Towards Smart Hybrid Fuzzing for Smart Contracts

Smart contracts are Turing-complete programs that are executed across a blockchain network. Unlike traditional programs, once deployed they cannot be modified. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In recent years, smart contracts suffered major exploits, costing millions of dollars, due to programming errors. As a result, a variety of tools for detecting bugs has been proposed. However, majority of these tools often yield many false positives due to over-approximation or poor code coverage due to complex path constraints. Fuzzing or fuzz testing is a popular and effective software testing technique. However, traditional fuzzers tend to be more effective towards finding shallow bugs and less effective in finding bugs that lie deeper in the execution. In this work, we present CONFUZZIUS, a hybrid fuzzer that combines evolutionary fuzzing with constraint solving in order to execute more code and find more bugs in smart contracts. Evolutionary fuzzing is used to exercise shallow parts of a smart contract, while constraint solving is used to generate inputs which satisfy complex conditions that prevent the evolutionary fuzzing from exploring deeper paths. Moreover, we use data dependency analysis to efficiently generate sequences of transactions, that create specific contract states in which bugs may be hidden. We evaluate the effectiveness of our fuzzing strategy, by comparing CONFUZZIUS with state-of-the-art symbolic execution tools and fuzzers. Our evaluation shows that our hybrid fuzzing approach produces significantly better results than state-of-the-art symbolic execution tools and fuzzers

AMR:Autonomous Coin Mixer with Privacy Preserving Reward Distribution

It is well known that users on open blockchains are tracked by an industry providing services to governments, law enforcement, secret services, and alike. While most blockchains do not protect their users' privacy and allow external observers to link transactions and addresses, a growing research interest attempts to design add-on privacy solutions to help users regain their privacy on non-private blockchains. In this work, we propose to our knowledge the first censorship resilient mixer, which can reward its users in a privacy-preserving manner for participating in the system. Increasing the anonymity set size, and diversity of users, is, as we believe, an important endeavor to raise a mixer's contributed privacy in practice. The paid-out rewards can take the form of governance tokens to decentralize the voting on system parameters, similar to how popular "DeFi farming" protocols operate. Moreover, by leveraging existing "Defi" lending platforms, AMR is the first mixer design that allows participating clients to earn financial interests on their deposited funds. Our system AMR is autonomous as it does not rely on any external server or third party. The evaluation of our AMR implementation shows that the system supports today on Ethereum anonymity set sizes beyond thousands of users, and a capacity of over $66,000$ deposits per day, at constant system costs. We provide a formal specification of our zksnark-based AMR system, a privacy and security analysis, implementation, and evaluation with both the MiMC and Poseidon hash functions

Quantifying Blockchain Extractable Value: How dark is the forest?

Permissionless blockchains such as Bitcoin have excelled at financial services. Yet, opportunistic traders extract monetary value from the mesh of decentralized finance (DeFi) smart contracts through so-called blockchain extractable value (BEV). The recent emergence of centralized BEV relayer portrays BEV as a positive additional revenue source. Because BEV was quantitatively shown to deteriorate the blockchain’s consensus security, BEV relayers endanger the ledger security by incentivizing rational miners to fork the chain. For example, a rational miner with a 10% hashrate will fork Ethereum if a BEV opportunity exceeds 4× the block reward. However, related work is currently missing quantitative insights on past BEV extraction to assess the practical risks of BEV objectively. In this work, we allow to quantify the BEV danger by deriving the USD extracted from sandwich attacks, liquidations, and decentralized exchange arbitrage. We estimate that over 32 months, BEV yielded 540.54M USD in profit, divided among 11,289 addresses when capturing 49,691 cryptocurrencies and 60,830 on-chain markets. The highest BEV instance we find amounts to 4.1M USD, 616.6× the Ethereum block reward. Moreover, while the practitioner’s community has discussed the existence of generalized trading bots, we are, to our knowledge, the first to provide a concrete algorithm. Our algorithm can replace unconfirmed transactions without the need to understand the victim transactions’ underlying logic, which we estimate to have yielded a profit of 57,037.32 ETH (35.37M USD) over 32 months of past blockchain data. Finally, we formalize and analyze emerging BEV relay systems, where miners accept BEV transactions from a centralized relay server instead of the peer-to-peer (P2P) network. We find that such relay systems aggravate the consensus layer attacks and therefore further endanger blockchain security

Applying Private Information Retrieval to Lightweight Bitcoin Clients

Lightweight Bitcoin clients execute a Simple Payment Verification (SPV) protocol to verify the validity of transactions related to a particular user. Currently, lightweight clients use Bloom filters to significantly reduce the amount of bandwidth required to validate a particular transaction. This is despite the fact that research has shown that Bloom filters are insufficient at preserving the privacy of clients' queries. In this paper we describe our design of an SPV protocol that leverages Private Information Retrieval (PIR) to create fully private and performant queries. We show that our protocol has a low bandwidth and latency cost; properties that make our protocol a viable alternative for lightweight Bitcoin clients and other cryptocurrencies with a similar SPV model. In contract to Bloom filters, our PIR-based approach offers deterministic privacy to the user. Among our results, we show that in the worst case, clients who would like to verify 100 transactions occurring in the past week incurs a bandwidth cost of 33.54 MB with an associated latency of approximately 4.8 minutes, when using our protocol. The same query executed using the Bloom-filter-based SPV protocol incurs a bandwidth cost of 12.85 MB; this is a modest overhead considering the privacy guarantees it provides

Do you need a Blockchain?

Blockchain is being praised as a technological innovation which allows to revolutionize how society trades and interacts. This reputation is in particular attributable to its properties of allowing mutually mistrusting entities to exchange financial value and interact without relying on a trusted third party. A blockchain moreover provides an integrity protected data storage and allows to provide process transparency. In this article we critically analyze whether a blockchain is indeed the appropriate technical solution for a particular application scenario. We differentiate between permissionless (e.g., Bitcoin/Ethereum) and permissioned (e.g. Hyperledger/Corda) blockchains and contrast their properties to those of a centrally managed database. We provide a structured methodology to determine the appropriate technical solution to solve a particular application problem. Given our methodology, we analyze in depth three use cases --- Supply Chain Management, Interbank and International Payments, and Decentralized Autonomous Organizations and conclude the article with an outlook for further opportunities