- CVE-2026-73559
- GHSA-87x5-vmc3-756j
- PYSEC-2026-3704

## Timeline

- 2026-07-27: disclosed
- 2026-08-13: patched: patched in version 0.26.0
- 2026-08-13: advisory

## References

- [https://api.github.com/users/rexpository](https://api.github.com/users/rexpository)
- [https://github.com/rexpository](https://github.com/rexpository)
- [https://api.github.com/users/rexpository/gists%7B/gist_id%7D](https://api.github.com/users/rexpository/gists%7B/gist_id%7D)
- [https://api.github.com/users/rexpository/repos](https://api.github.com/users/rexpository/repos)
- [https://avatars.githubusercontent.com/u/30176934?v=4](https://avatars.githubusercontent.com/u/30176934?v=4)
- [https://api.github.com/users/rexpository/events%7B/privacy%7D](https://api.github.com/users/rexpository/events%7B/privacy%7D)

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- [vLLM off-by-one error in tokenizer max_token_id validation](https://junglewise.ai/threats/cve-2026-115916-vllm-is-an-inference-and-serving-engine-for-large-language)
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