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Development

Want to contribute to unblob? That's great! We developed a framework (we sometimes reference it as "unblob core"), to make it very easy to add support for new file formats. This page describes the process of how to do that.

If you don't want or don't know how to develop complex Python applications, that's not a problem! If there is a format you would like to be supported in unblob and you can describe and explain it (maybe with nifty hex-representations, hand-drawings or smoke signs, or whatever you cup-of-tea is), we might help you implement it! Just open a new ticket in the GitHub issue tracker.

If you do know all this stuff, and you have all the tools in the world installed, you can just jump to the How to write handlers section where the exciting stuff is.

Setting up development environment

Required tools

  • Python: unblob requires Python 3.9 or above. Make sure that Python is installed on your system.

  • git: You need it for cloning the repository. Install it from the git-scm website.

  • Poetry: it is a package manager for Python dependencies. Follow the instructions on the Poetry website to install the latest version.

  • pre-commit: We are using pre-commit to run checks like linters, type checks and formatting issues.

  • Git LFS: We have big integration test files, and we are using Git LFS to track them. Install git-lfs from the website.

  • Rust (for unblob-native): unblob has a Rust extension for performance intensive processing. Building it is entirely optional and requires rustup to be installed on the host system. Follow the instructions on the rustup website to install it.

  • pyenv (Recommended): When you are working with multiple versions of Python, pyenv makes it very easy to install and use different versions and make virtualenvs. Follow the instructions on GitHub for the installation. If your system already has at least Python 3.9 installed, you don't need it.

Cloning the Git repository

Set up your git config, fork the project on GitHub, then clone your fork locally.

If you installed pre-commit, you can run pre-commit install, which makes pre-commit run automatically during git commits with git hooks, so you don't have to run them manually.

You need to setup Git LFS once, before you will be able to run the whole test suite:

git lfs install

Warning

If you have cloned the repository prior to installing Git LFS, you need to run the following commands in the cloned repository once:

git lfs pull
git lfs checkout

Making a virtualenv

The recommended way to develop Python projects in a semi-isolated way is to use virtualenv.

If you don't want to manage it separately, you can rely on Poetry to automatically create a virtualenv for you on install.

If you don't want Poetry to automatically create a virtualenv, you can turn it off with this command:

poetry config virtualenvs.create false

Or instead of Poetry you can use pyenv. You can set the Python interpreter version for the local folder only with:

pyenv local 3.12.7

Installing Python dependencies

We are using poetry to manage our Python dependencies. To install all required dependencies for development, you can run the following command:

poetry install --with dev

Please note that it installs dependencies within the dedicated virtual environment. So if you want to run unblob or pytest, you need to do it from within the virtual environment:

Using poetry run:

poetry run unblob
poetry run pytest tests -v

By dropping into the virtual environment:

poetry shell
unblob
pytest tests -v

Running pre-commit

If you installed the pre-commit git hook when setting up your local git repo, you don't need this step, otherwise you can run all checks with pre-commit run --all-files.

Running the tests

We are using pytest for running our test suite.
We have big integration files in the tests/integration directory, we are using Git LFS to track them.
Only after you installed Git LFS, can you run all tests, with python -m pytest tests/ in the activated virtualenv.

Writing handlers

Every handler inherits from the abstract class Handler located in unblob/models.py:

class Handler(abc.ABC):
    """A file type handler is responsible for searching, validating and "unblobbing" files from Blobs."""
    NAME: str
    PATTERNS: str
    PATTERN_MATCH_OFFSET: int = 0
    EXTRACTOR: Optional[Extractor]

    @classmethod
    def get_dependencies(cls):
        """Returns external command dependencies needed for this handler to work."""

    @abc.abstractmethod
    def calculate_chunk(self, file: io.BufferedIOBase, start_offset: int) -> Optional[ValidChunk]:
        """Returns a ValidChunk when it found a valid format for this Handler.
        Otherwise it can raise and Exception or return None, those will be ignored.
        """

    def extract(self, inpath: Path, outdir: Path):
        """Responsible for extraction a ValidChunk."""
  • NAME: a unique name for this handler, this value will be appended at the end of carved out chunks
  • PATTERNS: an array of Hyperscan rules.
  • PATTERN_MATCH_OFFSET: an offset from the hyperscan match to the actual start offset.
    This happens when the magic is not the first field in a file header
  • EXTRACTOR: an optional Extractor. It can be set to None if the handler is supposed to only carve files
  • get_dependencies(): returns the extractor dependencies. This helps unblob keep track of third party dependencies.
  • calculate_chunk(): this is the method that needs to be overridden in your handler. It receives a file object and the effective start_offset of your chunk. This is where you implement the logic to compute the end_offset and return a ValidChunk object.

