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@INPROCEEDINGS{9678888,
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author={Choi, Jaeseung and Kim, Doyeon and Kim, Soomin and Grieco, Gustavo and Groce, Alex and Cha, Sang Kil},
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booktitle={2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)},
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title={SMARTIAN: Enhancing Smart Contract Fuzzing with Static and Dynamic Data-Flow Analyses},
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year={2021},
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volume={},
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number={},
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pages={227-239},
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doi={10.1109/ASE51524.2021.9678888}}
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@inproceedings{10.1145/3578527.3578538,
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author = {Pani, Siddhasagar and Nallagonda, Harshita Vani and Vigneswaran and Medicherla, Raveendra Kumar and Rajan M},
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title = {SmartFuzzDriverGen: Smart Contract Fuzzing Automation for Golang},
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year = {2023},
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isbn = {9798400700644},
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publisher = {Association for Computing Machinery},
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address = {New York, NY, USA},
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url = {https://doi.org/10.1145/3578527.3578538},
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doi = {10.1145/3578527.3578538},
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abstract = {Greybox fuzzers require intermediate programs called fuzz drivers to test smart contract APIs. These fuzz drivers use the semi-random inputs (bytes) generated by fuzzers to prepare suitable inputs required to test APIs. Further, fuzz driver also uses this input to decide sequence in which APIs to be invoked and enables the fuzzer to execute the APIs in that sequence to find the vulnerabilities, if any. Manually writing such complex and intelligent fuzz drivers is laborious, requires deep technical skills, hence can be cumbersome and error prone. In this paper, we propose SmartFuzzDriverGen framework to automatically generate fuzz drivers which invoke smart contract APIs using different strategies: unit-level, sequence-based (random, user-defined), and heuristics based. We evaluate the proposed framework by testing a prototype implementation of it with Golang smart contracts (targeted for Hyperledger Fabric platform) and study the effectiveness of the generated fuzz drivers in terms of code coverage as well as bug finding abilities. We observed that fuzzing of APIs in random sequences performed better than the other methods.},
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booktitle = {Proceedings of the 16th Innovations in Software Engineering Conference},
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articleno = {14},
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numpages = {11},
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keywords = {smart contracts, vulnerability detection, automated driver generation, blockchain, fuzzing, sequencing},
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location = {Allahabad, India},
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series = {ISEC '23}
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}
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@inproceedings {217464,
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author = {Johannes Krupp and Christian Rossow},
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title = {{teEther}: Gnawing at Ethereum to Automatically Exploit Smart Contracts},
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booktitle = {27th USENIX Security Symposium (USENIX Security 18)},
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year = {2018},
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isbn = {978-1-939133-04-5},
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address = {Baltimore, MD},
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pages = {1317--1333},
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url = {https://www.usenix.org/conference/usenixsecurity18/presentation/krupp},
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publisher = {USENIX Association},
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month = aug
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}
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@inproceedings{10.1145/3243734.3243780,
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author = {Tsankov, Petar and Dan, Andrei and Drachsler-Cohen, Dana and Gervais, Arthur and B\"{u}nzli, Florian and Vechev, Martin},
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title = {Securify: Practical Security Analysis of Smart Contracts},
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year = {2018},
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isbn = {9781450356930},
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publisher = {Association for Computing Machinery},
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address = {New York, NY, USA},
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url = {https://doi.org/10.1145/3243734.3243780},
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doi = {10.1145/3243734.3243780},
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abstract = {Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third parties. Despite their potential, repeated security concerns have shaken the trust in handling billions of USD by smart contracts. To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property. Securify's analysis consists of two steps. First, it symbolically analyzes the contract's dependency graph to extract precise semantic information from the code. Then, it checks compliance and violation patterns that capture sufficient conditions for proving if a property holds or not. To enable extensibility, all patterns are specified in a designated domain-specific language. Securify is publicly released, it has analyzed >18K contracts submitted by its users, and is regularly used to conduct security audits by experts. We present an extensive evaluation of Securify over real-world Ethereum smart contracts and demonstrate that it can effectively prove the correctness of smart contracts and discover critical violations.},
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booktitle = {Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security},
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pages = {67–82},
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numpages = {16},
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keywords = {smart contracts, stratified datalog, verification, security analysis},
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location = {Toronto, Canada},
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series = {CCS '18}
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}
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\citation{10.1145/3243734.3243780}
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\citation{10.1145/3578527.3578538}
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\citation{217464}
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\citation{9678888}
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\bibdata{exercise.bib}
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\bibcite{9678888}{1}
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\begin{thebibliography}{1}
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\bibitem{9678888}
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Jaeseung Choi, Doyeon Kim, Soomin Kim, Gustavo Grieco, Alex Groce, and Sang~Kil
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Cha.
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\newblock Smartian: Enhancing smart contract fuzzing with static and dynamic
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data-flow analyses.
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\newblock In {\em 2021 36th IEEE/ACM International Conference on Automated
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Software Engineering (ASE)}, pages 227--239, 2021.
