CVE-2026-0599— Unbounded External Image Fetch in Validation Leads to Resource-Exhaustion DoS in huggingface/text-generation-inference

Unbounded External Image Fetch in Validation Leads to Resource-Exhaustion DoS in huggingface/text-generation-inference

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

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未加控制的资源消耗(资源穷尽)

Text Generation Inference 资源管理错误漏洞

Text Generation Inference是Hugging Face开源的一个用于文本生成推理的 Rust、Python 和 gRPC 服务器。 Text Generation Inference 3.3.6版本存在资源管理错误漏洞，该漏洞源于VLM模式下输入验证期间无限制的外部图像获取，可能导致资源耗尽。

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