# CVE-2026-114052: vLLM memory exhaustion in MOSS-Audio model serving

Source: https://junglewise.ai/threats/cve-2026-114052-vllm-after-0-24-0-and-before-0-31-0-contains-a-memory-exhaustion  
Publisher: Junglewise Threat Intelligence, https://junglewise.ai/threats  
Published: 2026-10-21. Updated: 2026-10-21  
License: CC BY 4.0 with a link back

CVE-2026-114052 · Severity: high · CVSS 7.5 · Published 2026-10-21

vLLM is a library that serves large language and multimodal models. An attacker can exploit the MOSS-Audio model serving feature by sending specially crafted requests to the /tokenize endpoint, which bypasses API key authentication, to exhaust the server's memory and crash the API process. This creates a denial-of-service vulnerability that affects any deployment running MOSS-Audio models.

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

## Quick answers

**Is CVE-2026-114052 exploited in the wild?** No. Junglewise Threat Intelligence has no report of CVE-2026-114052 being exploited in the wild.

**How severe is CVE-2026-114052?** High severity, with a CVSS score of 7.5, published 21 October 2026.

**Which products and versions are affected?** vllm (PyPI) ECOSYSTEM:introduced=0.24.0,fixed=0.31.0; vLLM Project vllm after 0.24.0 and before 0.31.0.

**Is there a fix for CVE-2026-114052?** Yes. vllm (PyPI): upgrade to 0.31.0 or later. Junglewise finds every place you run it, drafts the upgrade and applies it once you approve.

**How is CVE-2026-114052 attacked?** It is attacked over the network, in a software library.

**Am I affected by CVE-2026-114052?** Check whether you run vllm (PyPI) or vLLM Project 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 0.31.0 or later

  ```sh
  pip install "vllm>=0.31.0"
  ```


## Technical details

The vulnerability is an unbounded resource allocation issue (CWE-770) in MOSS-Audio model serving where mm_processor_kwargs values from client requests are cached without limits in a private dictionary. Each distinct processor configuration instantiates a WhisperFeatureExtractor that allocates a mel-filter array; attackers can trigger persistent linear memory growth by submitting distinct configurations or amplify single-request allocations with oversized mel_config values. The /tokenize endpoint is intentionally unauthenticated and outside API-key-protected route prefixes, enabling unauthenticated remote exploitation.

## Affected products

- vLLM Project vLLM after 0.24.0 and before 0.31.0

## Identifiers

- CVE-2026-114052
- GHSA-823j-m4cj-hmjf
- PYSEC-2026-4361

## Timeline

- 2026-10-06: disclosed

## References

- [https://github.com/vllm-project/vllm/security/advisories/GHSA-823j-m4cj-hmjf](https://github.com/vllm-project/vllm/security/advisories/GHSA-823j-m4cj-hmjf)
- [https://www.vulncheck.com/advisories/vllm-0.25.0-before-0.31.0-moss-audio-memory-exhaustion-via-tokenize-mm-processor-kwargs](https://www.vulncheck.com/advisories/vllm-0.25.0-before-0.31.0-moss-audio-memory-exhaustion-via-tokenize-mm-processor-kwargs)

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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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