diff --git a/.github/workflows/bandit.yml b/.github/workflows/bandit.yml new file mode 100644 index 0000000..9f870cd --- /dev/null +++ b/.github/workflows/bandit.yml @@ -0,0 +1,33 @@ +name: Bandit + +on: + push: + branches: main + pull_request: + branches: "*" + +jobs: + bandit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: actions/setup-python@v2 + with: + python-version: "3.8" + - uses: snok/install-poetry@v1 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + - name: Load cached venv + id: cached-poetry-dependencies + uses: actions/cache@v3 + with: + path: .venv + key: venv-test-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} + - name: Install Dependencies + if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' + run: | + poetry install --with test --no-root + - name: Run Bandit + run: poetry run bandit -c pyproject.toml -r $(git ls-files '*.py') diff --git a/.github/workflows/black.yml b/.github/workflows/black.yml new file mode 100644 index 0000000..d00384b --- /dev/null +++ b/.github/workflows/black.yml @@ -0,0 +1,16 @@ +name: Lint with Black + +on: + push: + branches: main + pull_request: + branches: "*" + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: psf/black@stable + with: + version: "22.8.0" diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml new file mode 100644 index 0000000..8610e19 --- /dev/null +++ b/.github/workflows/build.yml @@ -0,0 +1,47 @@ +name: Build + +on: + push: + branches: main + pull_request: + branches: "*" + +permissions: + id-token: write + contents: read + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 0 # Necessary to get tags + - uses: actions/setup-python@v2 + with: + python-version: "3.8" + - uses: snok/install-poetry@v1 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + - name: Load cached venv + id: cached-poetry-dependencies + uses: actions/cache@v3 + with: + path: .venv + key: venv-prod-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} + - uses: mtkennerly/dunamai-action@v1 + with: + env-var: NBD_VERSION + args: --style pep440 --format "{base}.dev{distance}+{commit}" + - name: Install Dependencies + if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' + run: | + make install-prod + - name: Build Package + run: | + make build-prod + - name: PYPI Publish Dry Run + run: | + poetry publish --dry-run diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml new file mode 100644 index 0000000..51cfc87 --- /dev/null +++ b/.github/workflows/mypy.yml @@ -0,0 +1,34 @@ +name: MYPY + +on: + push: + branches: main + pull_request: + branches: "*" + +jobs: + mypy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: actions/setup-python@v2 + with: + python-version: "3.8" + - uses: snok/install-poetry@v1 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + - name: Load cached venv + id: cached-poetry-dependencies + uses: actions/cache@v3 + with: + path: .venv + key: venv-test-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} + - name: Install Dependencies + if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' + run: | + poetry install --with test --no-root + - name: Run MYPY + run: | + poetry run mypy --ignore-missing-imports --strict --check-untyped-defs $(git ls-files '*.py') diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000..c05d5f8 --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,43 @@ +name: Build and Publish Release to PYPI + +on: + push: + tags: + - v* + +jobs: + publish-modelscan: + runs-on: ubuntu-latest + permissions: + contents: write + pull-requests: write + + steps: + - name: Checkout + uses: actions/checkout@v2 + with: + fetch-depth: 0 # Necessary to get tags + - uses: actions/setup-python@v2 + with: + python-version: "3.8" + - uses: snok/install-poetry@v1 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + - name: Get Release Version + uses: mtkennerly/dunamai-action@v1 + with: + env-var: MODELSCAN_VERSION + args: --style semver --format "{base}" + - name: Set Package Version + run: | + echo "__version__ = '$MODELSCAN_VERSION'" > modelscan/_version.py + poetry version $MODELSCAN_VERSION + - name: Build Package + run: | + poetry build + - name: Publish Package to PYPI + run: | + poetry config pypi-token.pypi ${{ secrets.MODELSCAN_PYPI_API_TOKEN }} + poetry publish diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000..32d2f44 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,39 @@ +name: Test + +on: + push: + branches: main + pull_request: + branches: "*" + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.8", "3.9", "3.10"] + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - uses: snok/install-poetry@v1 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + - name: Load cached venv + id: cached-poetry-dependencies + uses: actions/cache@v3 + with: + path: .venv + key: venv-test-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} + - name: Install Dependencies + if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' + run: | + poetry install --with test + - name: Run Tests + run: | + make test diff --git a/.gitignore b/.gitignore index 68bc17f..bffd112 100644 --- a/.gitignore +++ b/.gitignore @@ -82,33 +82,6 @@ target/ profile_default/ ipython_config.py -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/#use-with-ide -.pdm.toml - # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ @@ -152,9 +125,6 @@ dmypy.json # Cython debug symbols cython_debug/ -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ +.DS_Store + +.vscode/ \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..ad7accb --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + - repo: https://github.com/psf/black + rev: 22.8.0 + hooks: + - id: black + - repo: https://github.com/python-poetry/poetry + rev: "1.4.0" + hooks: + - id: poetry-check # Makes sure poetry config is valid + - id: poetry-lock # Makes sure lock file is up to date + args: ["--check"] + - repo: https://github.com/PyCQA/bandit + rev: "1.7.5" + hooks: + - id: bandit + args: ["-c", "pyproject.toml"] + additional_dependencies: ["bandit[toml]"] + - repo: https://github.com/pre-commit/mirrors-mypy + rev: "v1.4.1" + hooks: + - id: mypy + args: ["--ignore-missing-imports", "--strict", "--check-untyped-defs"] + additional_dependencies: ["click>=8.1.3","numpy==1.24.0"] diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..7b9f0dd --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,46 @@ +# 👩‍💻 CONTRIBUTING + +Welcome! We're glad to have you. If you would like to report a bug, request a new feature or enhancement, follow [this link](to-be-updated-in-new-repo) for more help. + +## ❗️ Requirements + +1. Python + + `modelscan` requires python version `>=3.8` and `<4.0` + +2. Poetry + + The following install commands require [Poetry](https://python-poetry.org/). To install Poetry you can follow [this installation guide](https://python-poetry.org/docs/#installation). Poetry can also be installed with brew using the command `brew install poetry`. + +## 💪 Developing with modelscan + +1. Clone the repo + + ```bash + git clone git@github.com:protectai/modelscan.git + ``` + +2. To install development dependencies to your environment and set up the cli for live updates, run the following command in the root of the `modelscan` directory: + + ```bash + make install-dev + ``` + +3. You are now ready to start developing! + + Run a scan with the cli with the following command: + + ```bash + modelscan --huggingface ykilcher/totally-harmless-model + ``` + +## 📝 Submitting Changes + +Thanks for contributing! In order to open a PR into the `modelscan` project, you'll have to follow these steps: + +1. Fork the repo and clone your fork locally +2. Run `make install-dev` from the root of your forked repo to setup your environment +3. Make your changes +4. Submit a pull request + +After these steps have been completed, someone on our team at Protect AI will review the code and help merge in your changes! \ No newline at end of file diff --git a/LICENSE b/LICENSE index 261eeb9..88eddcc 100644 --- a/LICENSE +++ b/LICENSE @@ -175,18 +175,7 @@ END OF TERMS AND CONDITIONS - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] + Copyright 2023 Protect AI Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -198,4 +187,4 @@ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and - limitations under the License. + limitations under the License. \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..cf5c275 --- /dev/null +++ b/Makefile @@ -0,0 +1,39 @@ +VERSION ?= $(shell dunamai from git --style pep440 --format "{base}.dev{distance}+{commit}") + +install-dev: + poetry install --with dev --extras "tensorflow h5py" + pre-commit install + +install: + poetry install + +install-prod: + poetry install --with prod + +clean: + pip uninstall modelscan + +test: + poetry run pytest + +build: + poetry build + +build-prod: version + poetry build + +version: + echo "__version__ = '$(VERSION)'" > modelscan/_version.py + poetry version $(VERSION) + +lint: bandit mypy + +bandit: + poetry run bandit -c pyproject.toml -r . + +mypy: + poetry run mypy --ignore-missing-imports --strict --check-untyped-defs . + +format: + black . + diff --git a/README.md b/README.md index 1aba670..3e6e37f 100644 --- a/README.md +++ b/README.md @@ -1 +1,132 @@ -# model-scanner \ No newline at end of file + +# modelscan +

