Source-linked AI summary
Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing
Marc Schmitt, Ivan Flechais
TL;DR
Generative AI creates new risks for social engineering and phishing by producing increasingly convincing interactions and enabling targeted, automated campaigns. This paper uses a systematic review and analysis of AI capabilities to develop the Generative AI Social Engineering Framework, organized around three pillars. It concludes that these capabilities can amplify attacks at industrial scale, while challenging human ability to distinguish genuine from fabricated content and motivating refined countermeasures.
Problem
The paper addresses limited understanding of how Generative AI amplifies social-engineering and phishing threats and the resulting risks to trustworthy digital interactions.
Method
The study conducts a systematic review and analyzes AI capabilities to develop a conceptual framework with three pillars for examining AI-driven social-engineering attacks.
Results
The framework identifies realistic content creation, advanced targeting and personalization, and automated attack infrastructure as pillars that amplify social-engineering attacks at industrial scale.
Takeaways & Limitations
The findings support developing and refining countermeasures while recognizing that human users may increasingly struggle to distinguish genuine from fabricated content.
Takeaways & Limitations
The paper states that awareness alone may become insufficient because people may be unable to differentiate genuine from fabricated content and attacks may require near-perfect awareness.
Abstract
from arXiv · showhide
The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks - the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.
1 INTRODUCTION
Generative AI expands social-engineering and phishing risks by enabling convincing, personalized, evasive, and potentially industrial-scale attacks. The paper examines these capabilities, their implications, and countermeasures.
- Generative AI can produce persuasive content, personalize phishing attacks, automate attack activity, and evade traditional security measures.
- AI-enabled autonomous agents could automate parts or potentially the entire social-engineering attack lifecycle, learning from attacks to improve success rates.
- The paper asks how Generative AI amplifies social-engineering and phishing effectiveness across computer-to-computer, human-to-computer, and human-to-human interactions.
- The study analyzes AI-facilitated attack options and seeks to inform threat intelligence and future mitigation strategies for researchers, practitioners, and policymakers.
- The framework discussion centers on realistic content generation, advanced targeting and personalization, and automated attack infrastructure.
2 METHODOLOGY
The study combines a systematic literature review with qualitative and framework-based analysis to examine how Generative AI fits into social-engineering tactics. It develops and applies a three-pillar conceptual framework to analyze attack impacts and countermeasures.
- The methodology combines qualitative and framework-based analyses to study Generative AI's integration into social-engineering attacks.
- A systematic review establishes the state of the art in Social Engineering and Generative AI for subsequent analyses.
- The study analyzes AI capabilities using Mouton et al.'s Social Engineering Framework to connect Generative AI with existing tactics and strategies.
- The authors identify three pillars: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure.
- The Generative AI Social Engineering Framework provides a blueprint for broader investigations, implications, and solutions.
- The framework is applied to phishing types to investigate threat amplification, cost-effectiveness, and countermeasures for each pillar.
3 LITERATURE BACKGROUND
Social engineering and phishing rely on deception, trust, and human psychology, while AI capabilities increasingly support realistic content generation, target analysis, data scraping, and automated interaction. The background establishes the attack lifecycle and surveys how AI may amplify these techniques.
- Social Engineering and Phishing: Social engineering manipulates people into performing actions or divulging confidential information, often by posing as a trustworthy entity.
- Social Engineering and Phishing: Phishing impersonates legitimate entities through deceptive emails or texts, seeking confidential information, compromised actions, malware infections, account control, or financial transactions.
- Social Engineering and Phishing: Phishing encompasses multiple forms, including email phishing, spear phishing, smishing, whaling, pharming, vishing, and social-media phishing.
- Social Engineering and Phishing: The reproduced social engineering framework describes attack phases from formulation and information gathering through preparation, relationship development, exploitation, and debriefing.
- Artificial Intelligence Capabilities: Generative AI includes systems such as GANs and LLMs that produce realistic digital assets and text, further blurring the distinction between human- and AI-generated content.
- Artificial Intelligence Capabilities: AI and ML provide capabilities relevant to attacks, including realistic content generation, analysis of targets and vulnerabilities, automated scraping, and automated communication.
4 CAPABILITY ANALYSIS OF GENERATIVE AI IN SOCIAL ENGINEERING
The section analyzes how Generative AI can enhance social-engineering attacks across realistic content creation, advanced targeting and personalization, and automated attack infrastructure. These capabilities support deceptive media and communications, adaptive interactions, and campaigns operating at unprecedented scale and speed.
- Automated Attack Infrastructure: AI can automate information gathering, assessment, aggregation, attack-vector development, communication, elicitation, and relationship maintenance across the SE lifecycle.The analysis focuses on lifecycle stages where Generative AI has the greatest potential.
- Realistic Content Generation: Generative AI can create realistic phishing content across text, images, voice, and video, including cloned websites and deepfakes.These outputs can imitate trusted people or legitimate communication and make deceptive content more credible.
