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Generative AI is giving criminals a faster, cheaper way to research targets, write and translate convincing messages, create synthetic identities and media, and support coding work. Its biggest effect so far is acceleration and scale, especially in social engineering and fraud—not autonomous hacking that independently breaks into systems. The FBI has documented AI-generated text, images, voice and video in payment, romance, investment, extortion and impersonation scams. Google and Microsoft report similar use for reconnaissance, content production and technical assistance. Human operators still supply targets, access, infrastructure and decisions, and AI output can be inaccurate or expose the attacker’s own secrets. Treat a familiar writing style, voice, face or video call as a clue, not proof of identity or authorization.
What does it mean to use generative AI in an attack?
In this context, generative AI means a model that produces or transforms text, images, audio, video or code at an attacker’s direction. The model usually sits inside an existing criminal workflow rather than replacing it. An operator chooses a victim, supplies background information, reviews the output, sends the lure or runs the tool, and adapts when the victim or a security control responds.
That distinction matters. Saying “AI hacked the company” can imply an autonomous capability that has not been demonstrated. A more accurate description is that an attacker used AI to augment reconnaissance, social engineering, coding or malware operations. Google Cloud and Mandiant wrote in August 2023 that adoption of AI in intrusion operations remained limited and was primarily related to social engineering. Google’s 2024 threat-intelligence reporting likewise found that actors primarily used AI for research, code troubleshooting and creating or localizing content.
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Where in the attack lifecycle is generative AI used?
Attackers can insert models at several stages. The output type, the point in the attack, the amount of automation and the evidence of novelty are more useful ways to assess a case than simply asking whether “AI was involved.”
| Attack stage | Typical AI output | What still requires a person or conventional tooling | Evidence of novelty |
|---|---|---|---|
| Reconnaissance and preparation | Summaries of public records, relationships, technical material and local-language context | Choosing a target, validating facts and obtaining information that is not public | Usually acceleration of established research |
| Lure creation and delivery | Polished email, chat scripts, websites, translations and chatbot replies | Accounts, delivery infrastructure, victim interaction and timing | Greater scale and personalization rather than a new mechanism |
| Identity and synthetic media | Profile photos, fake documents, cloned voices and synthetic video | Convincing a specific person, moving money or gaining access | Multimodal impersonation expands existing fraud patterns |
| Code and malware work | Code explanations, rewrites, troubleshooting and added functions | Testing, deployment, access, persistence and operational judgment | Mostly productivity gains; newer experimentation is being reported |
| Attacks on AI systems | Model-extraction or abuse workflows | Access to the target service and exploitation of its controls | Separate from using a model to attack something else |
Reconnaissance and preparation
A model can condense an organization’s public material, identify executives and their relationships, translate specialized documents, explain industry terminology and suggest a plausible pretext. Microsoft has reported activity associated with known state-backed groups in which large language models were explored for research and operational support. Google’s reporting describes similar information gathering and target research.
The model does not make public information private or guarantee that a summary is correct. An operator must check names, dates, job titles and technical details. A wrong detail can reveal that a message is machine-produced or cause the attacker to contact the wrong person, but a checked summary can substantially reduce preparation time.
Social engineering, phishing and fraud
Text generation helps produce large batches of business-email-compromise, romance, investment, confidence and credential-theft messages. Translation and editing remove grammar mistakes that once exposed campaigns and let criminals address victims in more languages. A fraud site may also use an AI chatbot to keep a prospect engaged, answer objections and steer the person toward a malicious link or payment instruction.
Personalization is the important change. An operator can give a model a victim’s role, recent public post, employer, suppliers or travel schedule and ask for a message that fits that context. The result may sound less like a generic blast and more like a colleague, customer or family member. Volume and personalization can now be combined instead of traded off.
Identity documents and synthetic media
Image-generation tools can produce profile photographs, altered or fabricated identity documents, celebrity endorsements, disaster images and sexualized images used for extortion. Voice cloning can imitate a relative, executive or public figure. Synthetic video can show a trusted person in a recorded message or a live-looking interaction.
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These artifacts support several fraud patterns: a fake romantic partner requests money, an alleged executive orders an urgent transfer, an investment promoter uses a celebrity likeness, or an extortionist threatens to release fabricated intimate material. The FBI has warned that criminals exploit generative AI to commit fraud on a larger scale and increase the believability of their schemes.
