AI-driven attacks are either conventional cyberattacks that use AI to assist the attacker, or attacks that target AI systems themselves. AI can help tailor phishing, create synthetic text or media, and support research or coding. It can also expose risks when an AI agent follows malicious instructions hidden in content it reads. These changes can make some attacks harder to recognize or contain, but the evidence does not show that every AI-assisted attack is more successful or impossible to detect.
What counts as an AI-driven attack?
The phrase has two distinct meanings. In one, AI is a tool used by an attacker during a conventional operation, such as phishing or code development. In the other, an AI model or agent is the target—for example, an attacker may try to manipulate its behavior or obtain information through it. These are related security problems, but they are not the same kind of attack.
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| Type | What the attacker targets | What AI does |
|---|---|---|
| AI-assisted attack | A person, organization, account, device, or conventional system | Helps with tasks such as researching targets, drafting messages, or adapting code |
| Attack against an AI system | An AI model, its data, or an agent connected to tools and information | May be the system being manipulated, misused, or exposed to harmful input |
NIST’s adversarial machine-learning taxonomy includes evasion, poisoning, privacy, and misuse attacks against AI systems. Those categories concern ways AI can be attacked or misused; they should not be conflated with an attacker using AI to improve a phishing message.
How can AI assist a conventional attack?
AI can support multiple parts of an operation rather than creating a wholly new kind of crime. Its practical effect depends on the attacker, the task, and the systems involved.
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Tailoring social-engineering messages
AI can help produce text in different languages, adjust tone, and incorporate details about a person or organization that are publicly available. NIST describes AI-assisted spear-phishing that can use realistic text, audio, or video, as well as synthetic websites and links. Personal information online can make a fabricated trust story more plausible.
Generating or adapting synthetic media
Generated audio, video, and images can make familiar clues—such as awkward wording or an unfamiliar voice—less dependable on their own. A convincing appearance or voice is not proof that a request is legitimate. This matters especially for requests involving payment, credentials, account access, or secrecy.
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Supporting research and technical work
Threat reports describe AI assistance with research, vulnerability-related work, coding, and other attack-lifecycle tasks. OpenAI’s October 2025 account of a phishing and scripting operation described multilingual email generation and help with tooling. Google has also reported more advanced uses, including AI-assisted vulnerability exploitation, augmented operations, and agentic workflows. These are attributed observations about particular actors and uses, not proof that all attackers have the same capabilities.
Why can AI-driven attacks be harder to stop?
The difficulty comes from how AI can change an operation’s speed, presentation, and access—not from a single universal “AI attack” technique.
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- Impersonation can be more convincing. Synthetic text, audio, video, or images may reduce the reliability of warning signs people have traditionally noticed.
- Messages can be more tailored. Language, tone, and institutional details can be adapted to a target, while public information supplies context for a plausible narrative.
- Attackers can get help across several tasks. Assistance with research, writing, or coding may make parts of a workflow more efficient. That does not by itself establish a higher success rate.
- Connected agents can take actions. If an AI agent can read sensitive material or use tools, hostile instructions embedded in content it processes may have consequences beyond deceiving a person.
- Security controls still have to work together. A persuasive message is only one stage in an attack. Authentication, email defenses, staff awareness, permissions, and incident response affect whether an attempt succeeds and how far it can go.
How can an AI agent be hijacked?
An AI agent may read emails, web pages, documents, or code repositories and also have permission to use tools. Malicious instructions hidden in an external source can attempt to steer the agent’s behavior. NIST describes this as indirect prompt injection or agent hijacking. Depending on the agent’s access, unintended actions could include exposing sensitive data or downloading and running malicious code.
This risk is different from a person being tricked by a phishing email: the agent may act on hostile content as if it were an instruction, and its permissions can determine the impact. The relevant questions are what the agent can read, what tools it can use, and whether actions require review.
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Does AI make cyberattacks universally more effective?
No broad conclusion is established. In its October 2025 account of a threat cluster, OpenAI said the operators mainly sought incremental efficiency in existing phishing and tooling workflows, and that its model did not introduce novel offensive capabilities. Google’s later reporting describes maturing uses, including AI-assisted exploitation and agentic workflows. Those findings concern different actors, periods, and applications; they can coexist without demonstrating a universal leap in attacker capability.
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How should people and organizations respond?
- Verify sensitive requests independently. For unusual requests involving money, credentials, access, or urgent secrecy, contact the person or organization through a channel you already know is genuine—not through contact details in the suspicious message. Verification is a safeguard, not a guarantee that synthetic media can always be identified.
- Keep core defenses in place and adapt them. Maintain authentication, email security, staff training, and incident response. NIST’s initial preliminary draft of its AI Cybersecurity Framework Profile recommends integrated automated defenses alongside training and email and authentication safeguards; it is draft material rather than final guidance.
- Restrict agent access. Give an AI agent only the data and tools it needs. Require appropriate review before consequential actions, and consider the instructions and external content it processes as potential sources of hostile input.
- Apply security fundamentals to AI deployments. NIST’s summary of AI-agent security responses reports broad agreement that established cybersecurity practices remain relevant, while needing adaptation to agent-specific risks.
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