Benchmarking the effectiveness of multi-agent LLMs in collaborative privacy threat modeling with <span class="small-caps">LINDDUN GO</span>
inforesearchPeer-ReviewedLLM-Specific
researchsecurity
Source: Elsevier Security JournalsApril 26, 2026
Summary
This research paper evaluates whether multiple AI agents working together can effectively help identify privacy threats in software systems using LINDDUN GO, a structured methodology for privacy threat modeling (a process of identifying ways a system could leak or misuse personal data). The study, published in July 2026, examines whether collaborative multi-agent LLM (large language model) systems can improve the quality and completeness of privacy threat identification compared to single AI agents or human analysis.
Classification
Attack SophisticationModerate
Impact (CIA+S)
confidentiality
AI Component TargetedAgent
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Original source: https://www.sciencedirect.com/science/article/pii/S2214212626001195?dgcid=rss_sd_all
First tracked: April 26, 2026 at 02:01 PM
Classified by LLM (prompt v3) · confidence: 85%