Secretary of the Army sees future of cyber warfare, AI integration at ARCYBER - army.mil
Secretary of the Army sees future of cyber warfare, AI integration at ARCYBER army.mil
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Seeking arXiv cs.AI endorsement — neuroscience-inspired memory architecture for AI agents
Hi everyone, I’m an independent researcher (Zensation AI) seeking endorsement for my first arXiv submission in cs.AI. Paper: “ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems” Summary: ZenBrain is the first AI memory system grounded in cognitive neuroscience. It implements 7 memory layers (working, short-term, episodic, semantic, procedural, core, cross-context) with 12 algorithms including Hebbian learning, FSRS spaced repetition, sleep-time consolidation (Stickgold & Walker 2013), and Bayesian confidence propagation. Prior art: Published as defensive publication on TDCommons (dpubs_series/9683) and archived on Zenodo (DOI: 10.5281/zenodo.19353663). Open-source npm packages with 9,000+ tests. Why this matters: Recent surveys (arxiv:2603.07670) identi
Foundation Models for Autonomous Driving System: An Initial Roadmap
arXiv:2504.00911v2 Announce Type: replace Abstract: Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous driving systems (ADSs) in perception, reasoning, decision-making, and interaction. However, ADSs are safety-critical cyber-physical systems, and integrating FMs into them raises substantial software engineering challenges in data curation, system design, deployment, evaluation, and assurance. To clarify this rapidly evolving landscape, we present an initial roadmap, grounded in a structured literature review, for integrating FMs into autonomous driving across three dimensions: FM infrastructure, in-vehicle integration, and practical deployment. For each dimension,
From Component Manipulation to System Compromise: Understanding and Detecting Malicious MCP Servers
arXiv:2604.01905v1 Announce Type: cross Abstract: The model context protocol (MCP) standardizes how LLMs connect to external tools and data sources, enabling faster integration but introducing new attack vectors. Despite the growing adoption of MCP, existing MCP security studies classify attacks by their observable effects, obscuring how attacks behave across different MCP server components and overlooking multi-component attack chains. Meanwhile, existing defenses are less effective when facing multi-component attacks or previously unknown malicious behaviors. This work presents a component-centric perspective for understanding and detecting malicious MCP servers. First, we build the first component-centric PoC dataset of 114 malicious MCP servers where attacks are achieved as manipulatio
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