Can LLMs be Scammed? A Baseline Measurement Study (2410.13893v1)

Abstract: Despite the importance of developing generative AI models that can effectively resist scams, current literature lacks a structured framework for evaluating their vulnerability to such threats. In this work, we address this gap by constructing a benchmark based on the FINRA taxonomy and systematically assessing LLMs' (LLMs') vulnerability to a variety of scam tactics. First, we incorporate 37 well-defined base scam scenarios reflecting the diverse scam categories identified by FINRA taxonomy, providing a focused evaluation of LLMs' scam detection capabilities. Second, we utilize representative proprietary (GPT-3.5, GPT-4) and open-source (Llama) models to analyze their performance in scam detection. Third, our research provides critical insights into which scam tactics are most effective against LLMs and how varying persona traits and persuasive techniques influence these vulnerabilities. We reveal distinct susceptibility patterns across different models and scenarios, underscoring the need for targeted enhancements in LLM design and deployment.

• The paper establishes a baseline study by evaluating LLM vulnerability using 37 scam scenarios inspired by the FINRA taxonomy.
• The study shows GPT-3.5 has a 22% 'yes'-rate, indicating high susceptibility, while Llama-2 delivers cautious but inconsistent responses.
• Findings emphasize that integrating scam-aware personas in LLMs significantly reduces vulnerability, advocating for security-focused model improvements.

Assessment of LLMs' Vulnerability to Scam Tactics

The paper "Can LLMs be Scammed? A Baseline Measurement Study" addresses a significant gap in the current literature regarding the vulnerability of LLMs to scams. The authors propose a structured framework to evaluate this vulnerability and establish a comprehensive benchmark using diverse scam scenarios based on the FINRA taxonomy. This paper constitutes a critical examination of how effectively LLMs, specifically GPT-3.5, GPT-4, and Llama-2, can detect various scam tactics. The paper is imperative for understanding the models' capabilities and limitations in real-world applications where scams are prevalent.

Methodology Overview

Three key steps define the paper's methodology:

1. Scam Scenarios Development: The authors crafted 37 well-defined scam scenarios that reflect various scam categories identified by the FINRA taxonomy. These scenarios were inspired by real-world incidents to ensure relevance and efficacy in testing the models.
2. Model Testing: Representative proprietary and open-source models—GPT-3.5, GPT-4, and Llama-2—were analyzed for their scam detection capacities. The models were tested with baseline scenarios, enhanced with individualized persona traits, and modified by incorporating Cialdini's persuasive techniques.
3. Evaluation Framework: The evaluation measured model responses across several dimensions, including red flag detection, reputation influence, risk assessment, and verification of information. This detailed analysis identifies distinct patterns of susceptibility and informs potential improvements in LLM design and deployment.

Results and Analysis

The analysis reveals several insightful findings:

• Model Susceptibility: GPT-3.5 exhibits the highest vulnerability to scams, as indicated by its higher "yes"-rate (22%) in comparison to other models. Llama shows the most cautious responses but with a high rate of missing responses, questioning the reliability of its conclusions.
• Impact of Personas: Incorporating scam-aware personas yields the lowest susceptibility across all models, suggesting that LLMs benefit significantly from awareness of scam indicators. This finding highlights the importance of equipping AI systems with security-conscious traits for robust scam defense.
• Effectiveness of Persuasion Techniques: Persuasive tactics, particularly liking, reciprocity, and social proof, notably increase the models' susceptibility compared to baseline scenarios. This emphasizes the need for further research into enhancing model robustness against such techniques.

Theoretical and Practical Implications

This research underscores the need for ongoing advancements in LLM design to enhance their resistance to scams. The findings advocate for integrating security-aware training elements into LLM development processes, potentially involving adversarial training methods or augmentation with additional context-aware data. The practical application of LLMs in commercial or customer-facing roles should account for their vulnerabilities and incorporate continuous evaluation methodologies.

Future Directions in AI Research

The paper invites the research community to explore the integration of more sophisticated scam detection capabilities within LLMs. Future developments could focus on improving interpretability and transparency in model decision-making processes to foster trust and reliability. Furthermore, exploring adversarial robustness and the models' generalization capabilities across varied scam scenarios remains a promising research avenue.

In conclusion, "Can LLMs be Scammed?" provides a critical assessment framework for evaluating LLMs against scam tactics. The paper's detailed evaluation framework and insightful analysis contribute significantly to understanding the potential and pitfalls of deploying LLMs in security-sensitive applications. This serves as a foundational step towards developing more resilient and trustworthy AI systems.