+ + modelscan logo + + +```python +# malicious code injection +command = "system" +malicious_code = """cat ~/.aws/secrets""" +``` + + + modelscan logo + + +

+
+ +

modelscan is an open-source tool for scanning Machine Learning (ML) models. With modelscan, the ML models can be scanned *without* loading them in your machines: saving you from potential malicious code injection attacks.

+ +

+ +

+ + modelscan logo + + +

+ +

+ +## How modelscan works +

+ +

+ + Appsmith Logo + +
+Fig 1: An outline for scanning models using modelscan. +

+
+ +TODO: Add a gif here like NBDefense to show how modelscan works- example notebook from pytorch + +

+ +## Getting Started +1. Install modelscan: + + ```shell + pip install modelscan + ``` + +2. Scan the model: + + For scanning model from local directory: + + ```shell + modelscan -p /path/to/model_file + ``` + + For scanning model from huggingface: + + ```shell + modelscan -hf /repo_id/model_file + ``` + +3. Inspect the modelscan result: + + The modelscan results include: + + - List of files scanned. + - List of files _not_ scanned. + - A summery of scan results categorized using modelscan severity levels of: CRITICAL, HIGH, MEDIUM, and LOW. + - A detailed list under each severity level of the malicious code found. + + More information on which ML models will be scanned using modelscan can be found [here](#which-ml-models-can-be-scanned-using-modelscan) + + More information about modelscan severity levels can be found [here](docs/SeverityLevels.md). + + + + +

+ +## [Which ML Models can be Scanned using modelscan](#which-ml-models-can-be-scanned-using-modelscan) +At the moment, modelscan supports the following ML libraries. +

+### PyTorch + +Pytorch models can be saved and loaded using pickle. modelscan can scan models saved using pickle. A notebook to illustarate the modelscan usage and expected results with pytorch model is included in ./examples folder. [TODO] +

+### Tensorflow + +Tensorflow uses saved_model for model serialization. modelscan can scan models saved using saved_model. A notebook to illustarate the modelscan usage and expected results with tensorflow model is included in ./examples folder. [TODO] +

+### Keras +Keras uses saved_model and h5 for model serialization. modelscan can scan models saved using saved_model and h5. A notebook to illustarate the modelscan usage and expected results with keras model is included in ./examples folder. [TODO] + +

+### Classical ML libraries +modelscan also supports all ML libraries that support pickle for their model serialization, such as Sklearn, XGBoost, Catboost etc. A notebook to illustarate the modelscan usage and expected results with keras model is included in ./examples folder. [TODO] + + + +

+## Example Notebooks + +TODO + +

+ +## modelscan CLI arguments: + +The modelscan CLI arguments and their usage is as follows: + +| argument | Exaplanation| Usage +| ----| ----| ----| +| -h or --help | For getting help | ```modelscan -h ``` +| -p or --path | For scanning a model file in local directory | ```modelscan -p /path/to/model_file``` +| -hf or --huggingface | For scanning a model file on hugging face| ```modelscan -hf /repo/model_file``` + +

