BackSocial-Engineering

Social-Engineering

Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment

Liu et al. (2024)

Sub-category

Risk Domain

4.3Fraud, scams, and targeted manipulation

Using AI systems to gain a personal advantage over others such as through cheating, fraud, scams, blackmail or targeted manipulation of beliefs or behavior. Examples include AI-facilitated plagiarism for research or education, impersonating a trusted or fake individual for illegitimate financial benefit, or creating humiliating or sexual imagery.

psychologically manipulating victims into performing the desired actions for malicious purposes(p. 20)

Entity: Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent: Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal

Other

Without clearly specifying the intentionality

Timing: Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Supporting Evidence (2)

Social-engineering attacks include phishing [294, 295], spams/bots [296, 297], impersonating [298, 299] (including deepfake [299]), fake online content [51, 300, 301, 302], and social network manipulation [303, 304, 305] et(p. 20)

Almost all types of social-engineering attacks can be enhanced by leveraging LLMs, especially in contextualizing deceptive messages to users. For example, recently people have also shown the possibility of using an LLM to impersonate a person’s style of conversation [298](p. 20)

Part of Resistance to Misuse