彙總 pytorch 相關全部產品的 CVE 與安全漏洞情報，包括 CVSS、EPSS、公開時間與漏洞情報資料。
歷史漏洞主要涉及 SSRF與路徑處理缺陷 等問題，部分漏洞可能導致 檔案覆寫，並影響 軟體部署與生產負載 相關場景。
相關漏洞資料主要來源於公開漏洞披露與安全公告，可用於評估歷史漏洞暴露面與修補優先順序。
| CVE | 摘要 | 來源 | 最高 CVSS | EPSS % | 公開時間 | 更新時間 |
|---|---|---|---|---|---|---|
| CVE-2024-35199 | TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production. In affected versions the two gRPC ports 7070 and 7071, are not bound to [localhost](http://localhost/) by default, so when TorchServe is launched, these two interfaces are bound to all interfaces. Customers using PyTorch inference Deep Learning Containers (DLC) through Amazon SageMaker and EKS are not affected. This issue in TorchServe has been fixed in PR #3083. TorchServe release 0.11.0 includes | [email protected] | 8.2 | 0.63% | 2024-07-19 | 2025-09-04 |
| CVE-2024-35198 | TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production. TorchServe 's check on allowed_urls configuration can be by-passed if the URL contains characters such as ".." but it does not prevent the model from being downloaded into the model store. Once a file is downloaded, it can be referenced without providing a URL the second time, which effectively bypasses the allowed_urls security check. Customers using PyTorch inference Deep Learning Containers (DL | [email protected] | 9.8 | 0.79% | 2024-07-19 | 2025-09-04 |
| CVE-2023-48299 | TorchServe is a tool for serving and scaling PyTorch models in production. Starting in version 0.1.0 and prior to version 0.9.0, using the model/workflow management API, there is a chance of uploading potentially harmful archives that contain files that are extracted to any location on the filesystem that is within the process permissions. Leveraging this issue could aid third-party actors in hiding harmful code in open-source/public models, which can be downloaded from the internet, and take ad | [email protected] | 5.3 | 0.67% | 2023-11-21 | 2024-11-21 |
| CVE-2023-43654 | TorchServe is a tool for serving and scaling PyTorch models in production. TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions 0.1.0 to 0.8.1. A user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is r | [email protected] | 10.0 | 35.26% | 2023-09-28 | 2024-11-21 |