---
license: apache-2.0
tags:
- security
- coordinated-disclosure
- modelscan
---

# Benign PoC — modelscan Keras positional Lambda-scan bypass (compiled loss)

Coordinated-disclosure proof-of-concept for Protect AI `modelscan`, submitted via huntr (Model File Vulnerability).

modelscan's Keras/H5 Lambda detection (`KerasLambdaDetectScan` / `H5LambdaDetectScan`) is **positional**:
it flags only a top-level layer with `class_name=="Lambda"`. A marshalled-bytecode lambda serialized as
the model's compiled **loss** (in `compile_config`, which the DEFAULT `load_model(compile=True)`
deserializes) is never inspected — so a model that executes attacker bytecode on load+evaluate scans **clean**.

- `evil_loss.keras`      — lambda in `compile_config.loss` → modelscan: **No issues found**  (the bypass)
- `control_lambda.keras` — the **identical** benign function as a Lambda **layer** → modelscan: **MEDIUM `Lambda`**  (control)

## Reproduce

```
pip install tensorflow modelscan
modelscan -p evil_loss.keras        # -> No issues found            (bypass)
modelscan -p control_lambda.keras   # -> MEDIUM 'Lambda' from Keras  (same function, caught as a layer)

python - <<'PY'
import keras, numpy as np
keras.config.enable_unsafe_deserialization()
m = keras.models.load_model("evil_loss.keras", safe_mode=False, compile=True)
m.evaluate(np.zeros((2,4)), np.zeros((2,1)))     # -> writes /tmp/celvex_mfv_keras_marker.txt
PY
```

The lambda reconstructs from marshalled bytecode and runs when the loss is invoked (evaluate/fit) after a
default `load_model(compile=True, safe_mode=False)` — the same safe-mode premise as the accepted
Lambda-layer class, but from a config position modelscan does not scan.

**Strictly benign:** the lambda only writes a marker string (`CELVEX-MFV-KERAS-LOSS-MARKER`). No
weaponization. Validated against modelscan 0.8.8 + tensorflow/keras 3.15.0.