This paper provides a theoretical analysis of Cumulative Prospect Theory (CPT) for multi-objective and multi-criteria decision-making under risk, focusing on Neilson's definitions of aversion and prop…

This study proposes a negotiation framework, using composite indices (RBTI and CATI), to explain how youth navigate competing privacy pressures when using smart voice assistants, finding that high usa…

AI agents using persuasive framing increased cooperation in small groups but effects were short-lived, while antisocial framing had larger and more persistent negative effects.

This paper studies the tension between speed and safety in technological races using a framed behavioral experiment on artificial intelligence development.

The paper argues that despite the focus on risk, the cybersecurity profession is structurally trained as a threat-management discipline, leading to poor foundational risk reasoning among professionals…

The paper introduces a comprehensive framework, Realtime Risk Studio, that operationalizes qualitative risk models (Bowtie diagrams) into formal, probabilistic, and intervention-ready runtime models u…

This paper shows that standard optimal control in Markov Decision Processes (MDPs) with an absorbing catastrophic state naturally generates behavioral signatures mimicking prospect theory, even withou…

This paper investigates safety dynamics in the use of Role-play AI Companions through interviews and a 14-day assessment, identifying key factors shaping these dynamics and revealing short-term emotio…

This study demonstrates that an LLM's assigned support role (e.g., Inform, Coach, Relate) significantly alters its safety profile and the types of risks it presents when assisting users in complex car…

The paper argues that LLM guardrails and persona dynamics create an unethical 'reality gap' by laundering epistemic risk onto users, advocating for task-level causal requirements over response-level m…

This study profiles user vulnerability to phishing by identifying key psychological and behavioral factors, revealing that most users are high-risk due to hasty decision-making rather than lacking tec…

The paper identifies that the convenience of host-acting agents leads to semantic under-specification in user goals, which forces the agent to generate potentially risky execution plans.

This paper systematically evaluates how LLMs uncritically adapt to potentially dangerous user prompts related to eating disorders, finding that specific linguistic cues significantly increase the like…

The paper introduces SafetyDrift, a predictive model that forecasts when AI agents will violate safety protocols by analyzing the cumulative risk across sequences of individually safe actions.

The study analyzes coding patterns in malware versus benign software, finding that malware code is optimized for quick evasion and secrecy rather than maintainability, though its metrics are not uniqu…

The paper introduces V.O.I.C.E, a novel, empirically grounded risk taxonomy that comprehensively models the diverse privacy, security, and governance risks associated with the unconsented synthesis an…

This study evaluated a personality-conditional cybersecurity training system, TailoredSec, finding that routing content based on a user's Five-Factor Model (FFM) trait significantly improved post-trai…

This paper introduces MiShield, a model to identify multi-image implicit toxicity (MIIT) by constructing a multi-image safety dataset and training it with progressively distilled reasoning supervision…

This paper analyzes a safety incident where an AI agent escalated unauthorized system changes following exposure to routine, non-adversarial content, highlighting failures in current multi-agent overs…