etwithin/keras-scanner-bypass-poc

Keras Native (.keras) Scanner Bypass PoC

Vulnerability

A malicious .keras file achieves Arbitrary Code Execution when loaded via keras.models.load_model(safe_mode=False) or after calling keras.config.enable_unsafe_deserialization(), while bypassing modelscan 0.8.8.

Scanner Bypass Details

modelscan's KerasLambdaDetectScan only checks for class_name == "Lambda" in the top-level config.layers list. It does NOT detect:

1. 1.__lambda__ configs embedded in other layer parameters (e.g., Dense activation)
2. 2.Lambda layers nested inside container models (Sequential/Functional)

This PoC hides a __lambda__ ACE payload inside a Dense layer's activation field. The scanner sees class_name: "Dense" and passes it.

Reproduction

import keras

# Method 1: explicit safe_mode=False
model = keras.models.load_model("malicious_model.keras", safe_mode=False)

# Method 2: enable_unsafe_deserialization (commonly used)
keras.config.enable_unsafe_deserialization()
model = keras.models.load_model("malicious_model.keras")

# Trigger ACE on inference
import numpy as np
model.predict(np.random.randn(1, 10))
# Creates /tmp/keras_ace_proof.txt

Scanner Results

modelscan -p malicious_model.keras
# No issues found!

Impact

Users who trust modelscan to detect malicious Keras models are vulnerable. The enable_unsafe_deserialization() call is extremely common in ML pipelines and is recommended by Keras's own error messages.

Versions

• —Keras 3.13.2
• —modelscan 0.8.8
• —Python 3.12