7 matches found
CVE-2026-44222
vLLM is an inference and serving engine for large language models LLMs. From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder...
CVE-2026-44222
vLLM is an inference and serving engine for large language models LLMs. From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder...
CVE-2026-44222 vLLM: Remote DoS via Special-Token Placeholders
vLLM is an inference and serving engine for large language models LLMs. From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder...
CVE-2026-44222 vLLM: Remote DoS via Special-Token Placeholders
vLLM is an inference and serving engine for large language models LLMs. From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder...
CVE-2026-44222
vLLM is an inference and serving engine for large language models LLMs. From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder...
CVE-2026-44222
CVE-2026-44222 (vLLM) affects vLLM versions 0.6.1 through 0.19.x where a token-injection vulnerability in multimodal processing allows unauthenticated text prompts containing special tokens to be interpreted as control. When image/video placeholder sequences are provided without corresponding dat...
vLLM 输入验证错误漏洞
vLLM is an open-source inference and service engine designed for LLM models, featuring high throughput and efficient memory usage. Versions of vLLM prior to 0.6.1 to 0.20.0 contained a vulnerability related to input validation errors. This vulnerability stemmed from token injection issues during...