https://db.gcve.eu Contains only the most 10 recent entries. http://www.rssboard.org/rss-specification python-feedgen en Thu, 29 Oct 2026 17:47:54 +0000 https://db.gcve.eu/vuln/fkie_cve-2026-115911 <p>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.apply_chat_template() 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.</p> <p>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.apply_chat_template() 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.</p> https://db.gcve.eu/vuln/fkie_cve-2026-115911 https://db.gcve.eu/vuln/ghsa-542m-9234-8gvf <p><strong>Affected:</strong> PyPI: vllm</p> <p>### Summary vLLM renders chat templates by passing the resolved Jinja2 source to `tokenizer.apply_chat_template()`, 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 `max_workers` defaults to **1**, and CPython worker threads cannot be interrupted. An authenticated API client (or a malicious model repo loaded with `trust_remote_code=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.</p> <p>### Impact A single authenticated request of &lt;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. Fr…</p> <p><strong>Affected:</strong> PyPI: vllm</p> <p>### Summary vLLM renders chat templates by passing the resolved Jinja2 source to `tokenizer.apply_chat_template()`, 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 `max_workers` defaults to **1**, and CPython worker threads cannot be interrupted. An authenticated API client (or a malicious model repo loaded with `trust_remote_code=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.</p> <p>### Impact A single authenticated request of &lt;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. Fr…</p> https://db.gcve.eu/vuln/ghsa-542m-9234-8gvf https://db.gcve.eu/vuln/pysec-2026-4344 <p><strong>Affected:</strong> PyPI: vllm</p> <p>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.apply_chat_template() 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.</p> <p><strong>Affected:</strong> PyPI: vllm</p> <p>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.apply_chat_template() 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.</p> https://db.gcve.eu/vuln/pysec-2026-4344