43 matches found
CVE-2025-62426
A vulnerability in vLLM allows an authenticated user to trigger unintended tokenization during chat template processing by supplying crafted chattemplatekwargs to the /v1/chat/completions or /tokenize endpoints. By forcing the server to tokenize very large inputs, an attacker can block the API...
CVE-2025-62426
vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chattemplatekwargs request parameter that is used in the code before it is properly validated against the chat template. With the...
CVE-2025-62426 vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs`
vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chattemplatekwargs request parameter that is used in the code before it is properly validated against the chat template. With the...
CVE-2025-62426
Summary: CVE-2025-62426 affects vLLM up to versions before 0.11.1. The /v1/chat/completions and /tokenize endpoints accept a chat_template_kwargs parameter that is used before validation, allowing an attacker to block the API server by forcing large tokenization tasks and delaying all other reque...
vLLM 安全漏洞
vLLM is a high throughput and memory efficient inference and service engine for LLM from vLLM open source. A security vulnerability exists in vLLM version 0.5.5 through versions prior to 0.11.1, which stems from insufficient validation of the chattemplatekwargs parameter, and may result in API...
vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs`
Summary The /v1/chat/completions and /tokenize endpoints allow a chattemplatekwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chattemplatekwargs parameters, it is possible to block processing of the API server for long...
GHSA-69J4-GRXJ-J64P vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs`
Summary The /v1/chat/completions and /tokenize endpoints allow a chattemplatekwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chattemplatekwargs parameters, it is possible to block processing of the API server for long...
Allocation of Resources Without Limits or Throttling
Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the applyhfchattemplate method. An authenticated user can cause the server to become...
PT-2025-47650
Name of the Vulnerable Software and Affected Versions vLLM versions 0.5.5 through 0.11.0 Description vLLM is an inference and serving engine for large language models LLMs. The /v1/chat/completions and /tokenize API endpoints accept a chat template kwargs request parameter that is not properly...
vLLM: Resource-Exhaustion (DoS) through Malicious Jinja Template in OpenAI-Compatible Server
Summary A resource-exhaustion denial-of-service vulnerability exists in multiple endpoints of the OpenAI-Compatible Server due to the ability to specify Jinja templates via the chattemplate and chattemplatekwargs parameters. If an attacker can supply these parameters to the API, they can cause a...
GHSA-6FVQ-23CW-5628 vLLM: Resource-Exhaustion (DoS) through Malicious Jinja Template in OpenAI-Compatible Server
Summary A resource-exhaustion denial-of-service vulnerability exists in multiple endpoints of the OpenAI-Compatible Server due to the ability to specify Jinja templates via the chattemplate and chattemplatekwargs parameters. If an attacker can supply these parameters to the API, they can cause a...
Allocation of Resources Without Limits or Throttling
Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling through the chattemplate and chattemplatekwargs parameters. An attacker can cause excessive CPU...
EUVD-2023-53217
Malicious code in bioql PyPI...
Malicious code in dpais-chat-template-pwa (npm)
The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ossf-package-analysis 4d39545ec9be8c0beabaaf1f508b0f468a884898b3fb8b8ae6d73b5cb62c54fc The OpenSSF Package Analysis project identified...
MAL-2025-6923 Malicious code in dpais-chat-template-pwa (npm)
The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ossf-package-analysis 4d39545ec9be8c0beabaaf1f508b0f468a884898b3fb8b8ae6d73b5cb62c54fc The OpenSSF Package Analysis project identified...
CVE-2024-34359
llama-cpp-python is the Python bindings for llama.cpp. llama-cpp-python depends on class Llama in llama.py to load .gguf llama.cpp or Latency Machine Learning Models. The init constructor built in the Llama takes several parameters to configure the loading and running of the model. Other than NUM...
CVE-2024-34359
CVE-2024-34359 affects llama-cpp-python (Python bindings for llama.cpp). The vulnerability arises when init loads a model’s chat template from the gguf metadata and constructs self.chat_handler via llama_chat_format.Jinja2ChatFormatter.to_chat_handler(), using a sandbox-less Jinja2 Environment. R...
PT-2024-22934
Name of the Vulnerable Software and Affected Versions BerriAI/litellm affected versions not specified Description The issue arises from the hf chat template method processing the chat template parameter from the tokenizer config.json file through the Jinja template engine without proper...
