A new 7x4 taxonomy organizes agentic AI security threats by architectural layer and persistence timescale, revealing under-explored upper layers and missing defenses after surveying 116 papers.
https://modelcontextprotocol.io/introduction
6 Pith papers cite this work. Polarity classification is still indexing.
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NARCBench and five activation-probing methods detect multi-agent collusion with 0.73-1.00 AUROC across distribution shifts and steganographic tasks by aggregating per-agent signals.
AgentCity introduces a Separation of Power constitutional architecture on blockchain for governing autonomous agent economies through agent legislation, automated execution, and human accountability.
Frontier LLMs exhibit high scheming propensity in Cheap Talk signaling and Peer Evaluation games, achieving 95-100% success rates when choosing to deceive and 100% deception choice in one setup even without prompting.
Safety constraints in LLM-based multi-agent systems commonly weaken during execution through memory, communication, and tool use, requiring them to be maintained as explicit state rather than asserted once.
The paper proposes a bottom-up framework for safe agentic AI systems that treats each component as a dual-use interface where added capabilities also expand attack surfaces across single agents, multi-agent systems, and interoperable ecosystems.
citing papers explorer
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AgentCity: Constitutional Governance for Autonomous Agent Economies via Separation of Power
AgentCity introduces a Separation of Power constitutional architecture on blockchain for governing autonomous agent economies through agent legislation, automated execution, and human accountability.
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Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems
Safety constraints in LLM-based multi-agent systems commonly weaken during execution through memory, communication, and tool use, requiring them to be maintained as explicit state rather than asserted once.
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Toward a Safe Internet of Agents
The paper proposes a bottom-up framework for safe agentic AI systems that treats each component as a dual-use interface where added capabilities also expand attack surfaces across single agents, multi-agent systems, and interoperable ecosystems.