The paper analyzes the nascent DeFi investment agent market, finding that while token valuations are high, current deployments are heterogeneous, lack clear autonomous execution, and exhibit poor risk…

The paper empirically analyzes the nascent DeFi investment agent market, finding that while token valuations are high, current deployments lack robust autonomous execution and exhibit poor risk-adjust…

The paper introduces SseRex, a novel symbolic execution framework designed to detect unique and complex vulnerabilities in Solana smart contracts, significantly outperforming existing tools.

The paper investigates whether tokenizing real-world assets actually improves liquidity, finding that liquidity is highly heterogeneous across asset types and is not reliably predicted by the outstand…

The paper analyzes and documents various double-dip reward abuse attacks that exploit flaws in how cashback and reward engines handle transaction refunds, proposing formal invariants and defensive alg…

The paper proposes a novel structural invariant approach, derived from the economic constraints of fraud, that amplifies weak, low-precision signals into highly accurate fraud detections without requi…

GenDetect introduces a novel framework to rapidly generalize detection rules from single observed DeFi exploits, significantly improving resilience against subsequent, similar 'Imitative Attack Cascad…

The paper analyzes Ethereum builder transactions to show that builder centralization is an emergent property of the Proposer-Builder Separation (PBS) architecture, driven by specific order flow and ME…

The paper investigates speculative Oracle Extractable Value (OEV) on Layer-2 blockchains, demonstrating that predictable latency differences in cross-chain oracle updates allow for profitable cross-ch…

This paper analyzes the revenue drivers and interconnected risks of liquid restaking protocols, finding that while multi-blockchain expansion is key for adoption, the current bridge risk does not pose…

UniDetect is a novel LLM-driven method that detects cross-chain cryptocurrency fraud by generating generalized transaction summaries, significantly outperforming existing detection techniques across m…

LiquiLM is a novel framework that combines Large Language Models (LLMs) with a Dynamic Co-Attention Network (DCN) to effectively bridge the semantic gap between complex smart contract code and high-le…

The paper evaluates graph-context LLM defenders against multi-round, adaptive fraud attacks, finding that while graph context improves early safety, it significantly increases benign over-refusal due…

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

This paper critically re-evaluates the use of Graph Neural Networks (GNNs) for Bitcoin fraud detection, demonstrating that under strict, leakage-free temporal evaluation, simple feature-only models si…

The paper analyzes arbitrage competition on high-throughput blockchains, finding that while probabilistic search accounts for a small fraction of activity, it is disproportionately responsible for spa…

SAGE introduces a novel counterfactual-aware negative harvesting ensemble that accurately identifies fraudulent activity by robustly distinguishing it from legitimate, rare user behaviors in unlabeled…

SCAFDS introduces a novel, seven-stage graph attention system that models fraud propagation using co-occurrence edge features and generates forensically traceable SAR narratives, significantly improvi…

This paper investigates how Generative AI enables scalable, hyper-realistic fraud in Chinese e-commerce by fabricating product defect evidence, proposing new defense mechanisms like verifiable materia…