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1. Securing Smart Contracts with Information Flow

   Cecchetti, Ethan ; Yao, Siqiu ; Ni, Haobin ; Myers, Andrew C. ( May 2020 , International Symposium on Foundations and Applications of Blockchain)

   Securing blockchain smart contracts is difficult, especially when they interact with one another. Existing tools for reasoning about smart contract security are limited in one of two ways: they either cannot analyze cooperative interaction between contracts, or they require all interacting code to be written in a specific language. We propose an approach based on information flow control~(IFC), which supports fine-grained, compositional security policies and rules out dangerous vulnerabilities. However, existing IFC systems provide few guarantees on interaction with legacy contracts and unknown code. We extend existing IFC constructs to support these important functionalities while retaining compositional security guarantees, including reentrancy control. We mix static and dynamic mechanisms to achieve these goals in a flexible manner while minimizing run-time costs. 

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2. Adding concurrency to smart contracts

   https://doi.org/10.1145/3087801.3087835  

   Thomas Dickerson, Paul Gazzillo ( June 2020 , Distributed computing)

   null (Ed.)

   Modern cryptocurrency systems, such as the Ethereum project, permit complex financial transactions through scripts called smart contracts. These smart contracts are executed many, many times, always without real concurrency. First, all smart contracts are serially executed by miners before appending them to the blockchain. Later, those contracts are serially re-executed by validators to verify that the smart contracts were executed correctly by miners. Serial execution limits system throughput and fails to exploit today’s concurrent multicore and cluster architectures. Nevertheless, serial execution appears to be required: contracts share state, and contract programming languages have a serial semantics. This paper presents a novel way to permit miners and validators to execute smart contracts in parallel, based on techniques adapted from software transactional memory. Miners execute smart contracts speculatively in parallel, allowing non-conflicting contracts to proceed concurrently, and “discovering” a serializable concurrent schedule for a block’s transactions, This schedule is captured and encoded as a deterministic fork-join program used by validators to re-execute the miner’s parallel schedule deterministically but concurrently. We have proved that the validator’s execution is equivalent to miner’s execution. Smart contract benchmarks run on a JVM with ScalaSTM show that a speedup of 1.39× can be obtained for miners and 1.59× for validators with just three concurrent threads. 

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3. SGXonerated: Finding (and Partially Fixing) Privacy Flaws in TEE-based Smart Contract Platforms Without Breaking the TEE

   https://doi.org/10.56553/popets-2024-0035  

   Jean-Louis, Nerla ; Li, Yunqi ; Ji, Yan ; Malvai, Harjasleen ; Yurek, Thomas ; Bellemare, Sylvain ; Miller, Andrew ( January 2024 , Proceedings on Privacy Enhancing Technologies)

   TEE-based smart contracts are an emerging blockchain architecture, offering fully programmable privacy with better performance than alternatives like secure multiparty computation. They can also support compatibility with existing smart contract languages, such that existing (plaintext) applications can be readily ported, picking up privacy enhancements automatically. While previous analysis of TEE-based smart contracts have focused on failures of TEE itself, we asked whether other aspects might be understudied. We focused on state consistency, a concern area highlighted by Li et al., as well as new concerns including access pattern leakage and software upgrade mechanisms. We carried out a code review of a cohort of four TEE-based smart contract platforms. These include Secret Network, the first to market with in-use applications, as well as Oasis, Phala, and Obscuro, which have at least released public test networks.The first and most broadly applicable result is that access pattern leakage occurs when handling persistent contract storage. On Secret Network, its fine-grained access pattern is catastrophic for the transaction privacy of SNIP-20 tokens. If ERC-20 tokens were naively ported to Oasis they would be similarly vulnerable; the others in the cohort leak coarse-grained information at approximately the page level (4 kilobytes). Improving and characterizing this will require adopting techniques from ORAMs or encrypted databases.Second, the importance of state consistency has been underappreciated, in part because exploiting such vulnerabilities is thought to be impractical. We show they are fully practical by building a proof-of-concept tool that breaks all advertised privacy properties of SNIP-20 tokens, able to query the balance of individual accounts and the token amount of each transfer. We additionally demonstrate MEV attacks against the Sienna Swap application. As a final consequence of lacking state consistency, the developers have inadvertently introduced a decryption backdoor through their software upgrade process. We have helped the Secret developers mitigate this through a coordinated vulnerability disclosure, after which their state consistency should be roughly on par with the rest.

    

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4. A Resource-Efficient Smart Contract for Privacy Preserving Smart Home Systems

   https://doi.org/10.1109/SWC50871.2021.00079  

   Saquib, Nazmus ; Bakir, Fatih ; Krintz, Chandra ; Wolski, Rich ( October 2021 , IEEE SmartWorld)

   Due to the proliferation of IoT and the popularity of smart contracts mediated by blockchain, smart home systems have become capable of providing privacy and security to their occupants. In blockchain-based home automation systems, business logic is handled by smart contracts securely. However, a blockchain-based solution is inherently resource-intensive, making it unsuitable for resource-constrained IoT devices. Moreover, time-sensitive actions are complex to perform in a blockchainbased solution due to the time required to mine a block. In this work, we propose a blockchain-independent smart contract infrastructure suitable for resource-constrained IoT devices. Our proposed method is also capable of executing time-sensitive business logic. As an example of an end-to-end application, we describe a smart camera system using our proposed method, compare this system with an existing blockchain-based solution, and present an empirical evaluation of their performance. 

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5. What kinds of contracts do ML APIs need?

   Khairunnesa, Samantha Syeda ; Ahmed, Shibbir ; Imtiaz, Sayem Mohammad ; Rajan, Hridesh ; Leavens, Gary T ( October 2023 , Empirical software engineering)

   Feldt, Robert ; Zimmermann, Thomas ; Basili, Victor R ; Briand, Lionel C (Ed.)

   Recent work has shown that Machine Learning (ML) programs are error-prone and called for contracts for ML code. Contracts, as in the design by contract methodology, help document APIs and aid API users in writing correct code. The question is: what kinds of contracts would provide the most help to API users? We are especially interested in what kinds of contracts help API users catch errors at earlier stages in the ML pipeline. We describe an empirical study of posts on Stack Overflow of the four most often-discussed ML libraries: TensorFlow, Scikit-learn, Keras, and PyTorch. For these libraries, our study extracted 413 informal (English) API specifications. We used these specifications to understand the following questions. What are the root causes and effects behind ML contract violations? Are there common patterns of ML contract violations? When does understanding ML contracts require an advanced level of ML software expertise? Could checking contracts at the API level help detect the violations in early ML pipeline stages? Our key findings are that the most commonly needed contracts for ML APIs are either checking constraints on single arguments of an API or on the order of API calls. The software engineering community could employ existing contract mining approaches to mine these contracts to promote an increased understanding of ML APIs. We also noted a need to combine behavioral and temporal contract mining approaches. We report on categories of required ML contracts, which may help designers of contract languages. 

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