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NOTE: you need to clone this repo using the --recursive flag since this repo has submodules, e.g., git clone git@github.com:nevillegrech/gigahorse-toolchain.git --recursive

The Gigahorse binary lifter and toolchain

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.

Quickstart

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 2.3 (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/2.3)

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.

Running 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 default pipeline first attempts to decompile a contract using a transactional context-sensitivity configuration. If that times out it performs a second attempt with the scalable-fallback configuration (using a hybrid-precise context sensitivity algorithm, tuned for scalability). In addition, if the default configuration succeeds but produces imprecise output, the precise-fallback configuration (currently the same as the --early_cloning config) is used to attempt to remove that imprecision. Both fallback configurations can be disabled if needed using the --disable_scalable_fallback and --disable_precise_fallback flags respectivelly.
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

(For some Souffle versions, you will get an error message regarding the libsoufflenum.so dynamic library, during the first compilation. You can ignore this and gigahorse.py should work upon a re-run.)
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>

(The clients following the -C flag can be a comma-separated list, with no spaces, of path-reachable or fully-qualified filenames.)
Gigahorse can also be used in “bulk analysis” mode, by replacing by a directory filled with contracts.
For additional instructions in tuning the Gigahorse framework see Tuning.md.

Textual representation of the lifted IR

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. This uses several of the “analysis client libraries” under 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.

Several novel developments to Gigahorse after the original publication have been published as:

  • Grech, N., Lagouvardos, S., Tsatiris, I., Smaragdakis, Y. (2022), Elipmoc: Advanced Decompilation of Ethereum Smart Contracts Proceedings of the ACM in Programming Languages (OOPSLA).

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., Lagouvardos, S., 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., 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.
  • Smaragdakis, Y., Grech, N., Lagouvardos, S., Triantafyllou, K.,…


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