Research
Markets
SecureGARD: Dynamic Ontology-Guided Governance for Secure Tool-Using LLM Agents
Key Insights
- Tool-using large language model (LLM) agents translate model outputs into consequential actions, turning indirect prompt injection into an execution-security problem.
- Existing defenses constrain tool.
Cite this synthesis
DOI: 10.48550/arXiv.2611.06682 ↗
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@misc{ acaciamarkets-research-securegard-dynamic-ontology-guided-governance-for-secure-too,
title = { SecureGARD: Dynamic Ontology-Guided Governance for Secure Tool-Using LLM Agents },
author = { Leszek },
year = { 2026 },
doi = { 10.48550/arXiv.2611.06682 },
url = { http://arxiv.org/abs/2611.06682v1 },
note = {Summarized and classified by AcaciaFund}
}
TY - GEN TI - SecureGARD: Dynamic Ontology-Guided Governance for Secure Tool-Using LLM Agents AU - Leszek PY - 2026 DO - 10.48550/arXiv.2611.06682 UR - http://arxiv.org/abs/2611.06682v1 ER -
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Overview
Tool-using large language model (LLM) agents translate model outputs into consequential actions, turning indirect prompt injection into an execution-security problem. Existing defenses constrain tool
Key Topics
- Market Microstructure: A theme covered by this item within the markets context.
Why It Matters
This item adds to the markets knowledge base. Practitioners can use it to stay current on SecureGARD: Dynamic Ontology-Guided Governance for Secure Tool-Using LLM Agents, but should validate its claims against primary sources and more recent work before relying on it in production decisions.
Key Takeaways
- Understand how SecureGARD: Dynamic Ontology-Guided Governance for Secure Tool-Using LLM Agents relates to markets workflows and controls.
- Assess evidence quality and freshness before acting on the findings.
- Use the tagged topics to connect this item to related content in the library.
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Synthesized
SQI 0.750
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