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Note that the `panda.queue_blocking` decorator both marks a function as being a blocking function (which allows it to use functions such as `panda.run_serial_cmd`) and queues it up to run after the call to `panda.run()`
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## Recordings
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See [take_recording.py](https://github.com/panda-re/panda/tree/master/panda/python/examples/take_recording.py)
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See [take_recording.py](https://github.com/panda-re/panda/blob/dev/panda/python/examples/take_recording.py)
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A replay can be taken with the function `panda.record_cmd('cmd_to_run', recording_name='replay_name')` which will revert the guest to a `root` snapshot, type a command, begin a recording, press enter, wait for the command to finish, and then end the replay.
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Once a replay is created on disk, it can be analyzed by using `panda.run_replay('replay_name')`.
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1. Initialize a panda object based off a generic machine or a qcow you have.
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2. Register functions to run at various PANDA callbacks.
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3. Register and queue up a blocking function to revert the guest to a snapshot, run commands with `panda.run_serial_cmd()`, and stop the execution with `panda.end_analysis()`
PyPANDA currently supports interactions (e.g., ppp callbacks) with many PANDA plugins such as `taint2` and `osi`. If you wish to extend PyPANDA to support an new plugin, its header file
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must be cleaned up such that it can be parsed by CFFI. See [create_panda_datatypes.py](https://github.com/panda-re/panda/tree/master/panda/python/utils/create_panda_datatypes.py)
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must be cleaned up such that it can be parsed by CFFI. See [create_panda_datatypes.py](https://github.com/panda-re/panda/blob/dev/panda/python/core/create_panda_datatypes.py)
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and the magic `BEGIN_PYPANDA_NEEDS_THIS` strings it searches for.
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## Learn more
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The [PyPANDA paper](https://moyix.net/~moyix/papers/pypanda.pdf) was published at the NDSS Binary Analysis Research Workshop in 2021 and includes details on the project's design goals as well as an evaluation of it's usability and performance.
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The [PyPANDA paper](https://www.ndss-symposium.org/wp-content/uploads/bar2021_23001_paper.pdf) was published at the NDSS Binary Analysis Research Workshop in 2021 and includes details on the project's design goals as well as an evaluation of it's usability and performance.
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