PANDA is an open-source Platform for Architecture-Neutral Dynamic Analysis. It is built upon the QEMU whole system emulator, and so analyses have access to all code executing in the guest and all data. PANDA adds the ability to record and replay executions, enabling iterative, deep, whole system analyses. Further, the replay log files are compact and shareable, allowing for repeatable experiments. A nine billion instruction boot of FreeBSD, e.g., is represented by only a few hundred MB. PANDA leverages QEMU's support of thirteen different CPU architectures to make analyses of those diverse instruction sets possible within the LLVM IR. In this way, PANDA can have a single dynamic taint analysis, for example, that precisely supports many CPUs. PANDA analyses are written in a simple plugin architecture which includes a mechanism to share functionality between plugins, increasing analysis code re-use and simplifying complex analysis development.

It is currently being developed in collaboration with MIT Lincoln Laboratory, NYU, and Northeastern University.

Because PANDA has a few dependencies, we've encoded the build instructions into a script, panda/scripts/install_ubuntu.sh. The script should actually work on Debian 7/8 and Ubuntu 14.04, and it shouldn't be hard to translate the apt-get commands into whatever package manager your distribution uses. We currently only vouch for buildability on Debian 7/8 and Ubuntu 14.04, but we welcome pull requests to fix issues with other distros.

Note that if you want to use our LLVM features (mainly the dynamic taint system), you will need to install LLVM 3.3 from OS packages or compiled from source. On Ubuntu 14.04 this will happen automatically via install_ubuntu.sh. Alternatively, we have created an Ubuntu PPA at ppa:phulin/panda. You can use the following commands to install all dependencies on 14.04 or 16.04:

sudo add-apt-repository ppa:phulin/panda
sudo apt-get update
sudo apt-get build-dep qemu
sudo apt-get install python-pip git protobuf-compiler protobuf-c-compiler \
  libprotobuf-c0-dev libprotoc-dev libelf-dev \
  libcapstone-dev libdwarf-dev python-pycparser llvm-3.3 clang-3.3 libc++-dev
git clone https://github.com/panda-re/panda
mkdir -p build-panda && cd build-panda
../panda/build.sh

Building on Mac is less well-tested, but has been known to work. There is a script, panda/scripts/install_osx.sh to build under OS X.

Finally, if you want to skip the build process altogether, there is a Docker image available, created by ctfhacker. You can get it by running:

docker pull ctfhacker/panda-re

If you need help with PANDA, or want to discuss the project, you can join our IRC channel at #panda-re on Freenode, or join the PANDA mailing list.

We have a basic manual here.

Details about the architecture-neutral plugin interface can be found in panda/docs/PANDA.md. Existing plugins and tools can be found in panda/plugins and panda.

PANDA currently supports whole-system record/replay execution of x86, x86_64, and ARM guests. Documentation can be found in the manual.

• [1] B. Dolan-Gavitt, T. Leek, J. Hodosh, W. Lee. Tappan Zee (North) Bridge: Mining Memory Accesses for Introspection. 20th ACM Conference on Computer and Communications Security (CCS), Berlin, Germany, November 2013.

• [2] R. Whelan, T. Leek, D. Kaeli. Architecture-Independent Dynamic Information Flow Tracking. 22nd International Conference on Compiler Construction (CC), Rome, Italy, March 2013.

• [3] B. Dolan-Gavitt, J. Hodosh, P. Hulin, T. Leek, R. Whelan. Repeatable Reverse Engineering with PANDA. 5th Program Protection and Reverse Engineering Workshop, Los Angeles, California, December 2015.

• [4] M. Stamatogiannakis, P. Groth, H. Bos. Decoupling Provenance Capture and Analysis from Execution. 7th USENIX Workshop on the Theory and Practice of Provenance, Edinburgh, Scotland, July 2015.

• [5] B. Dolan-Gavitt, P. Hulin, T. Leek, E. Kirda, A. Mambretti, W. Robertson, F. Ulrich, R. Whelan. LAVA: Large-scale Automated Vulnerability Addition. 37th IEEE Symposium on Security and Privacy, San Jose, California, May 2016.

GPLv2.

This material is based upon work supported under Air Force Contract No. FA8721-05-C-0002 and/or FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. Air Force.