StructHandler class

StructHandler is a specialized subclass of Handler that provides a structure parsing API based on the dissect.cstruct library:

class StructHandler(Handler):
    C_DEFINITIONS: str
    HEADER_STRUCT: str

    def __init__(self):
        self._struct_parser = StructParser(self.C_DEFINITIONS)

    @property
    def cparser_le(self):
        return self._struct_parser.cparser_le

    @property
    def cparser_be(self):
        return self._struct_parser.cparser_be

    def parse_header(self, file: io.BufferedIOBase, endian=Endian.LITTLE):
        header = self._struct_parser.parse(self.HEADER_STRUCT, file, endian)
        logger.debug("Header parsed", header=header, _verbosity=3)
        return header

This class defines new attributes and methods:

  • C_DEFINITIONS: a string holding one or multiple structures definitions in C, which will be used to parse the format. We use the following standard to define structs:

    typedef struct my_struct {
    uint8 header_length;
} my_struct_t;

  • HEADER_STRUCT: the name of your C structure that you'll use to parse the format header.

  • parse_header(): it will parse the file from the current offset in endian endianness into a structure using HEADER_STRUCT defined in C_DEFINITIONS.

If you need to parse structure using different endianness, the class exposes two properties:

  • cparser_le: dissect.cstruct parser configured in little endian
  • cparser_be: dissect.cstruct parser configured in big endian

Recommendation

If your format allows it, we strongly recommend you to inherit from the StructHandler given that it will be strongly typed and less prone to errors.

DirectoryHandler class

DirectoryHandler is a specialized handler responsible for identifying multi-file formats located in a directory or in a subtree. The abstract class is located in unblob/models.py:

class DirectoryHandler(abc.ABC):
    """A directory type handler is responsible for searching, validating and "unblobbing" files from multiple files in a directory."""

    NAME: str

    EXTRACTOR: DirectoryExtractor

    PATTERN: DirectoryPattern

    @classmethod
    def get_dependencies(cls):
        """Return external command dependencies needed for this handler to work."""
        if cls.EXTRACTOR:
            return cls.EXTRACTOR.get_dependencies()
        return []

    @abc.abstractmethod
    def calculate_multifile(self, file: Path) -> Optional[MultiFile]:
        """Calculate the MultiFile in a directory, using a file matched by the pattern as a starting point."""

    def extract(self, paths: List[Path], outdir: Path):
        if self.EXTRACTOR is None:
            logger.debug("Skipping file: no extractor.", paths=paths)
            raise ExtractError

        # We only extract every blob once, it's a mistake to extract the same blob again
        outdir.mkdir(parents=True, exist_ok=False)

        self.EXTRACTOR.extract(paths, outdir)
  • NAME: a unique name for this handler
  • PATTERN: A DirectoryPattern used to identify a starting/main file of the given format.
  • EXTRACTOR: a DirectoryExtractor.
  • get_dependencies(): returns the extractor dependencies. This helps unblob keep track of third party dependencies.
  • calculate_multifile(): this is the method that needs to be overridden in your handler. It receives a file Path object identified by the PATTERN in the directory. This is where you implement the logic to compute and return the MultiFile file set.

Any files that are being processed as part of a MultiFile set would be skipped from Chunk detection.

Any file that is part of multiple MultiFile is a collision and results in a processing error.

Example Handler implementation

Let's imagine that we have a custom file format that always starts with the magic: UNBLOB!!, followed by the size of the file (header included) as an unsigned 32 bit integer.

First, we create a file in unblob/handlers/archive/myformat.py and write the skeleton of our handler:

class MyformatHandler(StructHandler):
    NAME = "myformat"

    PATTERNS = []
    C_DEFINITIONS = ""
    HEADER_STRUCT = ""
    EXTRACTOR = None

    def calculate_chunk(self, file: io.BufferedIOBase, start_offset: int) -> Optional[ValidChunk]:
        return

We need to match on our custom magic. To find the right offset, we need to match on the UNBLOB!! byte pattern, so we add a HexString Hyperscan rule:

class MyformatHandler(StructHandler):
    NAME = "myformat"

    PATTERNS = [
        HexString("55 4E 42 4C 4F 42 21 21"),  # "UNBLOB!!"
    ]

