Concern 176 · Working draft · editorial review in progress
AI-generated phishing
AI-generated phishing uses generated messages to trick people into revealing information, opening harmful links, or taking an unauthorized action.
Understanding the concern
Phishing relies on deception about who is speaking and why a request should be trusted. AI can help produce fluent messages, translations, and variations at low cost. Poor spelling is therefore an unreliable way to decide whether a request is genuine.
The deception predates AI; the concern is how generation changes its cost, speed, and reach. Not every polished message is suspicious. A safer judgment considers the requested action, the sender's verified identity, and a separate trusted route for checking unusual requests.
An example
An unexpected invoice looks professionally written and mentions a familiar project, but asks for payment to a changed account.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Phishing
- A deceptive message that tries to get someone to reveal information, send money, or take another harmful action.
Evidence and its limits
The FBI advisory documents uses of generated text in fraud. The invoice is an illustrative scenario, not a reported incident.
FBI Internet Crime Complaint Center · December 2024 · Official public advisory
Criminals Use Generative Artificial Intelligence to Facilitate Financial Fraud ↗ (opens in a new tab)Documents fraud uses of generated text, images, voice and video; does not quantify prevalence of each method.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
How can someone verify an unusual request without relying on the message's wording or links?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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