- Realistic Content Generation: LLMs can generate contextually relevant, personalized phishing messages and dynamically adapt conversations with victims in real time.The same capabilities extend across emails, SMS, and social-media chats.
- Advanced Targeting and Personalization: AI-supported reconnaissance analyzes targets’ online presence, behavior, and affiliations to develop tailored pretexts and attack strategies.Information gathering and analysis identify patterns and vulnerabilities for subsequent attack stages.
- Automated Attack Infrastructure: Automation enables deceptive campaigns at a scale and speed impossible for human actors, while adaptive models can learn from failures and improve evasion and deception.The paper identifies the ultimate threat as a completely autonomous intelligent social-engineering bot, increasing the need for robust countermeasures.
5 GENERATIVE AI SOCIAL ENGINEERING FRAMEWORK
The GenAI-SE Framework is a flexible model for analyzing how AI capabilities affect social-engineering threats and for informing research and proactive defenses. The section emphasizes that AI-enabled phishing increases attacker advantage through accessibility, personalization, automation, and realistic deception.
- Framework: The GenAI-SE Framework is a flexible, multidimensional model for investigating AI-driven social-engineering attacks.It is intended to accommodate diverse analytical layers or dimensions.
- Framework applications: The framework evaluates a priori how AI capabilities affect existing and emerging threats and informs decision-makers, security professionals, and automated security systems.Its intended use is to identify risks and vulnerabilities before proactive measures are taken.
- Threat amplification and cost-effectiveness: The section applies the framework to assess AI-enabled phishing in terms of threat amplification and cost-effectiveness.Figure 4 is described as analyzing increased phishing threats alongside reduced costs.
- Human implications: Traditional user training is insufficient against sophisticated, targeted phishing, while anomaly and spam detection systems fall short against spear-phishing and state-sponsored attacks.The section also notes that even cybersecurity experts can be susceptible to sophisticated AI-generated phishing.
- Threat amplification and cost-effectiveness: AI accessibility, SaaS infrastructure, personalization, and declining deployment costs lower barriers for attackers and can increase the scale and frequency of phishing.The paper describes AI-powered tools as readily available and increasingly affordable, including to adversaries with limited resources.
- Countermeasures: The paper proposes countermeasures mapped to the framework’s three pillars and emphasizes that AI can support both malicious deception and defensive tools.Potential defensive uses include training materials and systems that detect or counter AI-generated deceptive content.
6 DIRECTIONS FOR FUTURE RESEARCH
Future research should address the combined effects of realistic content, personalization, and automation in AI-driven social engineering. Proposed directions span user education, adversarial machine learning, active deception defense, explainable detection, and emerging technologies.
- Research priorities: The combination of realistic content, personalization, and automation could enable powerful autonomous social-engineering bots and unforeseeable innovations in hacking.The paper identifies this combination as a problematic development requiring new countermeasures.
- User Awareness and Education: User-awareness research should develop effective training programs, interactive simulations, and user-friendly educational materials, while recognizing that awareness training is not an easy fix.The proposed goal is to improve users’ ability to recognize and respond appropriately to threats.
- Adversarial Machine Learning: Adversarial machine learning should develop robust models and strategies that detect and mitigate manipulation in AI-powered social-engineering and phishing attacks.The proposed research focuses on models that can withstand attackers’ manipulation attempts.
- Active Deception Defense: Active deception defense should disrupt attacks in real time using natural-language processing, anomaly detection, and communication-channel analysis.These mechanisms are proposed to identify and block social-engineering and phishing attempts as they occur.
- Explainable AI for Threat Detection: Explainable AI could help analysts and users understand threat-detection decisions, making detection outputs easier to trust and validate.The proposed focus is transparency in AI models used for threat detection.
- Emerging Technologies: Research should examine how chatbots, brain-computer interfaces, robotics, quantum computing, and metaverse ecosystems create new avenues for social-engineering attacks.The paper links these emerging technologies to their increasing integration into daily life.
7 CONCLUSION
Generative AI is presented as amplifying social-engineering effectiveness through realistic content, tailored personalization, and automation, enabling industrial-scale attack patterns. This threatens trust in human-computer interactions and underscores the need for proactive countermeasures.
- Persistent high-volume social-engineering attacks threaten the trust users place in human-computer interactions.
- Continual social-engineering attacks may make users skeptical of system authenticity and hesitant to share information or perform tasks.
- Generative AI can profoundly amplify the effectiveness of social-engineering cyberattacks through realistic content, tailored personalization, and automation.The paper identifies these capabilities as the primary drivers of the enhancement.
- The combination of realistic content, personalization, and automation enables industrial-scale attack patterns.
- As AI becomes more powerful and economically accessible, barriers to misuse diminish, increasing risks for cybersecurity, industries, and individuals.
- The paper calls for developing and refining countermeasures, framing proactive defense as necessary against emerging AI-powered cyber threats.