New Highlights

We study the risks of validator reuse across multiple services in a restaking protocol. We

characterize the robust security of a restaking network as a function of the buffer between the

costs and profits from attacks. For example, our results imply that if attack costs always exceed

attack profits by 10%, then a sudden loss of .1% of the overall stake (e.g., due to a software

error) cannot result in the ultimate loss of more than 1.1% of the overall stake. We also provide

local analogs of these overcollateralization conditions and robust security guarantees that apply

specifically for a target service or coalition of services. All of our bounds on worst-case stake loss

are the best possible. Finally, we bound the maximum-possible length of a cascade of attacks.

Our results suggest measures of robustness that could be exposed to the participants in a

restaking protocol. We also suggest polynomial-time computable sufficient conditions that can

proxy for these measures. See Robust Restaking Networks.

Users bid in a transaction fee mechanism (TFM) to get their transactions included and

confirmed by a blockchain protocol. Roughgarden (EC’21) initiated the formal treatment of

TFMs and proposed three requirements: user incentive compatibility (UIC), miner incentive

compatibility (MIC), and a form of collusion-resilience called OCA-proofness. Ethereum’s EIP-

1559 mechanism satisfies all three properties simultaneously when there is no contention between

transactions, but loses the UIC property when there are too many eligible transactions to fit in a

single block. Chung and Shi (SODA’23) considered an alternative notion of collusion-resilience,

called c-side-contract-proofness (c-SCP), and showed that, when there is contention between

transactions, no TFM can satisfy UIC, MIC, and c-SCP for any c ≥ 1. OCA-proofness asserts

that the users and a miner should not be able to “steal from the protocol.” On the other hand,

the c-SCP condition requires that a coalition of a miner and a subset of users should not be able

to profit through strategic deviations (whether at the expense of the protocol or of the users

outside the coalition). Our main result is the first proof that, when there is contention between transactions, no

(possibly randomized) TFM in which users are expected to bid truthfully satisfies UIC, MIC,

and OCA-proofness. This result resolves the main open question in Roughgarden (EC’21). We

also suggest several relaxations of the basic model that allow our impossibility result to be

circumvented. See Collusion-Resilience in Transaction Fee Mechanism Design.

Smart contracts are software programs that enable diverse business activities on the blockchain. Recent research has identified new classes of ”machine un-auditable” bugs that arise from both transactional contexts and source code. Existing detection methods require human understanding of underlying transaction logic and manual reasoning across different sources of context (i.e., modalities), such as code, dynamic transaction executions, and natural language specifying the expected transaction behavior. To automate the detection of “machine un-auditable” bugs, we present SMARTINV, an accurate and fast smart contract invariant inference framework. Our key insight is that the expected behavior of smart contracts, as specified by invariants, relies on understanding and reasoning across multimodal information, such as source code and natural language. We propose a new prompting strategy to foundation models, Tier of Thought (ToT), to reason across multiple modalities of smart contracts and ultimately to generate invariants. By checking the violation of these generated invariants, SMARTINV can identify potential vulnerabilities. We evaluate SMARTINV on real-world contracts and rediscover bugs that resulted in multi-million dollar losses over the past 2.5 years (from January 1, 2021 to May 31, 2023). Our extensive evaluation shows that SMARTINV generates (3.5×) more bug-critical invariants and detects (4×) more critical bugs compared to the state-of-the-art tools in significantly (150×) less time. SMARTINV uncovers 119 zero-day vulnerabilities from the 89,621 real-world contracts. Among them, five are critical zero-day bugs confirmed by developers as “high severity.” See SmartInv: Multimodal Learning for Smart Contract Invariant Inference.