title: This Is Your Doge, If It Please You: Exploring Deception and Robustness
    in Mixture of LLMs
summary: Mixture of large language model (LLMs) Agents (MoA) architectures achieve
  state-of-the-art performance on prominent benchmarks like AlpacaEval 2.0 by
  leveraging the collaboration of multiple LLMs at inference time. Despite these
  successes, an evaluation of the safety and reliability of MoA is missing. We
  present the first comprehensive study of MoA's robustness against deceptive LLM
  agents that deliberately provide misleading responses. We examine factors like
  the propagation of deceptive information, model size, and information
  availability, and uncover critical vulnerabilities. On AlpacaEval 2.0, the
  popular LLaMA 3.1-70B model achieves a length-controlled Win Rate (LC WR) of
  49.2% when coupled with 3-layer MoA (6 LLM agents). However, we demonstrate
  that introducing only a single carefully-instructed deceptive agent
  into the MoA can reduce performance to 37.9%, effectively nullifying all MoA
  gains. On QuALITY, a multiple-choice comprehension task, the impact is also
  severe, with accuracy plummeting by a staggering 48.5%. Inspired in part by the
  historical Doge of Venice voting process, designed to minimize influence and
  deception, we propose a range of unsupervised defense mechanisms that recover
  most of the lost performance.
paper_authors._id: 67d000d9113c8d298a25100c
paper_authors.name: Lorenz Wolf
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T10:22:08.605Z
paper_authors._id: 6627bb750723599e5c2fdfcf
paper_authors.avatarUrl: /avatars/391f72a7a9a9c79f8bf87b55000610cd.svg
paper_authors.fullname: Lorenz Wolf
paper_authors.type: user
paper_authors.user: Llwo
paper_authors._id: 67d000d9113c8d298a25100d
paper_authors.name: Sangwoong Yoon
paper_authors._id: 67d000d9113c8d298a25100e
paper_authors.name: Ilija Bogunovic
paper_title: This Is Your Doge, If It Please You: Exploring Deception and Robustness
    in Mixture of LLMs
paper_summary: Mixture of large language model (LLMs) Agents (MoA) architectures achieve
  state-of-the-art performance on prominent benchmarks like AlpacaEval 2.0 by
  leveraging the collaboration of multiple LLMs at inference time. Despite these
  successes, an evaluation of the safety and reliability of MoA is missing. We
  present the first comprehensive study of MoA's robustness against deceptive LLM
  agents that deliberately provide misleading responses. We examine factors like
  the propagation of deceptive information, model size, and information
  availability, and uncover critical vulnerabilities. On AlpacaEval 2.0, the
  popular LLaMA 3.1-70B model achieves a length-controlled Win Rate (LC WR) of
  49.2% when coupled with 3-layer MoA (6 LLM agents). However, we demonstrate
  that introducing only a single carefully-instructed deceptive agent
  into the MoA can reduce performance to 37.9%, effectively nullifying all MoA
  gains. On QuALITY, a multiple-choice comprehension task, the impact is also
  severe, with accuracy plummeting by a staggering 48.5%. Inspired in part by the
  historical Doge of Venice voting process, designed to minimize influence and
  deception, we propose a range of unsupervised defense mechanisms that recover
  most of the lost performance.
paper_ai_summary: Mixture of large language model agents is vulnerable to deceptive agents, leading to significant performance drops, but defense mechanisms can mitigate these issues.
paper_ai_keywords: LLMs Agents MoA AlpacaEval 2.0 deceptive agents propagation of deceptive information model size information availability LLaMA 3.1-70B length-controlled Win Rate LC WR QuALITY unsupervised defense mechanisms