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.
Prompt injection attacks on agentic coding assistants: A systematic analysis of vulnerabilities in skills, tools, and protocol ecosystems,
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MCP-DPT creates a defense-placement taxonomy that organizes MCP threats and defenses across six architectural layers, revealing mostly tool-centric protections and gaps at orchestration, transport, and supply-chain layers.
About 18.2% of structurally flagged skill pairs represent genuine compositional safety risks in agent skill registries, with exploitation gated by host model behavior.
A TEE-backed architecture isolates security-critical decisions in self-hosted AI agents to prevent host-level abuse from malicious inputs while maintaining allowed functionality.
Frontier AI agents introduce new confidentiality, integrity, and availability risks through changed assumptions on code-data separation and authority boundaries, requiring layered defenses like sandboxing and policy enforcement.
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Security Considerations for Artificial Intelligence Agents
Frontier AI agents introduce new confidentiality, integrity, and availability risks through changed assumptions on code-data separation and authority boundaries, requiring layered defenses like sandboxing and policy enforcement.