+ +## Contributing + +We would love to have you contribute to our open source modelscan project. If you would like to contribute, please follow the details on [Contribution page](./CONTRIBUTING.md). + + \ No newline at end of file diff --git a/modelscan/__init__.py b/modelscan/__init__.py new file mode 100644 index 0000000..b420341 --- /dev/null +++ b/modelscan/__init__.py @@ -0,0 +1,6 @@ +"""CLI for scanning models""" +import logging + +from modelscan._version import __version__ + +logging.getLogger("modelscan").addHandler(logging.NullHandler()) diff --git a/modelscan/_version.py b/modelscan/_version.py new file mode 100644 index 0000000..6c8e6b9 --- /dev/null +++ b/modelscan/_version.py @@ -0,0 +1 @@ +__version__ = "0.0.0" diff --git a/modelscan/cli.py b/modelscan/cli.py new file mode 100644 index 0000000..e4f6fd3 --- /dev/null +++ b/modelscan/cli.py @@ -0,0 +1,90 @@ +import logging +import sys +from pathlib import Path +from typing import Optional + +import click + +from modelscan.modelscan import Modelscan +from modelscan.reports import ConsoleReport + +logger = logging.getLogger("modelscan") + + +# @click.group() +# @click.pass_context +@click.command( + help="Modelscan detects machine learning model files that perform suspicious actions" +) +@click.option( + "-p", + "--path", + type=click.Path(exists=True), + default=None, + help="Path to the file or folder to scan", +) +@click.option( + "-u", "--url", type=str, default=None, help="URL to the file or folder to scan" +) +@click.option( + "-hf", + "--huggingface", + type=str, + default=None, + help="Name of the Hugging Face model to scan", +) +@click.option( + "-l", + "--log", + type=click.Choice(["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG"]), + default="INFO", + help="level of log messages to display (default: INFO)", +) +@click.pass_context +def cli( + ctx: click.Context, + log: str, + url: Optional[str], + huggingface: Optional[str], + path: Optional[str], +) -> int: + logger.setLevel(logging.INFO) + logger.addHandler(logging.StreamHandler(stream=sys.stdout)) + + if log is not None: + logger.setLevel(getattr(logging, log)) + + try: + modelscan = Modelscan() + if path is not None: + if path == ".": + pathlibPath = Path().cwd() + else: + pathlibPath = Path(path).absolute() + if not pathlibPath.exists(): + raise FileNotFoundError(f"Path {path} does not exist") + else: + modelscan.scan_path(pathlibPath) + elif url is not None: + modelscan.scan_url(url) + elif huggingface is not None: + modelscan.scan_huggingface_model(huggingface) + else: + raise click.UsageError( + "Command line must include either a path, a URL, or a Hugging Face model" + ) + ConsoleReport.generate(modelscan.issues, modelscan.errors) + return 0 + + except click.UsageError as e: + click.echo(e) + click.echo(ctx.get_help()) + return 2 + + except Exception as e: + logger.exception(f"Exception: {e}") + return 2 + + +if __name__ == "__main__": + sys.exit(cli()) diff --git a/modelscan/error.py b/modelscan/error.py new file mode 100644 index 0000000..7f4739b --- /dev/null +++ b/modelscan/error.py @@ -0,0 +1,21 @@ +from typing import Optional + + +class Error: + def __init__(self) -> None: + pass + + def __str__(self) -> str: + raise NotImplementedError() + + +class ModelScanError(Error): + scan_name: str + message: Optional[str] + + def __init__(self, scan_name: str, message: Optional[str] = None) -> None: + self.scan_name = scan_name + self.message = message if message else "None" + + def __str__(self) -> str: + return f"The following error was raised during a {self.scan_name} scan: \n{self.message}" diff --git a/modelscan/issues.py b/modelscan/issues.py new file mode 100644 index 0000000..770f70f --- /dev/null +++ b/modelscan/issues.py @@ -0,0 +1,100 @@ +import abc +import logging +from enum import Enum +from pathlib import Path +from typing import List, Union, Dict + +from collections import defaultdict + +logger = logging.getLogger("modelscan") + + +class IssueSeverity(Enum): + LOW = 1 + MEDIUM = 2 + HIGH = 3 + CRITICAL = 4 + + +class IssueCode(Enum): + UNSAFE_OPERATOR = 1 + + +class IssueDetails(metaclass=abc.ABCMeta): + @abc.abstractmethod + def output_lines(self) -> List[str]: + raise NotImplemented + + +class Issue: + """ + Defines properties of a issue + """ + + def __init__( + self, + code: IssueCode, + severity: IssueSeverity, + details: IssueDetails, + ) -> None: + """ + Create a issue with given information + + :param code: Code of the issue from the issue code enum. + :param severity: The severity level of the issue from Severity enum. + :param details: An implementation of the IssueDetails object. + """ + self.code = code + self.severity = severity + self.details = details + + def print(self) -> None: + issue_description = self.code.name + if self.code == IssueCode.UNSAFE_OPERATOR: + issue_description = "Unsafe operator" + else: + logger.error(f"No issue description for issue code ${self.code}") + + print(f"\n{issue_description} found:") + print(f" - Severity: {self.severity.name}") + for output_line in self.details.output_lines(): + print(f" - {output_line}") + + +class Issues: + def __init__(self, issues: List[Issue] = []) -> None: + self.all_issues: List[Issue] = issues + + def add_issue(self, issue: Issue) -> None: + """ + Add a single issue + """ + self.all_issues.append(issue) + + def add_issues(self, issues: List[Issue]) -> None: + """ + Add a list of issues + """ + self.all_issues.extend(issues) + + def group_by_severity(self) -> Dict[str, List[Issue]]: + """ + Group issues by severity. + """ + issues: Dict[str, List[Issue]] = defaultdict(list) + for issue in self.all_issues: + issues[issue.severity.name].append(issue) + return issues + + +class OperatorIssueDetails(IssueDetails): + def __init__(self, module: str, operator: str, source: Union[Path, str]) -> None: + self.module = module + self.operator = operator + self.source = source + + def output_lines(self) -> List[str]: + return [ + f"Description: Use of unsafe operator '{self.operator}' from module '{self.module}'", + f"Source: {str(self.source)}", + ] diff --git a/modelscan/models/__init__.py b/modelscan/models/__init__.py new file mode 100644 index 0000000..259fb4e --- /dev/null +++ b/modelscan/models/__init__.py @@ -0,0 +1,7 @@ +from modelscan.models.h5.scan import H5Scan +from modelscan.models.pickle.scan import ( + PickleScan, + NumpyScan, + PyTorchScan, +) +from modelscan.models.saved_model.scan import SavedModelScan diff --git a/modelscan/models/h5/__init__.py b/modelscan/models/h5/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/modelscan/models/h5/scan.py b/modelscan/models/h5/scan.py new file mode 100644 index 0000000..2f6dd8c --- /dev/null +++ b/modelscan/models/h5/scan.py @@ -0,0 +1,78 @@ +import json +import logging +from pathlib import Path +from typing import IO, List, Tuple, Union, Optional + +try: + import h5py + + h5py_installed = True +except ImportError: + h5py_installed = False + +from modelscan.error import Error, ModelScanError +from modelscan.issues import Issue +from modelscan.models.saved_model.scan import SavedModelScan + +logger = logging.getLogger("modelscan") + + +class H5Scan(SavedModelScan): + @staticmethod + def scan( + source: Union[str, Path], + data: Optional[IO[bytes]] = None, + ) -> Tuple[List[Issue], List[Error]]: + if not h5py_installed: + return [], [ + ModelScanError( + SavedModelScan.name(), + f"File: {source} \nTo scan an h5py file, please install modelscan with h5py extras. 'pip install \"modelscan\[h5py]\"' if you are using pip.", + ) + ] + + if data: + logger.warning( + "H5 scanner got data bytes. It only support direct file scanning." + ) + + return H5Scan._scan_keras_h5_file(source) + + @staticmethod + def _scan_keras_h5_file( + source: Union[str, Path] + ) -> Tuple[List[Issue], List[Error]]: + machine_learning_library_name = "Keras" + operators_in_model = H5Scan._get_keras_h5_operator_names(source) + return H5Scan._check_for_unsafe_tf_keras_operator( + module_name=machine_learning_library_name, + raw_operator=operators_in_model, + source=source, + ) + + @staticmethod + def _get_keras_h5_operator_names(source: Union[str, Path]) -> List[str]: + # Todo: source isn't guaranteed to be a file + with h5py.File(source, "r") as model_hdf5: + lambda_code = [ + layer.get("config", {}).get("function", {}) + for layer in json.loads(model_hdf5.attrs["model_config"])["config"][ + "layers" + ] + if layer["class_name"] == "Lambda" + ] + + if lambda_code: + keras_operator = ["Lambda"] + else: + keras_operator = [] + + return keras_operator + + @staticmethod + def supported_extensions() -> List[str]: + return [".h5"] + + @staticmethod + def name() -> str: + return "hdf5" diff --git a/modelscan/models/pickle/__init__.py b/modelscan/models/pickle/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/modelscan/models/pickle/scan.py b/modelscan/models/pickle/scan.py new file mode 100644 index 0000000..1f47353 --- /dev/null +++ b/modelscan/models/pickle/scan.py @@ -0,0 +1,77 @@ +import logging +from pathlib import Path +from typing import IO, List, Tuple, Union, Optional + +from modelscan.error import Error +from modelscan.issues import Issue +from modelscan.models.scan import ScanBase +from modelscan.tools.picklescanner import ( + scan_numpy, + scan_pickle_bytes, + scan_pytorch, +) + +logger = logging.getLogger("modelscan") + + +class PyTorchScan(ScanBase): + @staticmethod + def scan( + source: Union[str, Path], + data: Optional[IO[bytes]] = None, + ) -> Tuple[List[Issue], List[Error]]: + if data: + return scan_pytorch(data=data, source=source) + + with open(source, "rb") as file_io: + return scan_pytorch(data=file_io, source=source) + + @staticmethod + def supported_extensions() -> List[str]: + return [".bin", ".pt", ".pth", ".ckpt"] + + @staticmethod + def name() -> str: + return "pytorch" + + +class NumpyScan(ScanBase): + @staticmethod + def scan( + source: Union[str, Path], + data: Optional[IO[bytes]] = None, + ) -> Tuple[List[Issue], List[Error]]: + if data: + return scan_numpy(data=data, source=source) + + with open(source, "rb") as file_io: + return scan_numpy(data=file_io, source=source) + + @staticmethod + def supported_extensions() -> List[str]: + return [".npy"] + + @staticmethod + def name() -> str: + return "numpy" + + +class PickleScan(ScanBase): + @staticmethod + def scan( + source: Union[str, Path], + data: Optional[IO[bytes]] = None, + ) -> Tuple[List[Issue], List[Error]]: + if data: + return scan_pickle_bytes(data=data, source=source) + + with open(source, "rb") as file_io: + return scan_pickle_bytes(data=file_io, source=source) + + @staticmethod + def supported_extensions() -> List[str]: + return [".pkl", ".pickle", ".joblib", ".dat", ".data"] + + @staticmethod + def name() -> str: + return "pickle" diff --git a/modelscan/models/saved_model/__init__.py b/modelscan/models/saved_model/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/modelscan/models/saved_model/scan.py b/modelscan/models/saved_model/scan.py new file