Instructions to use chirper/rwkv7-rce-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use chirper/rwkv7-rce-poc with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://chirper/rwkv7-rce-poc") - KerasHub
How to use chirper/rwkv7-rce-poc with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://chirper/rwkv7-rce-poc") - Keras
How to use chirper/rwkv7-rce-poc with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://chirper/rwkv7-rce-poc") - Notebooks
- Google Colab
- Kaggle
RWKV7 Security Research PoC
⚠️ This is a proof-of-concept for responsible security disclosure. Do not run untrusted models.
This repository contains a minimal .keras artifact used to demonstrate a deserialization vulnerability in keras-hub RWKVTokenizer (keras-hub==0.26.0).
What this demonstrates
Loading the model with keras.models.load_model() triggers code execution via eval() while parsing tokenizer vocabulary lines.
For safety, the payload is intentionally harmless and only runs:
touch /tmp/pwned
Reproduction
import keras
# safe_mode=True does NOT prevent this issue
obj = keras.models.load_model("poc_for_upload.keras", safe_mode=True)
After loading, /tmp/pwned will exist on the local machine.
Security note
This PoC is provided strictly for coordinated vulnerability disclosure and patch validation.
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