GHSA-3m4q-jmj6-r34q Keras has a Local File Disclosure via HDF5 External Storage During Keras Weight Loading
SummaryTensorFlow / Keras continues to honor HDF5 “external storage” and `ExternalLink` features when loading weights. A malicious `.weights.h5` (or a `.keras` archive embedding such weights) can direct `load_weights()` to read from an arbitrary readable filesystem path. The bytes pulled from that path populate model tensors and become observable through inference or subsequent re-save operations. Keras “safe mode” only guards object deserialization and does not cover weight I/O, so this behaviour persists even with safe mode enabled. The issue is confirmed on the latest publicly released stack (`tensorflow 2.20.0`, `keras 3.11.3`, `h5py 3.15.1`, `numpy 2.3.4`).Impact**Class**: CWE-200 (Exposure of Sensitive Information), CWE-73 (External Control of File Name or Path)**What leaks**: Contents of any readable file on the host (e.g., `/etc/hosts`, `/etc/passwd`, `/etc/hostname`).**Visibility**: Secrets appear in model outputs (e.g., Dense layer bias) or get embedded into newly saved artifacts.**Prerequisites**: Victim executes `model.load_weights()` or `tf.keras.models.load_model()` on an attacker-supplied HDF5 weights file or `.keras` archive.**Scope**: Applies to modern Keras (3.x) and TensorFlow 2.x lines; legacy HDF5 paths remain susceptible.Attacker Scenario**Initial foothold**: The attacker convinces a user (or CI automation) to consume a weight artifact—perhaps by publishing a pre-trained model, contributing to an open-source repository, or attaching weights to a bug report.**Crafted payload**: The artifact bundles innocuous model metadata but rewrites one or more datasets to use HDF5 external storage or external links pointing at sensitive files on the victim host (e.g., `/home/<user>/.ssh/id_rsa`, `/etc/shadow` if readable, configuration files containing API keys, etc.).**Execution**: The victim calls `model.load_weights()` (or `tf.keras.models.load_model()` for `.keras` archives). HDF5 follows the external references, opens the targeted host file, and streams its bytes into the model tensors.**Exfiltration vectors**:Running inference on controlled inputs (e.g., zero vectors) yields outputs equal to the injected weights; the attacker or downstream consumer can read the leaked data.Re-saving the model (weights or `.keras` archive) persists the secret into a new artifact, which may later be shared publicly or uploaded to a model registry.If the victim pushes the re-saved artifact to source control or a package repository, the attacker retrieves the captured data without needing continued access to the victim environment.Additional PreconditionsThe target file must exist and be readable by the process running TensorFlow/Keras.Safe mode (`load_model(..., safe_mode=True)`) does not mitigate the issue because the attack path is weight loading rather than object/lambda deserialization.Environments with strict filesystem permissioning or sandboxing (e.g., container runtime blocking access to `/etc/hostname`) can reduce impact, but common defaults expose a broad set of host files.Environment Used for Verification (2025‑10‑19)OS: Debian-based container running Python 3.11.Packages (installed via `python -m pip install -U ...`):`tensorflow==2.20.0``keras==3.11.3``h5py==3.15.1``numpy==2.3.4`Tooling: `strace` (for syscall tracing), `pip` upgraded to latest before installs.Debug flags: `PYTHONFAULTHANDLER=1`, `TF_CPP_MIN_LOG_LEVEL=0` during instrumentation to capture verbose logs if needed.Reproduction Instructions (Weights-Only PoC)Ensure the environment above (or equivalent) is prepared.Save the following script as `weights_external_demo.py`:Execute `python weights_external_demo.py`.Observe:`secret_text_source` prints the chosen host file path.`recovered_ascii`/`recovered_hex64` display the file contents recovered via model inference.A re-saved weights file contains the leaked bytes inside the artifact.Expanded Validation (Multiple Attack Scenarios)The following test harness generalises the attack for multiple HDF5 constructs:Build a minimal feed-forward model and baseline weights.Create three malicious variants:**External storage dataset**: dataset references `/etc/hosts`.**External link**: `ExternalLink` pointing at `/etc/passwd`.**Indirect link**: external storage referencing a helper HDF5 that, in turn, refers to `/etc/hostname`.Run each scenario under `strace -f -e trace=open,openat,read` while calling `model.load_weights(...)`.Post-process traces and weight tensors to show the exact bytes loaded.Relevant syscall excerpts captured during the run:The corresponding model weight bytes (converted to ASCII) mirrored these file contents, confirming successful exfiltration in every case.Recommended Product Fix**Default-deny external datasets/links**:Inspect creation property lists (`get_external_count`) before materialising tensors.Resolve `SoftLink` / `ExternalLink` targets and block if they leave the HDF5 file.**Provide an escape hatch**:Offer an explicit `allow_external_data=True` flag or environment variable for advanced users who truly rely on HDF5 external storage.**Documentation**:Update security guidance and API docs to clarify that weight loading bypasses safe mode and that external HDF5 references are rejected by default.**Regression coverage**:Add automated tests mirroring the scenarios above to ensure future refactors do not reintroduce the issue.WorkaroundsAvoid loading untrusted HDF5 weight files.Pre-scan weight files using `h5py` to detect external datasets or links before invoking Keras loaders.Prefer alternate formats (e.g., NumPy `.npz`) that lack external reference capabilities when exchanging weights.If isolation is unavoidable, run the load inside a sandboxed environment with limited filesystem access.Timeline (UTC)**2025‑10‑18**: Initial proof against TensorFlow 2.12.0 confirmed local file disclosure.**2025‑10‑19**: Re-validated on TensorFlow 2.20.0 / Keras 3.11.3 with syscall tracing; produced weight artifacts and JSON summaries for each malicious scenario; implemented `safe_keras_hdf5.py` prototype guard.
CVSS v3.1 score: 7.1