mode 100644 index 0000000..823a54c --- /dev/null +++ b/modelscan/models/saved_model/scan.py @@ -0,0 +1,145 @@ +# scan pb files for both tensorflow and keras + +import json +from pathlib import Path + +from typing import IO, List, Set, Tuple, Union, Optional, Dict + + +try: + import tensorflow + from tensorflow.core.protobuf.saved_model_pb2 import SavedModel + from tensorflow.python.keras.protobuf.saved_metadata_pb2 import SavedMetadata + + tensorflow_installed = True +except ImportError: + tensorflow_installed = False + + +from modelscan.error import Error, ModelScanError +from modelscan.issues import Issue, IssueCode, IssueSeverity, OperatorIssueDetails +from modelscan.models.scan import ScanBase + + +class SavedModelScan(ScanBase): + @staticmethod + def scan( + source: Union[str, Path], + data: Optional[IO[bytes]] = None, + ) -> Tuple[List[Issue], List[Error]]: + if not tensorflow_installed: + return [], [ + ModelScanError( + SavedModelScan.name(), + f"File: {source} \nTo scan an tensorflow file, please install modelscan with tensorflow extras. 'pip install \"modelscan\[tensorflow]\"' if you are using pip.", + ) + ] + + if data: + return SavedModelScan._scan(source, data) + + with open(source, "rb") as file_io: + return SavedModelScan._scan(source, data=file_io) + + @staticmethod + def _scan( + source: Union[str, Path], data: IO[bytes] + ) -> Tuple[List[Issue], List[Error]]: + file_name = str(source).split("/")[-1] + # Default is a tensorflow model file + if file_name == "keras_metadata.pb": + machine_learning_library_name = "Keras" + operators_in_model = SavedModelScan._get_keras_pb_operator_names(data=data) + + else: + machine_learning_library_name = "Tensorflow" + operators_in_model = SavedModelScan._get_tensorflow_operator_names( + data=data + ) + + return SavedModelScan._check_for_unsafe_tf_keras_operator( + machine_learning_library_name, operators_in_model, source + ) + + @staticmethod + def _get_keras_pb_operator_names(data: IO[bytes]) -> List[str]: + saved_metadata = SavedMetadata() + saved_metadata.ParseFromString(data.read()) + + lambda_code = [ + layer.get("config", {}).get("function", {}).get("items", {}) + for layer in [ + json.loads(node.metadata) + for node in saved_metadata.nodes + if node.identifier == "_tf_keras_layer" + ] + if layer["class_name"] == "Lambda" + ] + + # if lambda code is not empty list that means there has been some code injection in Keras layer + if lambda_code: + keras_operators = ["Lambda"] + else: + keras_operators = [] + + return keras_operators + + @staticmethod + def _get_tensorflow_operator_names(data: IO[bytes]) -> List[str]: + saved_model = SavedModel() + saved_model.ParseFromString(data.read()) + + model_op_names: Set[str] = set() + # Iterate over every metagraph in case there is more than one + for meta_graph in saved_model.meta_graphs: + # Add operations in the graph definition + model_op_names.update(node.op for node in meta_graph.graph_def.node) + # Go through the functions in the graph definition + for func in meta_graph.graph_def.library.function: + # Add operations in each function + model_op_names.update(node.op for node in func.node_def) + # Sort and convert to list + return list(sorted(model_op_names)) + + # This function checks for malicious operators in both Keras and Tensorflow + @staticmethod + def _check_for_unsafe_tf_keras_operator( + module_name: str, raw_operator: List[str], source: Union[str, Path] + ) -> Tuple[List[Issue], List[Error]]: + unsafe_operators: Dict[str, IssueSeverity] = { + "ReadFile": IssueSeverity.HIGH, + "WriteFile": IssueSeverity.HIGH, + "Lambda": IssueSeverity.MEDIUM, + } + issues: List[Issue] = [] + all_operators = tensorflow.raw_ops.__dict__.keys() + all_safe_operators = [ + operator for operator in list(all_operators) if operator[0] != "_" + ] + + for op in raw_operator: + if op in unsafe_operators: + severity = unsafe_operators[op] + elif op not in all_safe_operators: + severity = IssueSeverity.MEDIUM + else: + continue + + issues.append( + Issue( + code=IssueCode.UNSAFE_OPERATOR, + severity=severity, + details=OperatorIssueDetails( + module=module_name, operator=op, source=source + ), + ) + ) + return issues, [] + + @staticmethod + def supported_extensions() -> List[str]: + return [".pb"] + + @staticmethod + def name() -> str: + return "saved_model" diff --git a/modelscan/models/scan.py b/modelscan/models/scan.py new file mode 100644 index 0000000..2eade34 --- /dev/null +++ b/modelscan/models/scan.py @@ -0,0 +1,25 @@ +import abc +from pathlib import Path +from typing import List, Tuple, Union, Optional, IO + +from modelscan.error import Error +from modelscan.issues import Issue + + +class ScanBase(metaclass=abc.ABCMeta): + @staticmethod + @abc.abstractmethod + def name() -> str: + raise NotImplementedError + + @staticmethod + @abc.abstractmethod + def scan( + source: Union[str, Path], data: Optional[IO[bytes]] = None + ) -> Tuple[List[Issue], List[Error]]: + raise NotImplementedError + + @staticmethod + @abc.abstractmethod + def supported_extensions() -> List[str]: + raise NotImplementedError diff --git a/modelscan/modelscan.py b/modelscan/modelscan.py new file mode 100644 index 0000000..f89c47e --- /dev/null +++ b/modelscan/modelscan.py @@ -0,0 +1,130 @@ +import io +import json +import logging +import os +import zipfile +import inspect + +from pathlib import Path +from typing import List, Union, Optional, IO + +from modelscan.error import Error +from modelscan.issues import Issues, Issue +from modelscan