Mobile GUI agents are designed to autonomously execute diverse device-control tasks by interpreting and interacting with mobile screens. Despite notable advancements, their resilience in real-world scenarios where screen content may be partially manipulated by untrustworthy third parties remains largely unexplored. Owing to their black-box and autonomous nature, these agents are vulnerable to manipulations that could compromise user devices. In this work, we present the first systematic investigation into the vulnerabilities of mobile GUI agents. We introduce a scalable attack simulation framework AgentHazard, which enables flexible and targeted modifications of screen content within existing applications. Leveraging this framework, we develop a comprehensive benchmark suite comprising both a dynamic task execution environment and a static dataset of vision-language-action tuples, totaling over 3,000 attack scenarios. The dynamic environment encompasses 58 reproducible tasks in an emulator with various types of hazardous UI content, while the static dataset is constructed from 210 screenshots collected from 14 popular commercial apps. Importantly, our content modifications are designed to be feasible for unprivileged third parties. We evaluate 7 widely-used mobile GUI agents and 5 common backbone models using our benchmark. Our findings reveal that all examined agents are significantly influenced by misleading third-party content (with an average misleading rate of 28.8% in human-crafted attack scenarios) and that their vulnerabilities are closely linked to the employed perception modalities and backbone LLMs. Furthermore, we assess training-based mitigation strategies, highlighting both the challenges and opportunities for enhancing the robustness of mobile GUI agents. Our code and data will be released at https://agenthazard.github.io.

Meta is introducing a tool to identify AI-generated images shared on its platforms amid a global rise in synthetic content spreading misinformation. Due to several of systems on the web, the Mark Zuckerberg-owned company is aiming to expand labels to others like Google, OpenAI, Microsoft, and Adobe. Meta said it will fully roll out the labeling feature in the coming months and plans to add a feature that lets users flag AI-generated content. However, the US presidential race is in full swing, leaving some to wonder if the labels will be out in time to stop fake content from spreading. The move comes after Meta's Oversight Board urged the company to take steps to label manipulated audio and video that could mislead users. 'The Board's recommendations go further in that it advised the company to expand the Manipulated Media policy to include audio, clearly state the harms it seeks to reduce, and begin labeling these types of posts more broadly than what was announced,' an Oversight Board spokesperson Dan Chaison told Dailymail.com.

Preventing the spread of misinformation is challenging. The detection of misleading content presents a significant hurdle due to its extreme linguistic and domain variability. Content-based models have managed to identify deceptive language by learning representations from textual data such as social media posts and web articles. However, aggregating representative samples of this heterogeneous phenomenon and implementing effective real-world applications is still elusive. Based on analytical work on the language of misinformation, this paper analyzes the linguistic attributes that characterize this phenomenon and how representative of such features some of the most popular misinformation datasets are. We demonstrate that the appropriate use of pertinent symbolic knowledge in combination with neural language models is helpful in detecting misleading content. Our results achieve state-of-the-art performance in misinformation datasets across the board, showing that our approach offers a valid and robust alternative to multi-task transfer learning without requiring any additional training data. Furthermore, our results show evidence that structured knowledge can provide the extra boost required to address a complex and unpredictable real-world problem like misinformation detection, not only in terms of accuracy but also time efficiency and resource utilization.

Humans have designed a range of tools to aid us in the creation of art, and they've evolved dramatically over time. The creative person's tool kit has recently grown with the addition of a formidable new tool: text-to-image generators powered by artificial intelligence. The possibilities of what this novel technology can be used to create are in many ways endless, but that wide range of potential comes at a cost. While some images -- whether cartoon-like doodles or highly realistic scenes that resemble real photographs -- may be creative or inspiring, others could in some cases be harmful or dangerous. When a user enters a handful of key words, these models generate images that combine those concepts in novel ways.

As the Afghanistan crisis continues to unfold, it's clear that social media algorithms are unable to counter enough misleading and/or fake content. While it's unreasonable to expect that no disingenuous content will slip through the net, the sheer amount that continues to plague social networks shows that platform-holders still have little grip on the issue. When content is removed, it should either be prevented from being reuploaded or at least flagged as potentially misleading when displayed to other users. Too often, another account – whether real or fake – simply reposts the removed content so that it can continue spreading without limitation. The damage is only stopped when the vast amount of content that makes it AI-powered moderation efforts like object detection and scene recognition is flagged by users and eventually reviewed by an actual person, often long after it's been widely viewed.