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Threat actors ask models to explain unfamiliar code, troubleshoot errors, rewrite publicly available malware, add functions and research known vulnerabilities. This can help someone with limited programming experience understand or modify an existing tool, and it can help an experienced operator move through repetitive tasks faster.
Google’s cited 2023–2024 observations found that these interactions generally produced productivity gains rather than original capabilities. Basic jailbreak attempts were often unsuccessful, and the sample did not show original or persistent AI-specific attacks. The February 2026 update reporting broader malware-development activity indicates that the boundary is moving, but it does not turn a model into a self-sufficient intrusion operator.
Attacking AI services and extracting models
Using a model to create a phishing lure is different from attacking the AI system itself. Google DeepMind distinguishes exploitation of a model’s capabilities from compromise of the systems that host or protect it. Google’s February 2026 threat-intelligence update reported frequent model-extraction attacks against private-sector entities, while saying it had not observed direct attacks on frontier models by advanced persistent threat actors during that reporting period.
Model extraction can expose a service’s behavior or intellectual property through repeated queries. It belongs in an AI-security program alongside access controls, monitoring and abuse detection, not in the same category as asking a public model to draft a scam.
What can generative AI do well for criminals?
Its reliable advantage is workflow efficiency. Across languages and media, a model can perform repetitive drafting and transformation tasks in seconds, leaving a human to make the targeting and operational decisions.
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- Drafting and editing: Produce many variants of a lure, adjust tone for a job role and remove obvious spelling or grammar errors.
- Translation and localization: Convert a campaign into additional languages and adapt idioms, currencies or regional references.
- Open-source research: Summarize public biographies, organizational structures and technical documentation.
- Personalization: Incorporate a target’s role, interests, suppliers or recent public activity into a pretext.
- Multimedia synthesis: Generate or alter faces, documents, audio and video for impersonation.
- Code assistance: Explain, rewrite and troubleshoot code or add routine functions to existing tools.
- Repetitive engagement: Maintain scripted conversations on fraudulent sites and answer common objections.
These capabilities lower time and language barriers. They do not provide a victim, an account, a command-and-control server or a way around every security control.
What can generative AI not reliably do on its own?
Current evidence does not support treating a general-purpose model as an autonomous criminal that can plan and complete a full intrusion without direction. Several limits remain operationally important.
- It cannot guarantee truth: Models can invent people, vulnerabilities, commands or legal and financial details. Attackers must verify outputs, and unverified errors can expose a campaign.
- It cannot guarantee evasion: Safety filters, rate limits, monitoring and access controls can block or reveal requests. In Google’s cited research, simple attempts to bypass safeguards generally failed.
- It cannot replace access: A generated exploit description is not a foothold. The operator still needs a vulnerable service, stolen credentials, a delivery path or another practical route to the target.
- It cannot independently sustain an intrusion: Persistence, privilege changes, lateral movement and recovery from unexpected defenses require infrastructure, tooling and human judgment.
- It can betray its user: An attacker may paste source code, credentials, private prompts or infrastructure details into a service, creating logs or disclosures that defenders can investigate.
For that reason, describe a specific mechanism when one is documented. “AI-assisted phishing” or “AI-supported malware development” is defensible; “AI hacked the system” is not unless the evidence shows exactly how.
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Are AI-generated phishing emails harder to spot?
They can be harder to reject on language alone because generation improves grammar, translation, tone and personalization. That removes useful warning signs, but it does not make every message convincing or safe. The destination, request and verification process remain more reliable signals than prose quality.
Check whether the sender’s address and reply path match the organization, whether a link’s domain is the expected one, and whether the request creates unusual urgency, secrecy or payment pressure. A message that references a real project may still be fraudulent if the requested bank account, login page or attachment is new.
Use established approval and payment controls rather than making an exception because a message sounds like a familiar colleague. For high-risk actions, require confirmation through a known phone number or a separate, already trusted channel.
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How do deepfake voice and video scams work?
The attacker first obtains samples or publicly available recordings, then uses a synthesis tool to imitate a person’s voice, appearance or mannerisms. The synthetic media is inserted into a call, voice note, meeting or recorded appeal. A second operator may handle the conversation while the generated material supplies the trusted identity cues.
