Elipmoc: advanced decompilation of Ethereum smart contracts

Grech, Neville; Lagouvardos, Sifis; Tsatiris, Ilias; Smaragdakis, Yannis

doi:10.1145/3527321

Cited by 11 publications

(14 citation statements)

References 17 publications

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“…Regarding (1), the static modeling of the EVM memory in [14] has some similarities with the memory analysis presented in Secs. 3.2 and 3.3, since in both cases we are seeking to model the memory although with different applications in mind.…”

Section: Conclusion and Related Workmentioning

confidence: 89%

“…The construction of the CFG of Ethereum smart contracts is a key part of any decompiler and static analysis tool and has been subject of previous research [13,14,21]. The more precise the CFG is, the more accurate our analysis results will be.…”

Section: Context-sensitive Cfg and Flow-sensitive Static Analysismentioning

confidence: 99%

“…The more precise the CFG is, the more accurate our analysis results will be. In particular, context-sensitivity [14] on the CFG construction is vital to achieve precise results. Our implementation of context-sensitivity is realized by cloning the blocks which are reached from different contexts.…”

Section: Context-sensitive Cfg and Flow-sensitive Static Analysismentioning

confidence: 99%

See 2 more Smart Citations

Inferring Needless Write Memory Accesses on Ethereum Bytecode (Extended Version)

Albert¹,

Fernández²,

Gordillo³

et al. 2023

Preprint

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Efficiency is a fundamental property of any type of program, but it is even more so in the context of the programs executing on the blockchain (known as smart contracts). This is because optimizing smart contracts has direct consequences on reducing the costs of deploying and executing the contracts, as there are fees to pay related to their bytessize and to their resource consumption (called gas). Optimizing memory usage is considered a challenging problem that, among other things, requires a precise inference of the memory locations being accessed. This is also the case for the Ethereum Virtual Machine (EVM) bytecode generated by the most-widely used compiler, solc, whose rather unconventional and low-level memory usage challenges automated reasoning. This paper presents a static analysis, developed at the level of the EVM bytecode generated by solc, that infers write memory accesses that are needless and thus can be safely removed. The application of our implementation on more than 19,000 real smart contracts has detected about 6,200 needless write accesses in less than 4 hours. Interestingly, many of these writes were involved in memory usage patterns generated by solc that can be greatly optimized by removing entire blocks of bytecodes. To the best of our knowledge, existing optimization tools cannot infer such needless write accesses, and hence cannot detect these inefficiencies that affect both the deployment and the execution costs of Ethereum smart contracts.

show abstract

Section: Conclusion and Related Workmentioning

confidence: 89%

Section: Context-sensitive Cfg and Flow-sensitive Static Analysismentioning

confidence: 99%

See 1 more Smart Citation

Inferring Needless Write Memory Accesses on Ethereum Bytecode (Extended Version)

Albert¹,

Fernández²,

Gordillo³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, Gigahorse (Grech et al, 2019) decompiles smart contracts from EVM bytecode into a high-level 3-address code representation, which implicit data-and control-flow dependencies of the EVM bytecode. Elipmoc (Grech et al, 2022) is an evolution of Gigahorse by employing several high-precision heuristics and making them scalable. There are also some tools proposed in the industry to recover solidity-like programs from the bytecode of smart contracts, such as Octopus, Panoramix, ethervm, EVM bytecode decompiler and JEB.…”

Section: Decompilers For Smart Contractsmentioning

confidence: 99%

“…Recent studies (Grech et al, 2019;Brent et al, 2018;Zhou et al, 2018;Grech et al, 2022) have focused on identifying functions for the EVM bytecode by using handcrafted heuristic rules. For example, they suggest a few rules for identifying call-sites and determining the function entry points.…”

Section: Introductionmentioning

confidence: 99%

Neural-FEBI: Accurate Function Identification in Ethereum Virtual Machine Bytecode

He¹,

Li²,

Wang³

et al. 2023

Preprint

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Millions of smart contracts have been deployed onto the Ethereum platform, posing potential attack subjects. Therefore, analyzing contract binaries is vital since their sources are unavailable, involving identification comprising function entry identification and detecting its boundaries. Such boundaries are critical to many smart contract applications, e.g. reverse engineering and profiling. Unfortunately, it is challenging to identify functions from these stripped contract binaries due to the lack of internal function call statements and the compiler-inducing instruction reshuffling. Recently, several existing works excessively relied on a set of handcrafted heuristic rules which impose several faults. To address this issue, we propose a novel neural network-based framework for EVM bytecode Function Entries and Boundaries Identification (neural-FEBI) that does not rely on a fixed set of handcrafted rules. Instead, it used a two-level bi-Long Short-Term Memory network and a Conditional Random Field network to locate the function entries. The suggested framework also devises a control flow traversal algorithm to determine the code segments reachable from the function entry as its boundary. Several experiments on 38,996 publicly available smart contracts collected as binary demonstrate that neural-FEBI confirms the lowest and highest F1-scores for the function entries identification task across different datasets of 88.3 to 99.7, respectively. Its performance on the function boundary identification task is also increased from 79.4% to 97.1% compared with state-of-the-art. We further demonstrate that the identified function information can be used to construct more accurate intra-procedural CFGs and call graphs. The experimental results confirm that the proposed framework significantly outperforms state-of-the-art, often based on handcrafted heuristic rules.

show abstract

Inferring Needless Write Memory Accesses on Ethereum Bytecode

Albert

Fernández

Gordillo

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Efficiency is a fundamental property of any type of program, but it is even more so in the context of the programs executing on the blockchain (known as smart contracts). This is because optimizing smart contracts has direct consequences on reducing the costs of deploying and executing the contracts, as there are fees to pay related to their bytes-size and to their resource consumption (called gas). Optimizing memory usage is considered a challenging problem that, among other things, requires a precise inference of the memory locations being accessed. This is also the case for the Ethereum Virtual Machine (EVM) bytecode generated by the most-widely used compiler, , whose rather unconventional and low-level memory usage challenges automated reasoning. This paper presents a static analysis, developed at the level of the EVM bytecode generated by , that infers write memory accesses that are needless and thus can be safely removed. The application of our implementation on more than 19,000 real smart contracts has detected about 6,200 needless write accesses in less than 4 hours. Interestingly, many of these writes were involved in memory usage patterns generated by that can be greatly optimized by removing entire blocks of bytecodes. To the best of our knowledge, existing optimization tools cannot infer such needless write accesses, and hence cannot detect these inefficiencies that affect both the deployment and the execution costs of Ethereum smart contracts.

show abstract

Elipmoc: advanced decompilation of Ethereum smart contracts

Cited by 11 publications

References 17 publications

Inferring Needless Write Memory Accesses on Ethereum Bytecode (Extended Version)

Inferring Needless Write Memory Accesses on Ethereum Bytecode (Extended Version)

Neural-FEBI: Accurate Function Identification in Ethereum Virtual Machine Bytecode

Inferring Needless Write Memory Accesses on Ethereum Bytecode

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