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6.5 Medium

vLLM is an inference and serving engine for large language models.

CVE-2026-115911

Summary vLLM renders chat templates by passing the resolved Jinja2 source to tokenizer.applychattemplate(), which compiles and .render()s it in an ImmutableSandboxedEnvironment. The sandbox prevents code-execution escapes but imposes no wall-clock timeout, no output-size cap, and no nested-loop or binop limit. The render runs in a ThreadPoolExecutor whose maxworkers defaults to 1, and CPython worker threads cannot be interrupted. An authenticated API client (or a malicious model repo loaded with trustremotecode=False) can therefore submit a single short template and permanently wedge all chat-style endpoints (/v1/chat/completions, /v1/responses, /tokenize, /pooling-chat, /invocations-chat) for every tenant until the process is restarted. A string-multiplication variant instead OOM-kills the API-server process. Because of a separately-reported (GHSA-jmjw-q4x3-hmc2) gate omission on /v1/responses, the remote authenticated attacker vector works in default vllm serve configuration with no operator opt-in. Impact A single authenticated request of <100 bytes permanently denies all chat-style inference (/v1/chat/completions, /v1/responses, /tokenize, /pooling-chat, /invocations-chat) to…

Part of the AI stack

This affects vLLM, in Inference & serving.

Assessment

CVSS
6.5 (v3.1)
Severity
Medium
EPSS
0.00312% chance of exploitation in the next 30 days
Known exploited
Not listed in CISA KEV
Exploit availability
Nothing found in Exploit-DB, Metasploit, Nuclei or public PoC repositories
Published
26 October 2026
Last updated
30 October 2026
Affected
pip · vllm
Weakness
CWE-400, CWE-770, CWE-834
Assigner
security-advisories@github.com

CVSS vectorCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

At the source

References

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