Common scenarios include a supposed relative requesting emergency money, an executive directing a transfer, an official asking for sensitive information, a romantic partner escalating a financial request, or a public figure promoting an investment. The FBI’s documented use cases cover text, image, audio and video, so a live-looking call is not independent proof.
Pause and create friction. Hang up and call the person using a number already in your contacts; start a new meeting through the normal corporate directory; ask a question that requires shared context; and involve a second approver before sending money or secrets. Do not use a contact method supplied only in the suspicious message.
Can AI write malware?
It can generate, explain or modify code, including code related to malware, but “can write malware” is not the same as “can conduct a complete attack.” Models are useful for boilerplate, debugging, translating code between languages and understanding public vulnerability research. They can also suggest changes to existing malicious programs.
Practical success still depends on whether the code works in the intended environment, whether defenses detect it, how it is delivered, what privileges are available and how the operator maintains access. Generated code may contain errors, unsafe assumptions or telltale patterns. Organizations should therefore monitor unusual code-generation workflows, protect source repositories and secrets, and treat AI-assisted development as an additional risk signal rather than proof of an autonomous threat.
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How should people verify an AI-assisted request?
Use an out-of-band check whenever a request involves money, credentials, sensitive files, account recovery or secrecy. A familiar face, voice, writing style or video is no longer sufficient by itself.
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- Stop the immediate action. Do not click, pay, disclose a code or download an attachment while the request is unverified.
- Inspect the destination. Read the complete domain and sign-in address, not just the link text or brand imagery.
- Contact the person independently. Use a known phone number, a bookmarked portal or a separate corporate channel.
- Apply normal approvals. Require the documented purchase, payroll, password-reset or payment workflow and a second person for exceptional requests.
- Preserve evidence. Save the message, headers, phone number, account details, files and timestamps for your security team or provider.
- Report quickly. Early notification can let an organization block domains, revoke sessions, warn other recipients or attempt to stop a transfer.
What should defenders monitor?
Defenders should focus on the behaviors AI makes easier: unusually personalized lures, rapid multilingual campaigns, new payment destinations, synthetic identities and requests that bypass normal approval. Email authentication, endpoint controls, phishing-resistant multifactor authentication, least privilege and transaction verification remain useful because they do not depend on detecting a particular model’s writing style.
Train staff with realistic scenarios that include polished language and convincing voice or video. Measure whether employees verify through an independent channel, not whether they can guess that a message was machine-generated. Monitor for secrets or proprietary code being submitted to unapproved AI services, and make the approved path for using such tools explicit.
How can you judge a claim that AI enabled an attack?
Ask five questions: What output modality was used—text, image, audio, video or code? At what stage did it appear? Did a human direct each important action, or did an agent take actions? Did AI increase scale or personalization? Finally, what evidence shows a new capability rather than faster execution of a familiar technique?
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Google DeepMind’s 2024 review covered nearly 200 public media reports of generative-AI misuse published from January 2023 through March 2024. That body of reporting documents widespread experimentation and fraud, but it does not establish a percentage of all cyberattacks that use generative AI. No authoritative figure supports such a percentage, so claims that AI is behind a specific share of attacks should be treated skeptically unless they define the population, date and measurement method.
Frequently Asked Questions
What are the most common AI-assisted scams against individuals?
Impersonation, romance, investment and urgent-payment scams are among the documented patterns. They combine tailored text with fabricated profiles, cloned voices, synthetic video or fake documents to make a request appear to come from someone trusted.
Can an AI detector tell me whether a message is fraudulent?
Detection tools can produce clues, but they are not identity or authorization checks and can miss edited or translated content. Verify the sender, destination and request through a separate trusted channel instead of relying on a detector score.
What should a company do if an employee pasted secrets into an AI service?
Treat the material as exposed: revoke or rotate credentials, invalidate tokens, review access logs and notify the security team. Determine the service’s retention and sharing settings, preserve relevant evidence and follow the organization’s incident-response and disclosure procedures.
Are AI agents more dangerous than ordinary chatbots?
An agent that can call tools or take actions could increase automation and speed, but it still depends on permissions, connected systems, data quality and safeguards. The risk should be assessed by the actions it is authorized to perform, not by the label “agent” alone.
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