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ir_lifting_data

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Artifact for IEEE Security and Privacy 2022 paper: "SoK: Demystifying Binary Lifters Through the Lens of Downstream Applications"

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# SoK: Demystifying Binary Lifters Through the Lens of Downstream Applications (IEEE S&P 2022 artifacts)

Artifact for **IEEE Security and Privacy 2022** paper: "SoK: Demystifying Binary Lifters Through the Lens of Downstream Applications".

Paper: [link](https://www.computer.org/csdl/proceedings-article/sp/2022/131600a453/1wKCev3wlbO)

Zenodo link: [https://zenodo.org/record/5163174#.YQy6dEC-vb0](https://zenodo.org/record/5163174#.YQy6dEC-vb0)

(Note: This work started in the summer of 2020. Many of the tools evaluated have been under continuous development and refinement over the past two years (e.g., [rev.ng](https://github.com/revng/revng), and [Instrew](https://github.com/aengelke/instrew)). The results in the paper may not fully reflect the state of each tool at this time. We recommend that users try to use the latest version of tools when choosing the right one and don't rely solely on the results of this paper.)

## Lifters:

* McSema: [https://github.com/lifting-bits/mcsema](https://github.com/lifting-bits/mcsema)
 * McSema<sup>0</sup>: McSema with all LLVM optimizations disabled  
 * RetDec: [https://github.com/avast/retdec](https://github.com/avast/retdec)
 * Mctoll: [https://github.com/microsoft/llvm-mctoll](https://github.com/microsoft/llvm-mctoll)
 * BinRec: [https://github.com/securesystemslab/BinRec](https://github.com/securesystemslab/BinRec)

## Downstream Application

* Pointer Analysis: Static Value-Flow Analysis (SVF)  [https://github.com/SVF-tools/SVF](https://github.com/SVF-tools/SVF)
 * Discriminability Analysis: Neural Code Comprehension (NCC) [https://github.com/spcl/ncc](https://github.com/spcl/ncc)
 * Decompilation: retdec-decompiler (llvmir2hll) [https://github.com/avast/retdec/tree/master/src/llvmir2hll](https://github.com/avast/retdec/tree/master/src/llvmir2hll)

Please refer to [lifter-summary](https://github.com/monkbai/ir_lifting_data/blob/master/lifter-sm.pdf) for the patching & augmenting we made on these four lifters and more details about the downstream applications we leveraged.

## Datasets

### Pointer Analysis
 * Flow-sensitive tests provided in SVF [test suite](https://github.com/SVF-tools/Test-Suite/tree/master/test_cases_bc/fs_tests)
 * Testcases generated with EMI mutation [alias_test/alias_data.txt](https://github.com/monkbai/ir_lifting_data/blob/master/alias_test/alias_data.txt)

### Discriminability Analysis
 * We use the [POJ-104](https://arxiv.org/abs/1409.5718) dataset used in NCC, the lifted LLVM IR can be download from [here](https://github.com/monkbai/ir_lifting_data/blob/master/ncc_data/ncc_data.txt)

### Decompilation
 * In total nine C programs in SPEC INT 2006 benchmark, the lifted IR can be download from [decompile_data/decom_data.txt](https://github.com/monkbai/ir_lifting_data/blob/master/decompile_data/decom_data.txt)

### Address Sanitizer 
We apply the Address Sanitizer on the IR lifted by McSema and RetDec and compare the results with the binary-only tool, [RetroWrite](https://github.com/HexHive/retrowrite).

* We use the Juliet dataset used by RetroWrite [Juliet Test Suite for C/C++](https://samate.nist.gov/SRD/testsuite.php), the lifted IR and recompiled binaries can be found in [ASan_test/ASan_test_data.txt](https://github.com/monkbai/ir_lifting_data/blob/master/ASan_test/ASan_test_data.txt)

## Code and Data Structure

```bash
├── ASan_test          (Data used to compare with binary-only tool: RetroWrite)
├── Binary_Diffing     (Data used to compare with binary-only tool: DeepBinDiff)
├── aarch64            (Data of three downstream applications on ARM64 platform)
├── alias_test         (Data used for Pointer Analysis: SVF test suite)
├── correctness_check  (Scripts used to check correctness of lifted IR)
├── decompile_data     (Data used for Decompilation: SPEC INT 2006)
├── ncc_data           (Data used for Discriminability Analysis: POJ-104)
└── lifter-sm.pdf      (Supplementary Materials)
```

## Main Results

For more detailed evaluation results, please see our [paper](https://github.com/monkbai/ir_lifting_data).

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