VSS Engine
Linux / amd64
Build a Video Search and Summarization Agent Ingest massive volumes of live or archived videos and extract insights for summarization and interactive Q&A
For VSS deployment instructions, please check: NVIDIA Blueprint VSS Helm Chart
Deployment Note
The Video Search and Summarization Blueprint is shared as reference and is provided "as is". The security in the production environment is the responsibility of the end users deploying it. When deploying in a production environment, please have security experts review any potential risks and threats; define the trust boundaries, implement logging and monitoring capabilities, secure the communication channels, integrate AuthN & AuthZ with appropriate access controls, keep the deployment up to date, ensure the containers/source code are secure and free of known vulnerabilities. The end users are also responsible for ensuring integrity and authenticity of the models and containers.
Known CVEs
VSS Engine 2.3.0 Container has the following known CVEs:
| CVE | Description |
|---|---|
| CVE-2024-8966 | This impacts gradio <= 5.22.0 python package, This impacts the file upload functionality of Gradio UI where an attacker can cause Denial-of-Service (DoS) attack by appending a large number of characters to the end of a multipart boundary. This affects the Gradio UI of VSS. |
| CVE-2025-32434 | This impacts the torch v2.51.0 python package. This impacts loading of saved model weights from a tar file using torch.load() API which can result in remote code execution in case of malicious weights. The default weights for the models used by VSS are in safetensors format and are not affected by this vulnerability since torch.load() is not used. However, users must ensure safety of the weights if using other formats. |
VSS Engine 2.3.0 Source Code has the following known CVEs:
| CVE | Description |
|---|---|
| CVE-2024-7246 | This affects the gRPC python package. It's possible for a gRPC client communicating with a HTTP/2 proxy to poison the HPACK table between the proxy and the backend such that other clients see failed requests. By default, VSS does not use a HTTP/2 proxy. |
| CVE-2024-27444 | This issue is reported for langchain-milvus 0.1.5 dependency on older langchain version 0.1.5. However, VSS explicitly uses langchain 0.3.3 and hence is not applicable. |
| CVE-2024-28088 | This issue is reported for langchain-milvus 0.1.5 dependency on older langchain version 0.1.5. However, VSS explicitly uses langchain 0.3.3 and hence is not applicable. |
| CVE-2024-38459 | This issue is reported for langchain-milvus 0.1.5 dependency on older langchain version 0.1.5. However, VSS explicitly uses langchain 0.3.3 and hence is not applicable. |
VSS 2.2.0 (Previous Release) has the following known CVEs:
| CVE | Description |
|---|---|
| CVE-2024-11393 | This impacts the transformers v4.47.0 python package. This impacts the Hugging Face Transformers MaskFormer Model Deserialization and allows remote attackers to execute arbitrary code. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. However, this does not affect VSS since MaskFormer model is not used in VSS. |
| CVE-2024-11392 | This impacts the transformers v4.47.0 python package. This impacts the Hugging Face Transformers MobileViTV2 Model Deserialization and allows remote attackers to execute arbitrary code. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. However, this does not affect VSS since MobileViTV2 model is not used in VSS. |
| CVE-2024-11394 | This impacts the transformers v4.47.0 python package. This impacts the Hugging Face Transformers Trax Model Deserialization and allows remote attackers to execute arbitrary code. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. However, this does not affect VSS since Trax model is not used in VSS. |
GOVERNING TERMS
The software and materials are governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products, except for models which are governed by the NVIDIA Community Model License.
Additional information: Llama 3.1 Community License Agreement for Llama-3.1-70b-instruct; Llama 3.2 Community License Agreement for NVIDIA Retrieval QA Llama 3.2 1B Embedding v2 and NVIDIA Retrieval QA Llama 3.2 1B Reranking v2; Apache License, Version 2.0 for https://github.com/google-research/big_vision/blob/main/LICENSE and Apache License, Version 2.0 for https://github.com/01-ai/Yi/blob/main/LICENSE. Built with Llama.