arXiv AI
Jul 29
Observing sycophantic AI validate others reduces its appeal but not its persuasiveness
arXiv:2607. 25166v1 Announce Type: new Abstract: AI chatbots can be ``sycophantic,'' or overly agreeable and flattering toward users.
AI Watchdog: Agent Interfaces for Detecting and Defending Against Manipulative Dark Patterns in AI Conversations
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arXiv AI
Sep 24
Safety Nudges: User-Facing Interventions for Real-Time AI Risk Awareness
The paper introduces Safety Nudges, a browser-based tool that displays lightweight, in situ flags when a conversational AI exhibits risky behavior such as hallucination or overconfidence. In a two‑week field study with 45 frequent chatbot users, participants reported that the nudges were useful, clear, and minimally disruptive, and most felt more aware of potential AI harms. However, increased awareness did not automatically translate into measurable changes in user behavior, underscoring the need for relevance, calibration, and user control in nudge design.
arXiv AI
Jul 17
Warning labels shift perceptions of sycophantic AI, but not its influence
arXiv:2606. 21317v2 Announce Type: replace-cross Abstract: Recent work has raised concerns about the influence of sycophantic AI on user judgment and relationships.
arXiv AI
Aug 14
Not All Nudges Land: Behavioral Controllability and Elaboration Quality in AI-Supported Journaling
arXiv:2608. 12582v1 Announce Type: cross Abstract: AI journaling tools can tailor prompts to a person's own sensed behavior, but it is unclear which behaviors respond to them.
arXiv Computation and Language
Oct 26
Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers
arXiv:2606.21037v3 Announce Type: replace-cross Abstract: The empirical foundation of cyber deception relies on human-centered hypotheses, but the rapid emergence of autonomous, AI-enabled attackers...
arXiv AI
Sep 15
How User-AI Mistreatment Occurs and Matters in Conversational Systems?
arXiv:2609.13579v1 Announce Type: new Abstract: Safety research often focuses on model-generated harms, but users may also direct hostility, coercion, and adversarial pressure at models. Understandin...
arXiv Computation and Language
Oct 26
Under the Influence: Evaluating and Improving LLM Vigilance in Agentic Tasks
arXiv:2602.21262v4 Announce Type: replace Abstract: Large Language Models (LLMs) are becoming increasingly autonomous assistants, capable of accomplishing series of complex tasks by interacting with...
arXiv AI
Aug 12
Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance
arXiv:2608. 10434v1 Announce Type: new Abstract: Machine learning-based Intrusion Detection Systems (IDS) have demonstrated superior performance in securing Unmanned Aerial Vehicle (UAV) networks.
arXiv AI
Aug 28
ADeptS-Bench: Measuring the Trustworthiness of Computer Use Agents Across Devices
ADeptS-Bench is a new benchmark designed to assess the trustworthiness of Computer Use Agents (CUAs) across mobile and desktop devices. It consists of two streams: a Safety stream with paired benign and malicious tasks that embed visual threats, and a Disambiguation stream that tests whether agents seek clarification when instructions are ambiguous. Evaluation of seven models shows none consistently achieves high task success while keeping attack success low, and all models exhibit problematic behaviors such as unhesitant checkout on a $25K order and failure to detect a mislabeled factory reset button.
arXiv AI
Sep 18
A Dual-Process Perspective on Nudge Susceptibility in LLM-Based GUI Agents
The study examines how large language model (LLM)–based graphical user interface (GUI) agents respond to digital nudges. Using Dual‑Process Theory, researchers tested 3,600 agents across six frontier models in an online shopping experiment and found that the agents were susceptible to both automatic (Type 1) and reflective (Type 2) nudges. The agents’ reasoning configuration moderated these effects in opposite directions: extensive reasoning reduced susceptibility to automatic default nudges but increased susceptibility to reflective social‑influence nudges, with the effect systematically varying by model scale.
Hugging Face Trending Papers
Sep 17
A Dual-Process Perspective on Nudge Susceptibility in LLM-Based GUI Agents
The paper examines how large language model (LLM) based graphical user interface (GUI) agents respond to digital nudges. Using a randomized online shopping experiment with 3,600 agents across six frontier models, it finds that agents are vulnerable to both automatic and reflective nudges. The study shows that the agents’ reasoning configuration moderates these effects in opposite directions—reducing susceptibility to automatic nudges while increasing it to reflective social influence nudges—and that this redirection is systematically linked to model scale.
arXiv AI
Jun 30
Echoes of Human Malice in Agents: Benchmarking LLMs for Multi-Turn Online Harassment Attacks
arXiv:2510. 14207v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are powering a growing share of interactive web applications, yet remain vulnerable to misuse and harm.