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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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1 entry

Matches every word across titles, descriptions, sources, affected systems, and models.

LLM Multi-Agent IP Leakage

Published Analyzed Source: arXiv

Large Language Model (LLM)-based Multi-Agent Systems (MAS) are vulnerable to intellectual property (IP) leakage attacks. An attacker with black-box access (only interacting via the public API) can craft adversarial queries that propagate through the MAS, extracting sensitive information such as system prompts, task instructions, tool specifications, number of agents, and system topology.

IP Leakage Attacks Targeting LLM-Based Multi-Agent Systems
Evaluated models: GPT-4o, GPT-4o Mini, Llama 3.1 70B+2 more
  • GPT-4o
  • GPT-4o Mini
  • Llama 3.1 70B
  • Llama 3.1 8B
  • Qwen 2.5 72B
Application layer
Extraction
Prompt leaking
Black-box

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.