We propose a ‘Model to Mitigate’ methodology: designing a platform-agnostic model of smart contract business logic and analyzing it before implementation. Using Dynamic Condition Response (DCR) graphs, originally developed for modeling business processes, we formally specify smart contracts and introduce a trace-conformance notion that links DCR-level guarantees to Solidity execution traces. Our method captures high-level properties such as event ordering, role-based access control, and time constraints, enabling the identification of design-rooted vulnerabilities through the discipline of explicit modeling. The DCR formalism requires developers to make concrete decisions about access control, preconditions, initial states, and event ordering-decisions that, when left implicit until implementation, are a documented source of vulnerabilities. Our analysis of real-world exploited and audited smart contracts yields six key insights, demonstrating how DCR-based modeling can enhance smart contract security by surfacing design flaws before they reach deployment. While we validate the approach on existing smart contracts with known flaws (i. e., post-implementation scenarios), the proposed methodology is applicable during design time (pre-development).

Many smart contracts are prone to exploits, which has given rise to analysis tools that try to detect and fix vulnerabilities. Such analysis tools are often trained and evaluated on limited data sets, which has the following drawbacks: 1. The ground truth is often based on the verdict of related tools rather than an actual verification result; 2. Data sets focus on low-level vulnerabilities like reentrancy and overflow; 3. Data sets lack concrete exploit examples. To address these shortcomings, we introduce XploGen, which uses a model-based oracle specification of the business logic of the smart contracts to synthesize valid exploits using LLMs. Our experiments, involving 104 synthesized vulnerability-exploit pairs, demonstrated a 57% success rate in exploiting targeted aspects of the contract. They achieved exploit efficiency with an average of only 3.5 transactions per exploit, highlighting the effectiveness of our methodology.

Logical flaws in smart contracts are often exploited, leading to significant financial losses. Our tool, HighGuard, detects transactions that violate business logic specifications of smart contracts. HighGuard employs dynamic condition response (DCR) graph models as formal specifications to verify contract execution against these models. It is capable of operating in a cross-chain environment for detecting business logic flaws across different blockchain platforms. We demonstrate HighGuard's effectiveness in identifying deviations from specified behaviors in smart contracts without requiring code instrumentation or incurring additional gas costs. By using precise specifications in the monitor, HighGuard achieves detection without false positives. Our evaluation, involving 54 exploits, confirms HighGuard's effectiveness in detecting business logic vulnerabilities.

In this work we propose Dynamit, a monitoring framework to detect reentrancy vulnerabilities in Ethereum smart contracts. The novelty of our framework is that it relies only on transaction metadata and balance data from the blockchain system; our approach requires no domain knowledge, code instrumentation, or special execution environment. Dynamit extracts features from transaction data and uses a machine learning model to classify transactions as benign or harmful. Therefore, not only can we find the contracts that are vulnerable to reentrancy attacks, but we also get an execution trace that reproduces the attack. Using a random forest classifier, our model achieved more than 90 percent accuracy on 105 transactions, showing the potential of our technique.