    C_DEFINITIONS = ""
    HEADER_STRUCT = ""
    EXTRACTOR = None

    def calculate_chunk(self, file: io.BufferedIOBase, start_offset: int) -> Optional[ValidChunk]:
        return

Then we need to parse the header, so we define a C structure in C_DEFINITIONS and adapt HEADER_STRUCT accordingly:

class MyformatHandler(StructHandler):
    NAME = "myformat"

    PATTERNS = [
        HexString("55 4E 42 4C 4F 42 21 21"),  # "UNBLOB!!"
    ]

    C_DEFINITIONS= r"""
        typedef struct myformat_header {
            char magic[8];
            uint32 size;
        } myformat_header_t;
    """
    HEADER_STRUCT= "myformat_header_t"

    EXTRACTOR = None

    def calculate_chunk(self, file: io.BufferedIOBase, start_offset: int) -> Optional[ValidChunk]:
        return

With everything set, all that is left is to implement the calculate_chunk function:

class MyformatHandler(StructHandler):
    NAME = "myformat"

    PATTERNS = [
        HexString("55 4E 42 4C 4F 42 21 21"),  # "UNBLOB!!"
    ]

    C_DEFINITIONS= r"""
        typedef struct myformat_header {
            char magic[8];
            uint32 size;
        } myformat_header_t;
    """
    HEADER_STRUCT= "myformat_header_t"

    EXTRACTOR = None

    def calculate_chunk(self, file: io.BufferedIOBase, start_offset: int) -> Optional[ValidChunk]:
        header = self.parse_header(file, Endian.LITTLE)
        end_offset = start_offset + header.size
        return ValidChunk(start_offset=start_offset, end_offset=end_offset)

That's it!
Now you have a working handler for your own custom format!

Testing Handlers

If you want to submit a new format handler to unblob, it needs to come up with its own set of integration tests.

We've implemented integration tests this way:

  1. pytest picks up integration test files corresponding to your handler in test/integration/type/handler_name/__input__ directory.
  2. pytest runs unblob on all the integration test files it picked up in the first step.
  3. pytest runs diff between the temporary extraction directory and test/integration/type/handler_name/__output__.
  4. if no differences are observed the test pass, otherwise it fails.

Important

Create integration test files that cover all the possible scenarios of the target format.

That includes different endianness, different versions, different padding, different algorithms. An excellent example of this is the integration test files for JFFS2 filesystems where we have filesystems covering both endianness (big endian, little endian), with or without padding, and with different compression algorithms (no compression, zlib, rtime, lzo):

./fruits.new.be.zlib.padded.jffs2
./fruits.new.be.nocomp.padded.jffs2
./fruits.new.be.rtime.jffs2
./fruits.new.le.lzo.jffs2
./fruits.new.le.rtime.jffs2
./fruits.new.le.nocomp.padded.jffs2
./fruits.new.be.rtime.padded.jffs2
./fruits.new.be.lzo.jffs2
./fruits.new.be.zlib.jffs2
./fruits.new.le.zlib.padded.jffs2
./fruits.new.be.lzo.padded.jffs2
./fruits.new.le.lzo.padded.jffs2
./fruits.new.be.nocomp.jffs2
./fruits.new.le.zlib.jffs2
./fruits.new.le.rtime.padded.jffs2
./fruits.new.le.nocomp.jffs2

Utilities Functions

We developed a bunch of utility functions which helped us during the development of existing unblob handlers. Do not hesitate to take a look at them in unblob/file_utils.py to see if any of those functions could help you during your own handler development.

Hyperscan Rules

Our hyperscan-based implementation accepts two different kinds of rule definitions: Regex and HexString.

Regex

This object simply represents any regular expression. Example:

PATTERNS = [
    Regex(r"-lh0-")
]

HexString

This object can be used to write rules using the same DSL as Yara. The only limitation is that we do not support multi-line comments and unbounded jumps. Here's an example of a Hyperscan rule based on HexString:

PATTERNS = [
    HexString("""
        // this is a comment
        AA 00 [2] 01
    """)
]

In addition, start and end of input anchors (^ and $ like in regular expressions) can also be used to restrict a match to the beginning or the end of the input file.

DirectoryPatterns

The DirectoryHandler uses these patterns to identify the starting/main file of a given multi-file format. There are currently two main types: Glob and SingleFile

Glob

The Glob object can use traditional globbing to detect files in a directory. This could be used when the file could have a varying part. There are cases where multiple multi-file set could be in a single directory. The job of the DirectoryPattern is to recognize the main file for each set.

