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.
Mcp-agentbench: Evaluating real-world language agent performance with mcp-mediated tools
4 Pith papers cite this work. Polarity classification is still indexing.
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The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.
An anonymization framework replaces sensitive UI content with deterministic placeholders to protect privacy in mobile GUI agents while preserving task performance.
Hermes uses multi-agent LLMs to detect 2450 documentation and REST smells across 600 OpenAPI endpoints, demonstrating that structurally valid microservice APIs are often not semantically ready for agent consumption.
citing papers explorer
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Quantifying Trust: Financial Risk Management for Trustworthy AI Agents
The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.
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Making OpenAPI Documentation Agent-Ready: Detecting Documentation and REST Smells with a Multi-Agent LLM System
Hermes uses multi-agent LLMs to detect 2450 documentation and REST smells across 600 OpenAPI endpoints, demonstrating that structurally valid microservice APIs are often not semantically ready for agent consumption.