import models +from modelscan.models.scan import ScanBase +from modelscan.tools.utils import _http_get, _is_zipfile + +logger = logging.getLogger("modelscan") + + +class Modelscan: + def __init__(self) -> None: + # Scans + + self.supported_model_scans = [ + member + for _, member in inspect.getmembers(models) + if inspect.isclass(member) + and issubclass(member, ScanBase) + and not inspect.isabstract(member) + ] + self.supported_extensions = set() + for scan in self.supported_model_scans: + self.supported_extensions.update(scan.supported_extensions()) + + logger.debug(f"Supported model files {self.supported_extensions}") + + # Output + self._issues = Issues() + self._errors: List[Error] = [] + self._skipped: List[str] = [] + + def scan_path(self, path: Path) -> None: + if path.is_dir(): + self._scan_directory(path) + elif _is_zipfile(path) or path.suffix in self._supported_zip_extensions(): + self._scan_zip(path) + else: + self._scan_source(source=path, extension=path.suffix) + + def _scan_directory(self, directory_path: Path) -> None: + for path in directory_path.rglob("*"): + if not path.is_dir(): + self.scan_path(path) + + def scan_huggingface_model(self, repo_id: str) -> None: + # List model files + model = json.loads( + _http_get(f"https://huggingface.co/api/models/{repo_id}").decode("utf-8") + ) + file_names = [ + file_name + for file_name in (sibling.get("rfilename") for sibling in model["siblings"]) + if file_name is not None + ] + + # Scan model files + for file_name in file_names: + file_ext = os.path.splitext(file_name)[1] + url = f"https://huggingface.co/{repo_id}/resolve/main/{file_name}" + self._scan_source( + source=url, + extension=file_ext, + data=io.BytesIO(_http_get(url)), + ) + + def scan_url(self, url: str) -> None: + # Todo: before it was just scanning scanning_pickle_bytes + # We need to validate this url and determine what type of file it is + # self._scan_bytes( + # data=io.BytesIO(_http_get(url)), + # source=url, + # extension=file_ext, + # ) + pass + + def _scan_source( + self, + source: Union[str, Path], + extension: str, + data: Optional[IO[bytes]] = None, + ) -> None: + issues: List[Issue] = [] + errors: List[Error] = [] + + if extension not in self.supported_extensions: + logger.debug(f"Skipping file {source}") + self._skipped.append(str(source)) + return + + for scan in self.supported_model_scans: + if extension in scan.supported_extensions(): + logger.info(f"Scanning {source} using {scan.name()} model scan") + issues, errors = scan.scan(source=source, data=data) + + self._issues.add_issues(issues) + self._errors.extend(errors) + + def _scan_zip(self, source: Union[str, Path]) -> None: + with zipfile.ZipFile(source, "r") as zip: + file_names = zip.namelist() + for file_name in file_names: + file_ext = os.path.splitext(file_name)[1] + with zip.open(file_name, "r") as file_io: + self._scan_source( + source=f"{source}:{file_name}", + extension=file_ext, + data=file_io, + ) + + @staticmethod + def _supported_zip_extensions() -> List[str]: + return [".zip", ".npz"] + + @property + def issues(self) -> Issues: + return self._issues + + @property + def errors(self) -> List[Error]: + return self._errors diff --git a/modelscan/reports.py b/modelscan/reports.py new file mode 100644 index 0000000..cca09f4 --- /dev/null +++ b/modelscan/reports.py @@ -0,0 +1,69 @@ +import abc +import logging +from typing import List, Optional + +from rich import print + +from modelscan.error import Error +from modelscan.issues import Issues, IssueSeverity + +logger = logging.getLogger("modelscan") + + +class Report(metaclass=abc.ABCMeta): + """ + Abstract base class for different reporting modules. + """ + + def __init__(self) -> None: + pass + + @staticmethod + def generate( + issues: Issues, + errors: List[Error], + ) -> Optional[str]: + """ + Generate report for the given codebase. + Derived classes must provide implementation of this method. + + :param issues: Instance of Issues object + + :param errors: Any errors that occurred during the scan. + """ + raise NotImplemented + + +class ConsoleReport(Report): + @staticmethod + def generate( + issues: Issues, + errors: List[Error], + ) -> None: + issues_by_severity = issues.group_by_severity() + print("\n[blue]--- Summary ---") + total_issue_count = len(issues.all_issues) + if total_issue_count > 0: + print(f"\nTotal Issues: {total_issue_count}") + print(f"\nTotal Issues By Severity:\n") + for severity in IssueSeverity: + if severity.name in issues_by_severity: + print( + f" - {severity.name}: {len(issues_by_severity[severity.name])}" + ) + else: + print(f" - {severity.name}: [green]0") + + print("\n[blue]--- Issues by Severity ---") + for issue_keys in issues_by_severity.keys(): + print(f"\n[blue]--- {issue_keys} ---") + for issue in issues_by_severity[issue_keys]: + issue.print() + else: + print("\n[green] No issues found! 🎉") + + if len(errors) > 0: + print("\n[red]--- Errors --- ") + for index, error in enumerate(errors): + print(f"\nError {index+1}:") + print(str(error)) diff --git a/modelscan/tools/LICENSE b/modelscan/tools/LICENSE new file mode 100644 index 0000000..f13718d --- /dev/null +++ b/modelscan/tools/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Matthieu Maitre + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/modelscan/tools/picklescanner.py b/modelscan/tools/picklescanner.py new file mode 100644 index 0000000..4c5ece6 --- /dev/null +++ b/modelscan/tools/picklescanner.py @@ -0,0 +1,283 @@ +import logging +import pickletools # nosec +from dataclasses import dataclass +from pathlib import Path +from tarfile import TarError +from typing import IO, Any, Dict, List, Set, Tuple, Union + +import numpy as np + +from modelscan.error import Error, ModelScanError +from modelscan.issues import Issue, IssueCode, IssueSeverity, OperatorIssueDetails + +logger = logging.getLogger("modelscan") + +from .utils import MAGIC_NUMBER, _should_read_directly, get_magic_number + + +class GenOpsError(Exception): + def __init__(self, msg: str): + self.msg = msg + super().