Here is an example on Glob:

PATTERN = Glob("*.7z.001")

This example identify the first volume of a multi-volume sevenzip archive. Notice that this could pick up all first volumes in a given directory. (NB: Detecting the other volumes of a given set is the responsibility of the DirectoryHandler.calculate_multifile function. Do not write a Glob which picks up all the files of a multi-file set as that would result in errors.)

SingleFile

The SingleFile object can be used to identify a single file with a known name. (Obviously only use this if the main file name is well-known and does not have a varying part. It also means that only a single multi-file set can be detected in a given directory.)

Here is an example on SingleFile:

PATTERN = SingleFile("meta-data.json")

This would pick up the file meta-data.json and pass it to the DirectoryHandler. The handler still has to verify the file and has to find the additional files.

Writing extractors

Recommendation

We support custom Python based extractors as part of unblob, but unless you write a handler for an exotic format, you should check if the Command extractor is sufficient for your needs, as it's very simple to use.

Command extractor

This extractor simply runs a command line tool on the carved-out file (inpath) to extract into the extraction directory (outdir). Below is the Command extractor instance of the ZIP handler:

EXTRACTOR = Command("7z", "x", "-p", "-y", "{inpath}", "-o{outdir}")

If you have a custom format with no supported command to extract it, check out the Extractor Python class.

Extractor class

The Extractor interface is defined in unblob/models.py:

class Extractor(abc.ABC):
    def get_dependencies(self) -> List[str]:
        """Returns the external command dependencies."""
        return []

    @abc.abstractmethod
    def extract(self, inpath: Path, outdir: Path) -> Optional[ExtractResult]:
        """Extract the carved out chunk. Raises ExtractError on failure."""

Two methods are exposed by this class:

  • get_dependencies(): you should override it if your custom extractor relies on external dependencies such as command line tools
  • extract(): you must override this function. This is where you'll perform the extraction of inpath content into outdir extraction directory

Recommendation

Although it is possible to implement extract() with path manipulations, checks for path traversals, and performing io by using Python libraries (os, pathlib.Path), but it turns out somewhat tedious. Instead we recommend to remove boilerplate and use a helper class FileSystem from unblob/file_utils.py which ensures that all file objects are created under its root.

DirectoryExtractor class

The DirectoryExtractor interface is defined in unblob/models.py:

class DirectoryExtractor(abc.ABC):
    def get_dependencies(self) -> List[str]:
        """Return the external command dependencies."""
        return []

    @abc.abstractmethod
    def extract(self, paths: List[Path], outdir: Path) -> Optional[ExtractResult]:
        """Extract from a multi file path list.

        Raises ExtractError on failure.
        """

Two methods are exposed by this class:

  • get_dependencies(): you should override it if your custom extractor relies on external dependencies such as command line tools
  • extract(): you must override this function. This is where you'll perform the extraction of paths files into outdir extraction directory

Recommendation

Similarly to Extractor, it is recommended to use the FileSystem helper class to implement extract.

Example Extractor

Extractors are quite complex beasts, so rather than trying to come up with a fake example, we recommend you to read through our RomFS extractor code to see what it looks like in real world applications.

Guidelines

Code style

We adhere to PEP8 and enforce proper formatting of source files using ruff format so you should not worry about formatting source code at all, pre-commit will take care of it.

For linting we use ruff check. Lint errors can be shown in your editor of choice by one of the editor plugins.

File Format Correctness

We want to strike the right balance between false positive reduction and a totally loose implementation. We tend not to validate checksums in order to still be able to extract corrupted content. However, if the lack of checksum validation gets in the way by leaving the handler generating a large amount of false positive, then it's time to revisit the handler and implement stronger header checks.

Common unblob Handler Mistakes

This is a collection of all the bad code we've seen during unblob development. Learn from us so you can avoid them in the future 🙂

  • Use seek rather than read whenever possible, it's faster.
  • You should always keep in mind to seek to the position the header starts or make sure you are always at the correct offset at all times. For example we made the mistake multiple times that read 4 bytes for file magic and didn't seek back.
  • Watch out for negative seeking
  • Make sure you get your types right! signedness can get in the way.
  • Try to use as specific as possible patterns to identify data in Handlers to avoid false-positive matches and extra processing in the Handler.
  • Try to avoid using overlapping patterns, as patterns that match on the same data could easily collide. Hyperscan does not guarantee priority between patterns matching on the same data. (Hyperscan reports matches ordered by the pattern match end offset. In case multiple pattern match on the same end offset the matching order depends on the pattern registration order which is undefined in unblob.)