This Is Your Doge, If It Please You: Exploring Deception and Robustness
  in Mixture of LLMs
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.
67d000d9113c8d298a25100c
Lorenz Wolf
claimed_verified
2025-03-11T10:22:08.605Z
6627bb750723599e5c2fdfcf
/avatars/391f72a7a9a9c79f8bf87b55000610cd.svg
Lorenz Wolf
user
Llwo
67d000d9113c8d298a25100d
Sangwoong Yoon
67d000d9113c8d298a25100e
Ilija Bogunovic
This Is Your Doge, If It Please You: Exploring Deception and Robustness
  in Mixture of LLMs
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.
Mixture of large language model agents is vulnerable to deceptive agents, leading to significant performance drops, but defense mechanisms can mitigate these issues.
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