__init__() + + def __str__(self) -> str: + return self.msg + + +_safe_globals: Dict[str, Set[str]] = { + "collections": {"OrderedDict"}, + "torch": { + "LongStorage", + "FloatStorage", + "HalfStorage", + "QUInt2x4Storage", + "QUInt4x2Storage", + "QInt32Storage", + "QInt8Storage", + "QUInt8Storage", + "ComplexFloatStorage", + "ComplexDoubleStorage", + "DoubleStorage", + "BFloat16Storage", + "BoolStorage", + "CharStorage", + "ShortStorage", + "IntStorage", + "ByteStorage", + }, + "torch._utils": {"_rebuild_tensor_v2"}, +} + +_unsafe_globals: Dict[str, Any] = { + "CRITICAL": { + "__builtin__": { + "eval", + "compile", + "getattr", + "apply", + "exec", + "open", + "breakpoint", + }, # Pickle versions 0, 1, 2 have those function under '__builtin__' + "builtins": { + "eval", + "compile", + "getattr", + "apply", + "exec", + "open", + "breakpoint", + }, # Pickle versions 3, 4 have those function under 'builtins' + "runpy": "*", + "os": "*", + "nt": "*", # Alias for 'os' on Windows. Includes os.system() + "posix": "*", # Alias for 'os' on Linux. Includes os.system() + "socket": "*", + "subprocess": "*", + "sys": "*", + }, + "HIGH": { + "webbrowser": "*", # Includes webbrowser.open() + "httplib": "*", # Includes http.client.HTTPSConnection() + "requests.api": "*", + "aiohttp.client": "*", + }, + "MEDIUM": {}, + "LOW": {}, +} + +# +# TODO: handle methods loading other Pickle files (either mark as suspicious, or follow calls to scan other files [preventing infinite loops]) +# +# pickle.loads() +# https://docs.python.org/3/library/pickle.html#pickle.loads +# pickle.load() +# https://docs.python.org/3/library/pickle.html#pickle.load +# numpy.load() +# https://numpy.org/doc/stable/reference/generated/numpy.load.html#numpy.load +# numpy.ctypeslib.load_library() +# https://numpy.org/doc/stable/reference/routines.ctypeslib.html#numpy.ctypeslib.load_library +# pandas.read_pickle() +# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_pickle.html +# joblib.load() +# https://joblib.readthedocs.io/en/latest/generated/joblib.load.html +# torch.load() +# https://pytorch.org/docs/stable/generated/torch.load.html +# tf.keras.models.load_model() +# https://www.tensorflow.org/api_docs/python/tf/keras/models/load_model +# + + +def _list_globals( + data: IO[bytes], multiple_pickles: bool = True +) -> Set[Tuple[str, str]]: + globals: Set[Any] = set() + + memo: Dict[int, str] = {} + # Scan the data for pickle buffers, stopping when parsing fails or stops making progress + last_byte = b"dummy" + while last_byte != b"": + # List opcodes + try: + ops: List[Tuple[Any, Any, Union[int, None]]] = list( + pickletools.genops(data) + ) + except Exception as e: + raise GenOpsError(str(e)) + last_byte = data.read(1) + data.seek(-1, 1) + + # Extract global imports + for n in range(len(ops)): + op = ops[n] + op_name = op[0].name + op_value: str = op[1] + + if op_name == "MEMOIZE" and n > 0: + memo[len(memo)] = ops[n - 1][1] + + if op_name == "GLOBAL": + globals.add(tuple(op_value.split(" ", 1))) + elif op_name == "STACK_GLOBAL": + values: List[str] = [] + for offset in range(1, n): + if ops[n - offset][0].name == "MEMOIZE": + continue + if ops[n - offset][0].name in ["GET", "BINGET", "LONG_BINGET"]: + values.append(memo[int(ops[n - offset][1])]) + elif ops[n - offset][0].name not in [ + "SHORT_BINUNICODE", + "UNICODE", + "BINUNICODE", + "BINUNICODE8", + ]: + logger.debug( + "Presence of non-string opcode, categorizing as an unknown dangerous import" + ) + values.append("unknown") + else: + values.append(ops[n - offset][1]) + if len(values) == 2: + break + if len(values) != 2: + raise ValueError( + f"Found {len(values)} values for STACK_GLOBAL at position {n} instead of 2." + ) + globals.add((values[1], values[0])) + if not multiple_pickles: + break + + return globals + + +def scan_pickle_bytes( + data: IO[bytes], + source: Union[Path, str], + scan_name: str = "pickle", + multiple_pickles: bool = True, +) -> Tuple[List[Issue], List[Error]]: + """Disassemble a Pickle stream and report issues""" + + issues: List[Issue] = [] + try: + raw_globals = _list_globals(data, multiple_pickles) + except GenOpsError as e: + return issues, [ + ModelScanError(scan_name, f"Error parsing pickle file {source}: {e}") + ] + + logger.debug("Global imports in %s: %s", source, raw_globals) + + for rg in raw_globals: + global_module, global_name, severity = rg[0], rg[1], None + safe_filter = _safe_globals.get(global_module) + unsafe_critical_filter = _unsafe_globals["CRITICAL"].get(global_module) + unsafe_high_filter = _unsafe_globals["HIGH"].get(global_module) + unsafe_medium_filter = _unsafe_globals["MEDIUM"].get(global_module) + unsafe_low_filter = _unsafe_globals["LOW"].get(global_module) + if unsafe_critical_filter is not None and ( + unsafe_critical_filter == "*" or global_name in unsafe_critical_filter + ): + severity = IssueSeverity.CRITICAL + + elif unsafe_high_filter is not None and ( + unsafe_high_filter == "*" or