Proactive Defense: Compound AI for Detecting Persuasion Attacks and Measuring Inoculation Effectiveness

TL;DR

This paper presents BRIES, a comprehensive AI system for detecting persuasion attacks, evaluating inoculation strategies, and analyzing vulnerabilities across language models, advancing AI safety and cognitive security.

Contribution

Introduces BRIES, a novel compound AI architecture with specialized agents for persuasion attack detection, inoculation, and causal analysis, enhancing understanding of AI vulnerabilities and resilience strategies.

Findings

GPT-4 outperforms open-source models in detection accuracy.

Detection performance varies significantly across language models.

Prompt engineering impacts detection efficacy and model-specific performance.

Abstract

This paper introduces BRIES, a novel compound AI architecture designed to detect and measure the effectiveness of persuasion attacks across information environments. We present a system with specialized agents: a Twister that generates adversarial content employing targeted persuasion tactics, a Detector that identifies attack types with configurable parameters, a Defender that creates resilient content through content inoculation, and an Assessor that employs causal inference to evaluate inoculation effectiveness. Experimenting with the SemEval 2023 Task 3 taxonomy across the synthetic persuasion dataset, we demonstrate significant variations in detection performance across language agents. Our comparative analysis reveals significant performance disparities with GPT-4 achieving superior detection accuracy on complex persuasion techniques, while open-source models like Llama3 and Mistral demonstrated notable weaknesses in identifying subtle rhetorical, suggesting that different architectures encode and process persuasive language patterns in fundamentally different ways. We show that prompt engineering dramatically affects detection efficacy, with temperature settings and confidence scoring producing model-specific variations; Gemma and GPT-4 perform optimally at lower temperatures while Llama3 and Mistral show improved capabilities at higher temperatures. Our causal analysis provides novel insights into socio-emotional-cognitive signatures of persuasion attacks, revealing that different attack types target specific cognitive dimensions. This research advances generative AI safety and cognitive security by quantifying LLM-specific vulnerabilities to persuasion attacks and delivers a framework for enhancing human cognitive resilience through structured interventions before exposure to harmful content.