With the rapid advancement of Large Language Models (LLMs), AI-generated text has become increasingly difficult to distinguish from human-authored content, raising serious concerns about online misinformation and academic integrity. Although plenty of methods have been proposed, they often suffer from high false positives on formal or highly structured human-written texts. Moreover, while external contextual information can provide valuable signals for verification, it remains largely underutilized in existing detection frameworks. In this paper, we propose an agentic framework driven by LLM for AI-generated text detection that integrates multiple machine learning (ML) detectors with an online search module to retrieve and leverage external evidence. Evaluated on a diverse real-world dataset spanning four distinct sources, our agent significantly outperforms standalone detectors, achieving an overall accuracy of 93.90%. We show that online search acts as an effective error-correction mechanism under detector conflict and incorrect agreement, improving accuracy by 10.81% and 90.91%, respectively. We further conduct a failure analysis, identifying key limitations and providing directions for future research.

Inference attacks have been widely studied and offer a systematic risk assessment of ML services; however, their implementation and the attack parameters for optimal estimation are challenging for non-experts. The emergence of advanced large language models presents a promising yet largely unexplored opportunity to develop autonomous agents as inference attack experts, helping address this challenge. In this paper, we propose InferPilot, an autonomous agent capable of independently conducting inference attacks without human intervention. We evaluate it on 20 target services. The evaluation shows that our agent, using GPT-4o, achieves a 100.0% task completion rate and near-expert attack performance, with an average token cost of only 0.627 per run. The agent can also be powered by many other representative LLMs and can adaptively optimize its strategy under service constraints. We further perform trace analysis, demonstrating that design choices, such as a multi-agent framework and task-specific action spaces, effectively mitigate errors such as bad plans, inability to follow instructions, task context loss, and hallucinations. We anticipate that such agents could empower non-expert ML service providers, auditors, or regulators to systematically assess the risks of ML services without requiring deep domain expertise.

Prompt injection attacks are recognized as one of the primary risks faced by LLM-integrated applications in recent years. However, common evaluation frameworks remain insufficient, lacking comprehensiveness and real-world relevance. To bridge this gap, we revisit the common evaluation framework and conduct an extensive evaluation across eight different evaluation settings, including 37 real-world applications, 185 injected tasks, 21 attack instructions, and a total of 143,745 queries. The evaluation highlights several findings. For example, real-world applications are more vulnerable to prompt injection attacks compared to those used in research settings. While complex attack instructions are more sophisticated, they are less effective than simple attack instructions. We further conduct an assessment of both prompt-level and model-level defense mechanisms and highlight their limitations in real-world applications. By exploring more diverse scenarios across different dimensions, our framework provides a solid foundation for assessing vulnerabilities in LLM-integrated applications and evaluating the efficacy of defensive strategies.