Andrei Arusoaie

Ethereum is currently the main blockchain ecosystem providing decentralised trust guarantees for applications ranging from finance to e-government. A common criticism of blockchain networks has been their energy consumption and operational costs. The switch from Proof-of-Work (PoW) protocol to Proof-of-Stake (PoS) protocol has significantly reduced this issue, though concerns remain, especially with network expansions via additional layers. The ERC-4337 standard is a recent proposal that facilitates end-user access to Ethereum-backed applications. It introduces a middleware called a bundler, operated as a third-party service, where part of its operational cost is represented by its power consumption. While bundlers have served over 500 million requests in the past two years, fewer than 15 official bundler providers exist, compared to over 100 regular Ethereum access providers. In this paper, we provide a first look at the active power consumption overhead that a bundler would add to an Ethereum access service. Using SmartWatts, a monitoring system leveraging Running Average Power Limit (RAPL) hardware interfaces, we empirically determine correlations between the bundler workload and its active power consumption.

The high rate of false alarms from static analysis tools and Large Language Models (LLMs) complicates vulnerability detection in Solidity Smart Contracts, demanding methods that can formally or empirically prove the presence of defects. This paper introduces a novel detection pipeline that integrates custom Slither-based detectors, LLMs, Kontrol, and Forge. Our approach is designed to reliably detect defects and generate proofs. We currently perform experiments with promising results for seven types of critical defects. We demonstrate the pipeline's efficacy by presenting our findings for three vulnerabilities -- Reentrancy, Complex Fallback, and Faulty Access Control Policies -- that are challenging for current verification solutions, which often generate false alarms or fail to detect them entirely. We highlight the potential of either symbolic or concrete execution in correctly classifying such code faults. By chaining these instruments, our method effectively validates true positives, significantly reducing the manual verification burden. Although we identify potential limitations, such as the inconsistency and the cost of LLMs, our findings establish a robust framework for combining heuristic analysis with formal verification to achieve more reliable and automated smart contract auditing.

This paper serves as a progress report on our research, specifically focusing on utilizing interval analysis, an existing static analysis method, for detecting vulnerabilities in smart contracts. We present a selection of motivating examples featuring vulnerable smart contracts and share the results from our experiments conducted with various existing detection tools. Our findings reveal that these tools were unable to detect the vulnerabilities in our examples. To enhance detection capabilities, we implement interval analysis on top of Slither [3], an existing detection tool, and demonstrate its effectiveness in identifying certain vulnerabilities that other tools fail to detect.

Complex frameworks for defining programming languages aim to generate various tools (e.g. interpreters, symbolic execution engines, deductive verifiers, etc.) using only the formal definition of a language. When used at an industrial scale, these tools are constantly updated, and at the same time, it is required to be trustworthy. Ensuring the correctness of such a framework is practically impossible. A solution is to generate proof objects as correctness artefacts that can be checked by an external trusted checker. A logic suitable for developing such frameworks is matching logic. K framework is a canonical example having matching logic-based foundation. Since the (symbolic) configurations of the programs are represented by matching logic patterns, the algorithms computing the dynamics of these configurations can be seen as pattern transformers and a proof object should be generated for the relationship between these patterns. In this paper, we show that conjunctions and disjunctions of patterns, produced by semantics or analysis rules, can be safely normalized using unification and antiunification algorithms. We also provide a prototype implementation of our proof object generation technique and a checker for certifying the generated objects.

Derivatives are a special type of financial contracts used to hedge risks or to speculate on the market fluctuations. In order to avoid ambiguities and misinterpretations, several domain specific languages (DSLs) for specifying such derivatives have been proposed. The recent development of the blockchain technologies enables the automatic execution of financial derivatives. Once deployed on the blockchain, a derivative cannot be modified. Therefore, more caution should be taken in order to avoid undesired situations. In this paper, we address the formal verification of financial derivatives written in a DSL for blockchain, called Findel. We identify a list of properties that, once proved, they exclude several security vulnerabilities (e.g., immutable bugs, money losses). We develop an infrastructure that provides means to interactively formalize and prove such properties. To provide a higher confidence, we also generate proof certificates. We use our infrastructure to certify non-trivial examples that cover the most common types of derivatives (forwards/futures, swaps, options).