This paper proposes TrapHunter, a framework to identify deceptive trap tokens in standardized contracts using intent deviation analysis and dynamic validation.

The paper proposes DUDE, a two-stage framework that significantly reduces the susceptibility of web agents to deceptive user interfaces by integrating deception detection into the agent's learning pro…

This paper provides a comprehensive, structured list of 42 email-based deception techniques, complete with 64 concrete examples, to serve as a modular reference for developing countermeasures.

The paper simulates bargaining scenarios using LLM agents to analyze how optimizing agents for financial profit affects their honesty and trust, finding that while fine-tuning improves deal-making, it…

The paper proposes a mutagenic incentive intervention approach that mitigates collusion in embodied multi-agent systems by reshaping agents' payoff structures, effectively inducing defection and maint…

LibScan is an automated framework that detects eight categories of smart contract library misuse by combining LLM-based semantic reasoning with rule-based analysis, achieving 85.15% accuracy on real-w…

This paper introduces ECO/CPO-DAG, a domain-specific accountability protocol for adversarial supply chains that uses contradiction detection as a supplemental validation layer.

ContractShield is a robust multimodal framework that uses a novel three-level fusion mechanism to accurately detect multiple types of vulnerabilities in obfuscated smart contracts, significantly outpe…

The paper demonstrates that the current per-token billing model for LLMs is susceptible to systematic overcharging because auditing frameworks must rely on evidence provided by the very companies that…

The paper demonstrates that the current per-token billing model for LLMs is susceptible to systematic inflation because auditing frameworks must rely on evidence provided by the service provider, crea…

This paper investigates the survival of licenses in the supply chain of AI artifacts, finding that a large percentage of artifacts lack declared licenses and that obligation-bearing licenses have low…

The paper introduces Phoenix, a training-free multi-agent framework that detects code vulnerabilities by synthesizing project-specific behavioral contracts, significantly outperforming existing method…

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 argues that post-hoc mitigation techniques like machine unlearning are insufficient to cure legal liability arising from the unlawful acquisition and training on copyrighted data, advocating…

The paper introduces Bastet, a novel, high-quality, expert-labeled dataset designed to overcome limitations in existing resources for detecting complex smart contract vulnerabilities in DeFi.

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 identifies Security-Recall Divergence (SRD), demonstrating that omission constraints (prohibitions) decay significantly in long-context LLM conversations, while commission constraints (requ…

The paper introduces alignment contracts, a formal framework for specifying and enforcing behavioral constraints over observable effect traces, ensuring that powerful agentic security systems operate…

The paper introduces Document-Driven Implicit Payload Execution (DDIPE) to demonstrate that malicious code can be embedded in LLM agent skill documentation, allowing supply-chain attacks to hijack age…

This paper introduces Dependency Steering, a novel attack paradigm demonstrating that malicious agent skills can actively bias LLM coding agents to use attacker-controlled packages, posing a significa…