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Josh HawkinsandGitHub
d4ebe64fde
* check go2rtc stream sources on the internal port
auth() returned admin for internal port requests before the go2rtc src check ran, so anyone who could reach port 5000 could send an unknown src to /api/ws, and go2rtc created a stream from it and started its producer, including ffmpeg sources with #raw= arguments. The internal port now rejects any src that isn't an existing stream, the same as the external port, and still skips the per-camera role checks.
* reject go2rtc stream names that can inject transcode parameters
live.transcode.source names a go2rtc stream that is spliced into an ffmpeg:{name}#video=h264 source, and stream names were never validated, so a stream named with #raw= parameters, whitespace, quotes or a ${VAR} reference added its own ffmpeg arguments. New stream names with these characters are rejected through the API, transcode.source fails validation when its stream name has them, and startup skips the generated transcode stream.
* only export allowlisted environment_vars
apply_config_env_vars wrote every unprefixed environment_vars key into os.environ and exported every FRIGATE_ key, so a config could set LD_PRELOAD or LIGHTER_ANE_EP to load a library into Frigate or its children, or set FRIGATE_RUN_AS_ROOT to turn off the ffmpeg.path writable-tree guard. Only the documented names are now exported, each with a value check, and a name the container already sets is never overwritten. FRIGATE_ keys still feed substitution, but only FRIGATE_JWT_SECRET is exported. Anything else logs a warning naming the key.
* don't trust live stream names as existing go2rtc streams
Frigate NVR™ - Realtime Object Detection for IP Cameras
[English] | 简体中文
A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported object detectors.
- Tight integration with Home Assistant via a custom component
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
- Uses a very low overhead motion detection to determine where to run object detection
- Object detection with TensorFlow runs in separate processes for maximum FPS
- Communicates over MQTT for easy integration into other systems
- Records video with retention settings based on detected objects
- 24/7 recording
- Re-streaming via RTSP to reduce the number of connections to your camera
- WebRTC & MSE support for low-latency live view
Documentation
View the documentation at https://docs.frigate.video
Donations
If you would like to make a donation to support development, please use Github Sponsors.
License
This project is licensed under the MIT License.
- Code: The source code, configuration files, and documentation in this repository are available under the MIT License. You are free to use, modify, and distribute the code as long as you include the original copyright notice.
- Trademarks: The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are trademarks of Frigate, Inc. and are not covered by the MIT License.
Please see our Trademark Policy for details on acceptable use of our brand assets.
Screenshots
Live dashboard
Streamlined review workflow
Multi-camera scrubbing
Built-in mask and zone editor
Translations
We use Weblate to support language translations. Contributions are always welcome.
Copyright © 2026 Frigate, Inc.