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\bibitem{217464}
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Johannes Krupp and Christian Rossow.
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\newblock {teEther}: Gnawing at ethereum to automatically exploit smart
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contracts.
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\newblock In {\em 27th USENIX Security Symposium (USENIX Security 18)}, pages
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1317--1333, Baltimore, MD, August 2018. USENIX Association.
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\bibitem{10.1145/3578527.3578538}
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Siddhasagar Pani, Harshita~Vani Nallagonda, Vigneswaran, Raveendra~Kumar
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Medicherla, and Rajan M.
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\newblock Smartfuzzdrivergen: Smart contract fuzzing automation for golang.
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\newblock In {\em Proceedings of the 16th Innovations in Software Engineering
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Conference}, ISEC '23, New York, NY, USA, 2023. Association for Computing
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Machinery.
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\bibitem{10.1145/3243734.3243780}
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Petar Tsankov, Andrei Dan, Dana Drachsler-Cohen, Arthur Gervais, Florian
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B\"{u}nzli, and Martin Vechev.
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\newblock Securify: Practical security analysis of smart contracts.
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\newblock In {\em Proceedings of the 2018 ACM SIGSAC Conference on Computer and
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Communications Security}, CCS '18, page 67–82, New York, NY, USA, 2018.
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Association for Computing Machinery.
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\end{thebibliography}
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\title{192.127 Seminar in Software Engineering (Smart Contracts) \\
|
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SWC-124: Write to Arbitrary Storage Location}
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\author{Exercises}
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\date{WT 2023/24}
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\author{\textbf{*** YOUR NAME AND STUDENT ID ***}}
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|
\maketitle
|
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|
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||||||
|
\section{Weakness and consequences}
|
||||||
|
|
||||||
|
\subsection{Solidity storage layout}
|
||||||
|
|
||||||
|
Any contract's storage is a continuous 256-bit address space consisting of 32-bit values. In order to implement dynamically sized data structures like maps and arrays, Solidity distributes their entries in a pseudo-random location. Due to the vast 256-bit range of addresses collisions are statistically extremely improbable and of no practical relevance.
|
||||||
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||||||
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\medspace
|
||||||
|
|
||||||
|
In the case of a dynamic array at variable slot $p$, data is written to continuous locations starting at $keccak(p)$. The array itself contains the length information. It is worth noting that Solidity does not come with utility functions to manipulate arrays, and the developer is required to correctly maintain the length value in order to keep track of the array's state.
|
||||||
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||||||
|
\medspace
|
||||||
|
|
||||||
|
For maps stored in variable slot $p$ the data for index $k$ can be found at $keccak(k . p)$ where $.$ is the concatenation operator.
|
||||||
|
|
||||||
|
\subsection{The Weakness}
|
||||||
|
|
||||||
|
Any unchecked array write is potentially dangerous, as the storage-location of all variables is publicly known and an unconstrained array index can be reverse engineered to target them.
|
||||||
|
|
||||||
|
\lstset{style=mystyle}
|
||||||
|
\begin{algorithm}
|
||||||
|
\begin{lstlisting}[language=Octave]
|
||||||
|
pragma solidity 0.4.25;
|
||||||
|
|
||||||
|
contract MyContract {
|
||||||
|
uint[] private arr;
|
||||||
|
address private owner;
|
||||||
|
|
||||||
|
function write(unit index, uint value) {
|
||||||
|
arr[index] = value;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
\end{lstlisting}
|
||||||
|
\caption{A completely unchecked array write}
|
||||||
|
\label{alg:agf-opt-merge}
|
||||||
|
\end{algorithm}
|
||||||
|
|
||||||
|
In the case of dynamic arrays an improper constraint of the $length$ can be dangerous. As $length$ is unsigned, it is possible to underflow it past $2^{256} - 1$ by decrementing the length below zero, thereby effectively marking the whole address space as part of it.
|
||||||
|
|
||||||
|
\section{Vulnerable contracts in literature}
|
||||||
|
|
||||||
|
collect vulnerable contracts used by different papers to motivate/illustrate the weakness
|
||||||
|
|
||||||
|
\section{Code properties and automatic detection}
|
||||||
|
|
||||||
|
summarize the code properties that tools are looking for so that they can detect the weakness
|
||||||
|
|
||||||
|
\section{Exploit sketch}
|
||||||
|
|
||||||
|
sketch ways to potentially exploit the different variants of the weakness.
|
||||||
|
|
||||||
|
%remove this later%
|
||||||
|
\cite{10.1145/3243734.3243780}
|
||||||
|
\cite{10.1145/3578527.3578538}
|
||||||
|
\cite{217464}
|
||||||
|
\cite{9678888}
|
||||||
|
|
||||||
|
\bibliography{exercise.bib}
|
||||||
|
|
||||||
|
\end{document}
|
||||||
|
|
||||||
|
|
||||||
Reference in New Issue
Block a user