As large language models (LLMs) become increasingly capable, security and safety evaluation are crucial. While current red teaming approaches have made strides in assessing LLM vulnerabilities, they often rely heavily on human input and lack comprehensive coverage of emerging attack vectors. This paper introduces AutoRedTeamer, a novel framework for fully automated, end-to-end red teaming against LLMs. AutoRedTeamer combines a multi-agent architecture with a memory-guided attack selection mechanism to enable continuous discovery and integration of new attack vectors. The dual-agent framework consists of a red teaming agent that can operate from high-level risk categories alone to generate and execute test cases and a strategy proposer agent that autonomously discovers and implements new attacks by analyzing recent research. This modular design allows AutoRedTeamer to adapt to emerging threats while maintaining strong performance on existing attack vectors. We demonstrate AutoRedTeamer's effectiveness across diverse evaluation settings, achieving 20% higher attack success rates on HarmBench against Llama-3.1-70B while reducing computational costs by 46% compared to existing approaches. AutoRedTeamer also matches the diversity of human-curated benchmarks in generating test cases, providing a comprehensive, scalable, and continuously evolving framework for evaluating the security of AI systems.

Environmental journalism is vital for raising awareness of ecological crises and driving evidence-based policy, yet traditional methods falter under delays, inaccuracies, and scalability limits, especially in under-monitored regions critical to the United Nations Sustainable Development Goals. To bridge these gaps, this paper introduces the AI-Environmental Journalism Integration Model (AEJIM), an innovative framework combining real-time hazard detection, crowdsourced validation, and AI-driven reporting. Validated through a pilot study, AEJIM significantly improved the speed and accuracy of environmental hazard reporting, outperforming traditional methods. Furthermore, the model directly addresses key ethical, regulatory, and scalability challenges, ensuring AI accountability through Explainable AI (XAI), GDPR-compliant data governance, and active public participation. AEJIM provides a transparent and adaptable solution, setting a new benchmark for AI-enhanced environmental journalism and supporting informed global decision-making across diverse socio-political landscapes.

In light of rising drug-related concerns and the increasing role of social media, sales and discussions of illicit drugs have become commonplace online. Social media platforms hosting user-generated content must therefore perform content moderation, which is a difficult task due to the vast amount of jargon used in drug discussions. Previous works on drug jargon detection were limited to extracting a list of terms, but these approaches have fundamental problems in practical application. First, they are trivially evaded using word substitutions. Second, they cannot distinguish whether euphemistic terms such as "pot" or "crack" are being used as drugs or in their benign meanings. We argue that drug content moderation should be done using contexts rather than relying on a banlist. However, manually annotated datasets for training such a task are not only expensive but also prone to becoming obsolete. We present JEDIS, a framework for detecting illicit drug jargon terms by analyzing their contexts. JEDIS utilizes a novel approach that combines distant supervision and delexicalization, which allows JEDIS to be trained without human-labeled data while being robust to new terms and euphemisms. Experiments on two manually annotated datasets show JEDIS significantly outperforms state-of-the-art word-based baselines in terms of F1-score and detection coverage in drug jargon detection. We also conduct qualitative analysis that demonstrates JEDIS is robust against pitfalls faced by existing approaches.

The success of OpenAI's ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of businesses in the Financial Industry. While these applications offer strong potential for efficiencies, they introduce new model risks, primarily hallucinations and toxicity. As highly regulated entities, financial enterprises (primarily large US banks) are obligated to enhance their model risk framework with additional testing and controls to ensure safe deployment of such applications. This paper outlines the key aspects for model risk management of generative AI model with a special emphasis on additional practices required in model validation.

Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Transformer (ChatGPT) standing out as a notable exampledue to its advanced capabilities and widespread applications. This survey provides a comprehensive analysis of ChatGPT, exploring its architecture, training processes, and functionalities. We examine its integration into various domains across industries such as customer service, education, healthcare, and entertainment. A comparative analysis with other LLMs highlights ChatGPT's unique features and performance metrics. Regarding benchmarks, the paper examines ChatGPT's comparative performance against other LLMs and discusses potential risks such as misinformation, bias, and data privacy concerns. Additionally, we offer a number of figures and tables that outline the backdrop of the discussion, the main ideas of the article, the numerous LLM models, a thorough list of datasets used for pre-training, fine-tuning, and evaluation, as well as particular LLM applications with pertinent references. Finally, we identify future research directions and technological advancements, underscoring the evolving landscape of LLMs and their profound impact on artificial intelligence Artificial Intelligence (AI) and society.

