This code is part of the prototype implementation of the Passe system described in the paper "Automating Isolation and Least Privilege in Web Services" by Aaron Blankstein and Michael J. Freedman.

A lot of this code inexplicably bears the name "Hachi". This was an old name for the project that has remained in the codebase.

CAVEAT: This prototype was used to evaluate the ability of the project to run efficiently, play nicely with AppArmor, and effectively generate invariants during the dynamic analysis phase. As such, it is not a robust codebase and is not meant for production use. Certain security parameters (such as the secret key used to MAC security tokens) are hardcoded, compromising the security in real usage scenarios. Furthermore, the constraint checker in the execution mode will not halt database accesses that violates constraints, rather it prints the violation to STDOUT.

This code is a modified fork of the release code of Django 1.3. Django's copyright information and license has been left intact. Any new code not owned or licensed by Django, to the extent that such code exists, is covered by the attached CRAPL license (see CRAPL-LICENSE.txt).

Getting Passe up and running on your machine is going to be a bit of an involved process and usually requires somewhat detailed knowledge of Django 1.3's loading and db syncing process and Python pathing intricacies.

To successfully run the "analysis phase" of Passe, you will first need to fetch and build the associated taint-tracking version of PyPy. Secondly, Passe requires Postgres or SQLite3. Once you've got that sorted, you will need to correctly set your PYTHONPATH such that Passe's libraries are loaded, and your Django application's libraries are loaded. Auto-generated scripts will assume that the analysis phase's paths are the same as the execution phase's paths. So if any of your path variables depend on your working directory, you may have to ensure that you analyze and execute your application from the same working directory.

Finally, the secret key used to MAC security token by the database proxy and other trusted helpers is hardcoded in htoken/__init__.py. Obviously, this is not secure. In production use, this key should be stored in a location inaccessible to untrusted views, but accessible to the trusted components (database proxy, dispatcher).

In order to interpose on scripted JS requests, Passe lightly modified code from HTML5 Privilege Seperation. The modified code is found in django/contrib/html5privsep/

• The dispatcher implementation is in the modifications to the Django dispatcher (django/core/handlers/base.py)
• During the execution phase, the handler will ad-hoc spawn middleware processes
• The logic of the dispatcher is contained in the get_response() function.
• The global variable building_iframe controls whether or not the JS shim extension is used for interposing on Javascript callbacks.
• The function call is_analysis_running() tells the dispatcher whether or not we are in the analysis mode or the execution mode.

• Database proxies are implemented in django/db/proxy/*.py
• The relevant guard checking code is check_token and _check_args_assert
• During the analysis phase, special analysis backends are loaded, these are in django/db/backends/postgresql_psycopg2/analysis_base.py and django/db/backends/sqlite3/analysis_base.py
• During the run phase, the run_base.py backend is used instead, which sets up communication with the proxy.

• Isolated views are wrapped by the code in django/hviews/run.py
• These handle accepting connections from the dispatcher code
• Token management is handled by code in htoken/init.py

• The Passe analyzer is contained in django/analysis/tracer.py
• This code runs by installing itself as a tracer in start_tracer
• When the tracer sees important calls (database usage, new requests), it receives a callback which logs the database query and adds appropriate taints using the PyPy tainter.
• On exit, the finally branch in start_tracer completes the analysis and outputs all the Passe configuration files (inferred constraints, spawn scripts for views, and starter AppArmor profiles)

1. Get Python 2.X, PIP, pgsql.
2. Get the modified taint tracking PyPy and build it for use by the analysis environment.

• See (https://github.com/kantai/passe-pypy-taint-tracking)

3. pip install selenium pyyaml pyro4

• libraries are required for both the execution environment and the analysis environment.
• the old version of pyro is actually a dependency, because Passe plays some tricks to ensure that pyro4 is using a safe deserialization technique.

4. clone into repository
5. export PYTHONPATH=$(hachi-framework-toplevel-dir)
6. export HACHIPATH=$(hachi-framework-toplevel-dir)

0. General hint: check out the benchmarking application's settings.py file.

1. Modify your settings.py: Engine should be the pgsql_psycopg option

   You can add fixtures to be loaded for the analysis phase with the config setting ANALYSIS_FIXTURES = ['authtestdata.json']

2. If it is a Django 1.4 app, it needs to have logging stuff removed, check what the sample app's LOGGING settings look like

3. Some middleware may be broken (one version of the CSRF middleware was broken by Passe)

4. Make sure your PYTHONPATH contains your application and the Passe library.

5. At this point, you can run manage.py runserver --analyze to begin the analysis phase. Submit some requests to the server.

6. Once finished (kill server with CTRL-C and wait for the merging of analysis) scripts and output will be placed in /tmp/hachi_*

7. The output scripts will execute your application with the Django test server. However, many environments will likely require some editing of these scripts. You will also need to be sure to run syncdb before trying to execute your application.

   You will have some starter AppArmor comnfiguration files (/tmp/hachi_view_*.a). These will need to be modified to match some specifics of your OS, if you want to run Passe with AppArmor jails. You can also modify the default AppArmor configuration in the source code file (django/analysis/persisted.py:create_apparmor_prof). This function will spit out the Passe specific sockets required for communication, and some libraries that were required for my environment. You can add additional libraries that need to be loaded and given exec privileges.

   The /tmp/django_analysis file contains the inferred invariants for database queries.

   The /tmp/hachi_spawn_script.sh script will spawn all of the Passe views, and the Passe helper processes (the dispatcher, isolated middleware, and database proxy). This file may need to be modified for your particular deployment. To use gunicorn, for example, you need to be replace the manage.py runserver line with the startup call to gunicorn.

   The /tmp/hachi_view_* files are spawn scripts which run each Passe view in a separate process. They are named such that the associated AppArmor profile will be loaded if those profiles are loaded into AppArmor (via: sudo apparmor_parser -C profile-name.a)

   Spawning multiple workers for each view and helper requires a little bit more editing. Each view script accepts a commandline argument for it's worker ID, and so too does the spawnhelpers call -- so modify the hachi_spawn_script.sh to contain a loop like so:

   for i in $(seq 0 1 $((PASSE_WORKERS - 1)))
   do
       /tmp/hachi_view_foo $i &
       /tmp/hachi_view_bar $i &
       python manage.py spawnhelpers $i &
   done
   gunicorn -w $PASSE_WORKERS -b 0.0.0.0:8000 foo.wsgi:application

   The benchmarking application in passe-sample-apps contains a modified hachi_spawn_script.sh used to spawn multiple workers.

8. Sometimes, the interaction between the path variables and working directories requires that you explicity tell Passe where your settings.py file is located. This can be done by setting the environment variable, HACHISETTINGS, to the Python module name (e.g., Foo.Bar.settings)