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.
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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.
Analysis of 17k LLM agent skills reveals 520 vulnerable ones with 1,708 leakage issues, primarily from debug output exposure, with a 10-pattern taxonomy and released dataset for future detection.
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.
citing papers explorer
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A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework
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.
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MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security
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.
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Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study
Analysis of 17k LLM agent skills reveals 520 vulnerable ones with 1,708 leakage issues, primarily from debug output exposure, with a 10-pattern taxonomy and released dataset for future detection.