Communication overhead in federated learning (FL) poses a significant challenge for network anomaly detection systems, where diverse client configurations and network conditions impact efficiency and detection accuracy. Existing approaches attempt optimization individually but struggle to balance reduced overhead with performance. This paper presents an adaptive FL framework combining batch size optimization, client selection, and asynchronous updates for efficient anomaly detection. Using UNSW-NB15 for general network traffic and ROAD for automotive networks, our framework reduces communication overhead by 97.6% (700.0s to 16.8s) while maintaining comparable accuracy (95.10% vs. 95.12%). The Mann-Whitney U test confirms significant improvements (p < 0.05). Profiling analysis reveals efficiency gains via reduced GPU operations and memory transfers, ensuring robust detection across varying client conditions.

Multi-agent systems often rely on specialized agents with distinct roles rather than general-purpose agents that perform the entire task independently. However, the conditions that govern the optimal degree of specialization remain poorly understood. In this work, we propose that specialist teams outperform generalist ones when environmental constraints limit task parallelizability -- the potential to execute task components concurrently. Drawing inspiration from distributed systems, we introduce a heuristic to predict the relative efficiency of generalist versus specialist teams by estimating the speed-up achieved when two agents perform a task in parallel rather than focus on complementary subtasks. We validate this heuristic through three multi-agent reinforcement learning (MARL) experiments in Overcooked-AI, demonstrating that key factors limiting task parallelizability influence specialization. We also observe that as the state space expands, agents tend to converge on specialist strategies, even when generalist ones are theoretically more efficient, highlighting potential biases in MARL training algorithms. Our findings provide a principled framework for interpreting specialization given the task and environment, and introduce a novel benchmark for evaluating whether MARL finds optimal strategies.

Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts. Unfortunately, predicting such properties is hindered by the difficulty in determining the atomic structure in materials displaying large moir\'e domains. Here, we introduce a split machine-learned interatomic potential and dataset curation approach that separates intralayer and interlayer interactions and significantly improves model accuracy -- with a tenfold increase in energy and force prediction accuracy relative to conventional models. We further demonstrate that traditional MLIP validation metrics -- force and energy errors -- are inadequate for moir\'e structures and develop a more holistic, physically-motivated metric based on the distribution of stacking configurations. This metric effectively compares the entirety of large-scale moir\'e domains between two structures instead of relying on conventional measures evaluated on smaller commensurate cells. Finally, we establish that one-dimensional instead of two-dimensional moir\'e structures can serve as efficient surrogate systems for validating MLIPs, allowing for a practical model validation protocol against explicit DFT calculations. Applying our framework to HfS2/GaS bilayers reveals that accurate structural predictions directly translate into reliable electronic properties. Our model-agnostic approach integrates seamlessly with various intralayer and interlayer interaction models, enabling computationally tractable relaxation of moir\'e materials, from bilayer to complex multilayers, with rigorously validated accuracy.

The Federal Trade Commission has removed all posts from President Joe Biden's term in office from its business blog. This online publication has historically provided advice about how companies could best comply with consumer-protection regulations, covering topics such as artificial intelligence and how big tech companies have collected and used customer data. Currently, it has no content published between December 21, 2020 and March 7, 2025. Wired highlighted some of the notable content from the more than 300 blog posts that have been deleted. Several current and former FTC officials spoke to the publication about the change anonymously out of fear of retaliation.

The Trump administration's Federal Trade Commission has removed four years worth of business guidance blogs as of Tuesday morning, including important consumer protection information related to artificial intelligence and the agency's landmark privacy lawsuits under former chair Lina Khan against companies like Amazon and Microsoft. More than 300 blogs were removed. On the FTC's website, the page hosting all of the agency's business-related blogs and guidance no longer includes any information published during former president Joe Biden's administration, current and former FTC employees, who spoke under anonymity for fear of retaliation, tell WIRED. These blogs contained advice from the FTC on how big tech companies could avoid violating consumer protection laws. One now deleted blog, titled "Hey, Alexa! What are you doing with my data?" explains how, according to two FTC complaints, Amazon and its Ring security camera products allegedly leveraged sensitive consumer data to train the ecommerce giant's algorithms.