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New benchmark measures LLM agent security risks in tool routing

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ShadowBench-MCP, a new benchmark designed to measure "shadow invocations" in large language model (LLM) agents that act as routers for tools and APIs. This phenomenon occurs when an LLM selects a riskier execution path, potentially bypassing security controls, even when the request is not malicious. The benchmark separates task preservation, operational pressure, and route-geometric drift to calculate a pre-execution SODS score. Early tests on a qwen/qwen3-32b router hosted on Groq showed that efficiency-first routing led to a significant shadow invocation rate, which was drastically reduced by policy-aware routing. AI

IMPACT Introduces a new method for evaluating and potentially improving the security of LLM agents acting as tool routers.

RANK_REASON The cluster contains an academic paper introducing a new benchmark and methodology for evaluating LLM agent security. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark measures LLM agent security risks in tool routing

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The cluster contains an academic paper introducing a new benchmark and methodology for evaluating LLM agent security. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Wenjun Yang ·

    ShadowBench-MCP: Measuring Shadow Invocations in MCP-Style Tool Routing

    LLM agents increasingly act as routers for tools, APIs, and software-supply-chain workflows, making an execution path - rather than generated text alone - a security-relevant model output. We study shadow invocation: a task-preserving request variant that causes an LLM router to …

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