global_name in unsafe_high_filter + ): + severity = IssueSeverity.HIGH + elif unsafe_medium_filter is not None and ( + unsafe_medium_filter == "*" or global_name in unsafe_medium_filter + ): + severity = IssueSeverity.MEDIUM + elif unsafe_low_filter is not None and ( + unsafe_low_filter == "*" or global_name in unsafe_low_filter + ): + severity = IssueSeverity.LOW + elif "unknown" in global_module or "unknown" in global_name: + severity = IssueSeverity.MEDIUM + elif ( + unsafe_critical_filter is None + and unsafe_high_filter is None + and safe_filter is None + ): + severity = IssueSeverity.MEDIUM + else: + continue + issues.append( + Issue( + code=IssueCode.UNSAFE_OPERATOR, + severity=severity, + details=OperatorIssueDetails( + module=global_module, operator=global_name, source=source + ), + ) + ) + return issues, [] + + +def scan_numpy( + data: IO[bytes], source: Union[str, Path] +) -> Tuple[List[Issue], List[Error]]: + # Code to distinguish from NumPy binary files and pickles. + _ZIP_PREFIX = b"PK\x03\x04" + _ZIP_SUFFIX = b"PK\x05\x06" # empty zip files start with this + N = len(np.lib.format.MAGIC_PREFIX) + magic = data.read(N) + # If the file size is less than N, we need to make sure not + # to seek past the beginning of the file + data.seek(-min(N, len(magic)), 1) # back-up + if magic.startswith(_ZIP_PREFIX) or magic.startswith(_ZIP_SUFFIX): + # .npz file + raise NotImplementedError("Scanning of .npz files is not implemented yet") + elif magic == np.lib.format.MAGIC_PREFIX: + # .npy file + version = np.lib.format.read_magic(data) # type: ignore[no-untyped-call] + np.lib.format._check_version(version) # type: ignore[attr-defined] + _, _, dtype = np.lib.format._read_array_header(data, version) # type: ignore[attr-defined] + + if dtype.hasobject: + return scan_pickle_bytes(data, source, "numpy") + else: + return [], [] + else: + return scan_pickle_bytes(data, source, "numpy") + + +def scan_pytorch( + data: IO[bytes], source: Union[str, Path] +) -> Tuple[List[Issue], List[Error]]: + should_read_directly = _should_read_directly(data) + if should_read_directly and data.tell() == 0: + # try loading from tar + try: + # TODO: implement loading from tar + raise TarError() + except TarError: + # file does not contain a tar + data.seek(0) + + magic = get_magic_number(data) + if magic != MAGIC_NUMBER: + return [], [ + ModelScanError("pytorch", f"Invalid magic number for file {source}") + ] + return scan_pickle_bytes(data, source, "pytorch", multiple_pickles=False) diff --git a/modelscan/tools/utils.py b/modelscan/tools/utils.py new file mode 100644 index 0000000..7f8aa99 --- /dev/null +++ b/modelscan/tools/utils.py @@ -0,0 +1,108 @@ +import http.client +import io +import urllib.parse +from pathlib import Path +from pickletools import genops # nosec +from typing import IO, Optional, Union + + +class InvalidMagicError(Exception): + def __init__(self, provided_magic: Optional[int], magic: int, file: str): + self.provided_magic = provided_magic + self.magic = magic + self.file = file + super().__init__() + + def __str__(self) -> str: + return f"{self.file}: {self.provided_magic} != {self.magic}" + + +# copied from pytorch code +# https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/serialization.py#L28 +MAGIC_NUMBER = 0x1950A86A20F9469CFC6C + + +# copied from pytorch code +# https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/serialization.py#L272 +def _is_compressed_file(f: IO[bytes]) -> bool: + compress_modules = ["gzip"] + try: + return f.__module__ in compress_modules + except AttributeError: + return False + + +# copied from pytorch code +# https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/serialization.py#L280 +def _should_read_directly(f: IO[bytes]) -> bool: + """ + Checks if f is a file that should be read directly. It should be read + directly if it is backed by a real file (has a fileno) and is not a + a compressed file (e.g. gzip) + """ + if _is_compressed_file(f): + return False + try: + return f.fileno() >= 0 + except io.UnsupportedOperation: + return False + except AttributeError: + return False + + +# copied from pytorch code +# https://github.com/pytorch/pytorch/blob/0b3316ad2c6ff61416597ef29e8865876dcb12f5/torch/serialization.py#L66 +def _is_zipfile(source: Union[Path, str]) -> bool: + # This is a stricter implementation than zipfile.is_zipfile(). + # zipfile.is_zipfile() is True if the magic number appears anywhere in the + # binary. Since we expect the files here to be generated by torch.save or + # torch.jit.save, it's safe to only check the start bytes and avoid + # collisions and assume the zip has only 1 file. + # See bugs.python.org/issue28494. + + # Read the first 4 bytes of the file + with open(source, "rb") as f: + read_bytes = [] + start = f.tell() + + byte = f.read(1) + while byte != b"": + read_bytes.append(byte) + if len(read_bytes) == 4: + break + byte = f.read(1) + f.seek(start) + + local_header_magic_number = [b"P", b"K", b"\x03", b"\x04"] + return read_bytes == local_header_magic_number + + +def get_magic_number(data: IO[bytes]) -> Optional[int]: + for opcode, args, _ in genops(data): + if "INT" in opcode.name or "LONG" in opcode.name: + data.seek(0) + return int(args) # type: ignore[arg-type] + return None + + +# TODO we can rewrite this function with better error logging and move it +# modelscan/tools/utils.py +def _http_get(url: str) -> bytes: + parsed_url = urllib.parse.urlparse(url) + 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