Infostealer malware infects devices worldwide and harvests their most sensitive contents: credentials, browser sessions, private keys, and access certificates. Yet its impact on victims remains difficult to study without an ethical, legal, and curated research dataset. To close this gap, we build a privacy-preserving pipeline that turns illicitly sourced infostealer logs into a reproducible research artifact, minimizing sensitive data while preserving measurement utility, and use it to construct a dataset of 170,298 victims from logs of multiple infostealer families. Analyzing these victims, we find that the most compromised services mirror the world's most popular platforms, with gaming and entertainment services strongly overrepresented. Within the sample we identify compromised credentials for high-value organizations, including law-enforcement domains, government and military services, and all eight Ivy League universities, as well as substantial exposure of security-critical infrastructure and of financial, remote-access, and development platforms. Victims also show widespread credential reuse and significant revictimization risk, overlapping with phishing and ransomware victim populations. We release the first anonymized victim-level infostealer dataset under controlled access to enable ethical, privacy-preserving, and reproducible research on information security and victim behavior.
Beyond simplistic 3D visualisations, archaeologists, as well as cultural heritage experts and practitioners, need applications with advanced functionalities. Such as the annotation and attachment of metadata onto particular regions of the 3D digital objects. Various approaches have been presented to tackle this challenge, most of which achieve excellent results in the domain of their application. However, they are often confined to that specific domain and particular problem. In this paper, we present ART3mis - a general-purpose, user-friendly, interactive textual annotation tool for 3D objects. Primarily attuned to aid cultural heritage conservators, restorers and curators with no technical skills in 3D imaging and graphics, the tool allows for the easy handling, segmenting and annotating of 3D digital replicas of artefacts. ART3mis applies a user-driven, direct-on-surface approach. It can handle detailed 3D cultural objects in real-time and store textual annotations for multiple complex regions in JSON data format.
Autonomous multi-agent systems nowadays act in finance, software supply chains, and security operations. Already, the first largely AI-orchestrated intrusion campaigns have been reported. Yet, when such a system causes harm, no method can robustly establish what happened, what caused it, or who is accountable. This is because provenance forensics works at the wrong abstraction, formal causality assumes the causal model, and agent auditing trusts self-recording. The target failure mode is, thus, attribution laundering, i.e., spreading an act across redundant agents until none is a but-for cause. Worse, the record is produced by the suspects, which comprises the assumption adopted throughout this work. Agents may therefore anticipate the investigation and the part of logging infrastructure may itself collude. In this paper, HANSARD is proposed, a reference architecture treating accountability as a life-cycle property. First, a readiness profile sealed before operation bounds what later findings may claim. Second, capturing at five choke points beyond the agents' reach makes omissions detectable, not only tampering. Third, a typed PROV-DM-aligned causal graph accrues as the system runs, and three indicators read it live to gate oversight without adjudicating. Fourth, post-incident replay yields contingent effects under the modified Halpern-Pearl definition, together with a compensation-set size. Finally, a synergy residual measures harm due to the combination rather than to individuals, making laundering visible. Cause, responsibility and accountability are then reported separately, each capped by an evidentiary tier, while a future research agenda is also provided.
Transformer-based architectures have significantly advanced the generation of complex symbolic sequences, yet a significant gap remains in achieving fine-grained, interpretable control over discrete signal attributes. This paper investigates the mechanistic interpretability of the Multitrack Music Transformer (MMT) and proposes a framework for deterministic attribute modulation without retraining to bridge this gap via inference-time activation steering. Utilizing the Difference-in-Means (DiffMean) methodology, we isolate latent directions for signal attributes, specifically Pitch and Duration, within the residual stream. We validate the Linear Representation Hypothesis in this domain, achieving high correlation between steering magnitude and attribute shift. To address the inherent feature entanglement in multi-attribute steering, we introduce a Dual Steering framework utilizing Gram-Schmidt Orthogonalization. Experimental results demonstrate that this geometric decoupling reduces conceptual interference and signal degradation compared to naive vector addition, enabling independent deterministic control even against strong autoregressive conditioning.
Future wireless networks (WNs) must address unprecedented challenges in resource allocation (RA) driven by dynamic environments, diverse user demands, and heterogeneous service requirements. Emerging services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) demand intelligent, context-aware resource management strategies beyond traditional methods. Semantic communication (SemCom), which prioritizes conveying intended meaning rather than raw data, offers a promising paradigm to enhance spectral efficiency, reduce communication overhead, and improve user satisfaction. In parallel, advancements in artificial general intelligence (AGI) and large language models (LLMs) introduce new capabilities in reasoning, semantic inference, and adaptive decision-making. This paper presents a unified conceptual and architectural framework that integrates SemCom, LLMs, and AGI for intelligent RA in future WNs. We first examine foundational concepts, then classify and compare methodologies across key performance metrics, and finally explore synergistic architectures that combine these technologies. We highlight open challenges, including semantic metric design, real-time AGI adaptation, scalable LLM deployment, and privacy-preserving semantic reasoning. Unlike prior works, this survey uniquely bridges the semantic and cognitive dimensions of RA, providing a comprehensive roadmap for building fully autonomous, semantic-aware, and resource-efficient wireless communication systems.
