Decentralized Finance (DeFi) attacks have resulted in significant losses, often orchestrated through Adversarial Exploiter Contracts (AECs) that exploit vulnerabilities in victim smart contracts. To proactively identify such threats, this paper targets the explainable detection of AECs. Existing detection methods struggle to capture semantic dependencies and lack interpretability, limiting their effectiveness and leaving critical knowledge gaps in AEC analysis. To address these challenges, we introduce SEASONED, an effective, self-explanatory, and robust framework for AEC detection. SEASONED extracts semantic information from contract bytecode to construct a semantic relation graph (SRG), and employs a self-counterfactual explainable detector (SCFED) to classify SRGs and generate explanations that highlight the core attack logic. SCFED further enhances robustness, generalizability, and data efficiency by extracting representative information from these explanations. Both theoretical analysis and experimental results demonstrate the effectiveness of SEASONED, which showcases outstanding detection performance, robustness, generalizability, and data efficiency learning ability. To support further research, we also release a new dataset of 359 AECs.

Leaders are seeking AI talent, even during this period of economic uncertainty. Companies at every level of AI sophistication see skills gaps--and are aiming to fill them. Companies across all industries have been scrambling to secure top AI talent from a pool that's not growing fast enough. Even during the economic disruptions and layoffs caused by the COVID-19 pandemic, the demand for AI talent has been strong. Leaders are looking to reduce costs through automation and increased efficiency, and AI has a real role to play in that effort.

Leaders are seeking AI talent, even during this period of economic uncertainty. Companies at every level of AI sophistication see skills gaps--and are aiming to fill them. Companies across all industries have been scrambling to secure top AI talent from a pool that's not growing fast enough. Even during the economic disruptions and layoffs caused by the COVID-19 pandemic, the demand for AI talent has been strong. Leaders are looking to reduce costs through automation and increased efficiency, and AI has a real role to play in that effort.

Companies across all industries have been scrambling to secure top AI talent from a pool that's not growing fast enough. Even during the economic disruptions and layoffs caused by the COVID-19 pandemic, the demand for AI talent has been strong. Leaders are looking to reduce costs through automation and efficiency, and AI has a real role to play in that effort.1 In Deloitte's third edition of the State of AI in the Enterprise survey, we found something unexpected when it came to skill gaps for AI implementations.2 Although a majority of the most mature AI adopters, the Seasoned, reported little or no gap between their AI needs and current abilities, 23 percent said they had a major or extreme one--a higher percentage than the less mature organizations.