Note: you need to clone this repo using the --recursive flag since this repo has submodules, e.g., git clone [email protected]:nevillegrech/gigahorse-toolchain.git --recursive
A binary lifter (and related framework) from low-level EVM code to a higher-level function-based three-address representation, similar to LLVM IR or Jimple.
First make sure you have the following things installed on your system:
-
Boost libraries (Can be installed on Debian with
apt install libboost-all-dev) -
Python 3.8 (Refer to standard documentation)
-
Souffle 2.0.2 (We only test using the release versions, later development versions may work but are untested by us. Refer to Souffle documentation. The easiest way to install this is to use the release from https://github.com/souffle-lang/souffle/releases/tag/2.0.2)
Now install the Souffle custom functors. Just navigate to the souffle-addon folder:
cd souffle-addon
And run make to install:
$ make # builds all, sets libfunctors.so as a link to libsoufflenum.so
You should now be ready to run Gigahorse.
The gigahorse.py script can be run on a contract individually or on a collection of contract bytecode files in specified directory, and it will run the binary lifter implemented in logic/main.dl on each contract, optionally followed by any additional client analyses specified by the user using the -C flag.
The Gigahorse pipeline also includes a few rounds of inlining of small functions in order to help the subsequent client libraries get more high-level inferences. The inlining functionality can be disabled with --disable_inline.
The expected file format for each contract is in .hex format.
Example (individual contract):
./gigahorse.py examples/long_running.hex
Contracts that take too long to analyse will be skipped after a configurable timeout.
The decompilation results are placed in the directory .temp, whereas metadate about the execution, e.g., metrics are placed in a results.json file, as a list of triples in the form:
[filename, properties, flags]
Here, properties is a list of the detected issues with the contract in filename,
where any output relations in the datalog files that are non-empty will have their
relation name placed in this list.
flags is a list indicating auxiliary or exceptional information. It may include
"ERROR" and "TIMEOUT", which are self-explanatory.
gigahorse.py --help for invocation instructions.
Example (with client analysis):
./gigahorse.py -j <number of jobs> -C clients/visualizeout.py <contracts>
Gigahorse can also be used in "bulk analysis" mode, by replacing by a directory filled with contracts.
Client analysis clients/visualizeout.py can be used to provide a pretty-printed textual representation of the IR produced by Gigahorse.
The pretty-printed text file is named contract.tac and will be placed in the out/ folder for each analyzed contract.
For example the output for ./gigahorse.py -C clients/visualizeout.py examples/long_running.hex will be placed in .temp/long_running/out/contract.tac.
A block visualized in contract.tac looks like:
Begin block 0x3e
prev=[0xb], succ=[0x10ee, 0x49]
=================================
0x3f: v3f(0xf42fdfb) = CONST
0x44: v44 = EQ v3f(0xf42fdfb), v32
0x10c7: v10c7(0x10ee) = CONST
0x10c8: JUMPI v10c7(0x10ee), v44
Keep in mind that the pretty-printed variable identifiers do not correspond to their identifiers in the underlying datalog facts.
## Running Gigahorse Manually (for development purposes)
To use this framework for development purposes (e.g., writing security analyses), an understanding of the analysis pipeline will be helpful. This section describes one common use case --- that of visualizing the CFG of the lifted IR. The pipeline will consist of the manual execution of following three steps:
1. Fact generation
2. Run main.dl using Souffle
3. Visualize results
In order to proceed, make sure that LD_LIBRARY_PATH and LIBRARY_PATH are set:
$ cd souffle-addon
$ export LD_LIBRARY_PATH=`pwd` # or wherever you want to put the resulting libfunctors.so
$ export LIBRARY_PATH=`pwd` # or wherever you want to put the resulting libfunctors.so
We suggest adding LD_LIBRARY_PATH and LIBRARY_PATH to your `.bashrc` file
Now let's manually execute the pipeline above:
$ ./generatefacts <contract> facts # fact generation (translates EVM bytecode into relational format)
$ souffle -F facts logic/main.dl # runs the main decompilation step (written as a Datalog program)
$ clients/visualizeout.py # visualizes the IR and outputs (all outputs of Datalog programs are in a relational format)
## Writing client analyses
Client analyses can be written in any language by reading the relational files that are written by the decompilation step (`main.dl`). This framework however provides preferential treatment for clients written in Datalog. The most notable example of client analysis for the Gigahorse framework is [MadMax](https://github.com/nevillegrech/MadMax). This uses several of the "analysis client libraries" under [clientlib](https://github.com/nevillegrech/gigahorse-toolchain/tree/master/clientlib). These libraries include customizable dataflow analysis, memory modeling, data structure reconstruction and others.
A common template for client analyses for decompiled bytecode is to create souffle datalog file that includes `clientlib/decompiler_imports.dl`, for instance:
#include "clientlib/decompiler_imports.dl"
.output ...
## Uses of Gigahorse
The Gigahorse toolchain was originally published as:
- Grech, N., Brent, L., Scholz, B., Smaragdakis, Y. (2019), Gigahorse: Thorough, Declarative Decompilation of Smart Contracts. *In 41st ACM/IEEE International Conference on Software Engineering.*
In addition, other research tools have been developed on top of Gigahorse, including:
- Grech, N., Kong, M., Jurisevic, A., Brent, L., Scholz, B., Smaragdakis, Y. (2018), MadMax: Surviving Out-of-Gas Conditions in Ethereum Smart Contracts. *Proceedings of the ACM on Programming Languages (OOPSLA).*
- Brent, L., Grech, N., Scholz, B., Smaragdakis, Y. (2020), Ethainter: A Smart Contract Security Analyzer for Composite Vulnerabilities.
*In 41st ACM SIGPLAN Conference on Programming Language Design and Implementation.*
- Lagouvardos, S., Grech, N., Tsatiris, I., and Smaragdakis, Y. (2020) Precise Static Modelling of Ethereum "Memory". *Proceedings of the ACM in Programming Languages (OOPSLA).*
- Grech, N., Kong, M., Jurisevic, A., Brent, L., Scholz, B., Smaragdakis, Y. (2020), Analyzing the Out-of-Gas World of Smart Contracts. *Communications of the ACM.*
The Gigahorse framework also underpins the realtime decompiler and analysis tool at [contract-library.com](https://contract-library.com).
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