## 3 Technical Overview of Wallet Architectures, Attack Vectors, and Related Work
### Related Work  
Numerous papers have proposed tools and methodologies aimed at improving the security of smart contracts, particularly in the Ethereum ecosystem. A common strategy involves the application of static analysis and formal verification techniques to detect known classes of vulnerabilities. For example, [PERSON] et al. [38] introduced Oyente, an early static analyzer that uses symbolic execution to identify issues such as reentry, transaction order dependence, and time-stamped dependence. [PERSON] et al. [39] developed SmartCheck, which performs abstract syntax tree (AST) traversal and pattern matching to report Solidity-specific anti-patterns. Another prominent example is Securify, proposed by [PERSON] et al. [40], which uses Datalog-based logic reasoning to distinguish between compliance and violation patterns, allowing for rule-based classification of contract behaviors. These tools have contributed significantly to the formalization and automation of vulnerability detection in smart contracts. Despite their analytical rigor, these tools often operate under simplified assumptions, such as the absence of external interactions, or rely solely on code-level heuristics. As such, they may fail to capture security flaws that arise dynamically from inter-function logic, user interactions, or unexpected input sequences. Moreover, their output is typically limited to a list of detected issues, without guidance on real-world exploitability or business logic inconsistencies. More recent empirical studies, such as those by [PERSON] et al. [41], have shifted their attention to analyzing deployed contracts to derive vulnerability taxonomies and classify honeypot patterns. While their contribution is valuable in mapping the threat landscape, the methodology is retrospective and does not offer a proactive testing strategy for new contracts prior to deployment.  
In contrast, the framework we proposed in this paper adopts a hybrid approach, combining automated symbolic analysis with structured manual testing. It simulates inter action scenarios reflecting both valid and adversarial behaviors to uncover vulnerabilities that may not be evident through code inspection alone. Rather than focusing exclusively on known vulnerability patterns, the approach emphasizes context-aware testing of access control logic, input validation, and execution paths under edge-case conditions. This integrated methodology enables the identification of latent flaws related to control flow, logic consistency, and functional misuse--areas often overlooked in traditional static analysis pipelines. Furthermore, unlike previous studies that evaluated tools in isolation or focused on the design of analysis frameworks, this paper demonstrates the application of a cohesive tool-assisted security assessment process through an end-to-end case study. By combining formal vulnerability classification (through SWC IDs), severity scoring, and remediation validation, the proposed framework contributes not only a technical method, but also a reproducible auditing model applicable to a broad class of decentralized applications.