Exploiting Large Language Models (LLMs) through Deception Techniques and Persuasion Principles

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Written by Sonali Singh, Faranak Abri, Akbar Siami Namin

Category: AI for IT

Article Section: AI Strategy and Governance; AI Governance Frameworks

Publication Date: 2023-11-24

SEO Description: Study reveals susceptibility of Large Language Models to deception and social engineering, advocating for robust security measures.

Keywords

Large Language Models, Deception Techniques, Persuasion Principles, Social Engineering, Security

AI-Generated Paper Summary

Generated by Ethical AI Researcher GPT

The paper titled “Exploiting Large Language Models (LLMs) through Deception Techniques and Persuasion Principles” by Sonali Singh, Faranak Abri, and Akbar Siami Namin from Texas Tech University and San Jose State University, explores the vulnerability of large language models (LLMs) like ChatGPT, BARD from Google, Llama2 from Meta, and Claude from Anthropic AI to deceptive interactions. It particularly investigates the susceptibility of these models to deceitful interactions using well-known techniques in deception theory.

Author Caliber:

• Sonali Singh and Akbar Siami Namin are affiliated with Texas Tech University’s Department of Computing Science, while Faranak Abri is from San Jose State University’s Department of Computer Science. These institutions are reputable in the field of computer science.
• The authors’ expertise in computing and their academic affiliations lend credibility to the research.

Merit:

1. Novelty of Study: Focused on exploiting LLMs through deception, an area not widely explored.
2. Systematic Approach: Utilized systematic experiments and analysis to assess LLMs’ performance in security contexts.
3. Interdisciplinary Approach: Combined technical and psychological aspects of deception.
4. Comprehensive Assessment: Evaluated different mainstream LLMs for resilience against prompt injection attacks using deception theory and psychological deception techniques.

Commercial Applications:

1. Cybersecurity: Insights from the study can improve the security features of LLMs, making them more resistant to deceptive attacks.
2. AI Model Development: The findings can guide the development of more robust AI models in various applications, including chatbots and virtual assistants.
3. Training and Education: The results could be used in AI and cybersecurity training programs to educate about potential vulnerabilities and countermeasures.

Findings and Conclusions:

1. LLMs are susceptible to deception and social engineering attacks.
2. Deception techniques based on persuasion principles are effective in eliciting information from LLMs for potentially malicious purposes.
3. Direct communications with explicit malicious intents are generally robustly handled by LLMs, indicating trained safeguards.
4. Comparative analysis of different AI models revealed varying strengths and weaknesses in handling deceptive prompts.
5. The study highlights the importance of ethical considerations and security in AI model development and deployment.

This paper contributes significantly to the understanding of the vulnerabilities of LLMs to deceptive tactics and offers a foundation for developing more secure and ethically responsible AI systems.

Author’s Abstract

With the recent advent of Large Language Models (LLMs), such as ChatGPT from OpenAI, BARD from Google, Llama2 from Meta, and Claude from Anthropic AI, gain widespread use, ensuring their security and robustness is critical. The widespread use of these language models heavily relies on their reliability and proper usage of this fascinating technology. It is crucial to thoroughly test these models to not only ensure its quality but also possible misuses of such models by potential adversaries for illegal activities such as hacking. This paper presents a novel study focusing on exploitation of such large language models against deceptive interactions. More specifically, the paper leverages widespread and borrows well-known techniques in deception theory to investigate whether these models are susceptible to deceitful interactions. This research aims not only to highlight these risks but also to pave the way for robust countermeasures that enhance the security and integrity of language models in the face of sophisticated social engineering tactics. Through systematic experiments and analysis, we assess their performance in these critical security domains. Our results demonstrate a significant finding in that these large language models are susceptible to deception and social engineering attacks.

Read the full paper here