PYSEC-2026-4344: vLLM: DoS via unbounded chat_template Jinja2 rendering exhausts the renderer thread pool
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 every tenant of the API-server replica until the operator restarts the process. The string-multiplication variant OOM-kills the process. In a multi-tenant deployment this is a cross-tenant denial-of-service from any one tenant. From the malicious-model angle, a model repo whose tokenizerconfig.json carries the hostile template wedges every replica that loads it on the first legitimate chat request, with trustremotecode=False.
Preconditions: any non-harmony model (modeltype != gptoss); renderernumworkers at its default of 1; for the default-config authenticated API attacker vector, --chat-template must be unset (the default because most HF models ship their own). Blast radius: API-server process, per replica. Engine/worker processes unaffected by the CPU variant (event loop survives); affected by the memory variant (process OOM). Persistence: until process restart. Survives client disconnect.
This advisory was fixed in MR50300.
Other sources
vLLM is an inference and serving engine for large language models. From 0.19.0 until 0.32.0, authenticated callers can supply a request chat template when no server-side --chat-template is configured, or a malicious model repository can provide a template that is compiled and rendered by tokenizer.applychattemplate() without a wall-clock timeout, output-size limit, or iteration bound. Rendering runs in a ThreadPoolExecutor with one worker by default, so a nonterminating render can block /v1/responses and other chat-template users after the request ends, while an output-expanding render can exhaust server memory. Harmony models are not affected because they do not use this Jinja path. This issue is fixed in version 0.32.0.
— OSV
Affected Software
Remediation
Recommended actions to resolve this vulnerability, in priority order.
- Upgrade
Upgrade
pip/vllmto a version that resolves this vulnerability.Fixed in 0.32.0 - Upgrade
Upgrade
vLLMto a version that resolves this vulnerability.Fixed in 0.32.0Patch MR50300 - Operational
Restart the affected API-server process to recover from a wedged chat-style inference service.