Lifting Assembly to Intermediate Representation

Hasabnis, Niranjan; Sekar, R.

doi:10.1145/2980024.2872380

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Hasabnis et. al [14] developed a learning-based method to automate this, which depended heavily on integration with the compiler. Wang et al [36] created Ouroboros, a tool to disassemble binaries in such a way that the output can be re-assembled into a binary again which is rule-based as opposed to learningbased.…”

Section: Related Workmentioning

confidence: 99%

Beyond the C: Retargetable Decompilation using Neural Machine Translation

Hosseini¹,

Dolan-Gavitt²

2022

Proceedings 2022 Workshop on Binary Analysis Research

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The problem of reversing the compilation process, decompilation, is an important tool in reverse engineering of computer software. Recently, researchers have proposed using techniques from neural machine translation to automate the process in decompilation. Although such techniques hold the promise of targeting a wider range of source and assembly languages, to date they have primarily targeted C code. In this paper we argue that existing neural decompilers have achieved higher accuracy at the cost of requiring language-specific domain knowledge such as tokenizers and parsers to build an abstract syntax tree (AST) for the source language, which increases the overhead of supporting new languages. We explore a different tradeoff that, to the extent possible, treats the assembly and source languages as plain text, and show that this allows us to build a decompiler that is easily retargetable to new languages. We evaluate our prototype decompiler, Beyond The C (BTC), on Go, Fortran, OCaml, and C, and examine the impact of parameters such as tokenization and training data selection on the quality of decompilation, finding that it achieves comparable decompilation results to prior work in neural decompilation with significantly less domain knowledge. We will release our training data, trained decompilation models, and code to help encourage future research into language-agnostic decompilation.

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Section: Related Workmentioning

confidence: 99%

Beyond the C: Retargetable Decompilation using Neural Machine Translation

Hosseini¹,

Dolan-Gavitt²

2022

Proceedings 2022 Workshop on Binary Analysis Research

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“…On the other hand, their usage requires to trust both the transpiler and the implementation of the analysis. Due to the complexity of writing a transpiler for each architecture, recent work has been done to synthesize the transpiler from compiler backends [25]. However, this requires to trust both: the synthesis procedure and the compiler backend.…”

Section: Related Workmentioning

confidence: 99%

TrABin: Trustworthy analyses of binaries

Lindner

Guanciale

Metere

2019

Science of Computer Programming

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Verification of microkernels, device drivers, and crypto routines requires analyses at the binary level. In order to automate these analyses, in the last years several binary analysis platforms have been introduced. These platforms share a common design: the adoption of hardware-independent intermediate representations, a mechanism to translate architecture dependent code to this representation, and a set of architecture independent analyses that process the intermediate representation.The usage of these platforms to verify software introduces the need for trusting both the correctness of the translation from binary code to intermediate language (called transpilation) and the correctness of the analyses. Achieving a high degree of trust is challenging since the transpilation must handle (i) all the side effects of the instructions, (ii) multiple instruction encodings (e.g. ARM Thumb), and (iii) variable instruction length (e.g. Intel). Similarly, analyses can use complex transformations (e.g. loop unrolling) and simplifications (e.g. partial evaluation) of the artifacts, whose bugs can jeopardize correctness of the results.We overcome these problems by developing a binary analysis platform on top of the interactive theorem prover HOL4. First, we formally model a binary intermediate language and we prove correctness of several supporting tools (i.e. a type checker). Then, we implement two proof-producing transpilers, which respectively translate ARMv8 and CortexM0 programs to the intermediate language and generate a certificate. This certificate is a HOL4 proof demonstrating correctness of the translation. As demonstrating analysis, we implement a proofproducing weakest precondition generator, which can be used to verify that a given loop-free program fragment satisfies a contract. Finally, we use an AES encryption implementation to benchmark our platform.

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“…On the other hand, their usage requires to trust the used transpiler. Due to the complexity of writing a transpiler for each architecture, recent work has been done to synthesize the transpiler from compiler backends [12]. However, this requires to trust both: the synthesis procedure and the compiler backend.…”

Section: Related Workmentioning

confidence: 99%

Sound Transpilation from Binary to Machine-Independent Code

Metere

Lindner

Guanciale

2017

Lecture Notes in Computer Science

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In order to handle the complexity and heterogeneity of modern instruction set architectures, analysis platforms share a common design, the adoption of hardware-independent intermediate representations. The usage of these platforms to verify systems down to binary-level is appealing due to the high degree of automation they provide. However, it introduces the need for trusting the correctness of the translation from binary code to intermediate language. Achieving a high degree of trust is challenging since this transpilation must handle (i) all the side effects of the instructions, (ii) multiple instruction encoding (e.g. ARM Thumb), and (iii) variable instruction length (e.g. Intel). We overcome these problems by formally modeling one of such intermediate languages in the interactive theorem prover HOL4 and by implementing a proofproducing transpiler. This tool translates ARMv8 programs to the intermediate language and generates a HOL4 proof that demonstrates the correctness of the translation in the form of a simulation theorem. We also show how the transpiler theorems can be used to transfer properties verified on the intermediate language to the binary code.

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Lifting Assembly to Intermediate Representation

Cited by 3 publications

References 23 publications

Beyond the C: Retargetable Decompilation using Neural Machine Translation

Beyond the C: Retargetable Decompilation using Neural Machine Translation

TrABin: Trustworthy analyses of binaries

Sound Transpilation from Binary to Machine-Independent Code

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