AI creates security risks in two directions: attackers can use AI to help carry out fraud, manipulation, or cyberattacks, and they can target an AI system to make it reveal information or take actions its operators did not intend. Neither outcome is automatic. The risk depends on the system’s design, what it can access, how it is used, and the controls around it.
What does “AI in the wrong hands” mean?
It can mean a malicious actor using AI as a tool, or an attacker exploiting an AI system itself. These are related but distinct problems. In the first, AI may help create or scale harmful activity. In the second, weaknesses in a model, its data, or its connections may expose information or disrupt a service.
NIST’s 2025 Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations describes several attack classes. Its 2024 Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile also discusses misuse, prompt injection, and risks from systems that connect models to other tools and data.
| Risk | What an attacker does | Why it matters |
|---|---|---|
| Evasion | Changes an input at the time a model is used to influence its response. | A deployed model may misclassify something or behave incorrectly. |
| Poisoning | Corrupts training data or other data used by a system. | It can influence model behavior, and tracing the source of tainted data may be difficult in complex supply chains. |
| Privacy attack | Tries to infer or extract sensitive information about a model or its data. | Information expected to remain confidential may be exposed. |
| Misuse or abuse | Repurposes an AI capability for harmful activity, or uses a system connected to compromised sources. | AI-enabled tools can assist fraudulent, harmful, or offensive activity. |
| Prompt injection | Provides malicious instructions directly or hides them in content an AI application retrieves. | An integrated application may be manipulated into unintended actions, such as disclosing data. |
| Synthetic media and impersonation | Uses generated text, images, audio, or video to fabricate content or impersonate someone. | It can support fraud or disinformation and weaken trust in authentic evidence. |
How can attackers use AI to assist cybercrime?
NIST’s Generative AI Profile identifies potential assistance with activities such as hacking, malware creation, and phishing. It also notes reports of large language models finding some vulnerabilities and writing exploit code. These are capabilities that may assist an attacker; they do not mean that AI independently finds and exploits vulnerabilities or that an attempted attack will succeed.
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AI can also help produce convincing fabricated content or impersonations. The security concern is not limited to breaking into a network: false messages and synthetic media may be used to deceive people, facilitate fraud, or make genuine evidence harder to trust. NIST also addresses privacy, intellectual-property, and harmful-content risks associated with generative AI.
How do attacks on AI systems work?
Prompt injection through prompts and retrieved content
A direct prompt injection puts malicious instructions in the input given to an AI system. An indirect injection hides instructions in content the application may retrieve, such as a document, email, or website. If the AI application treats that content as instructions rather than untrusted data, it may produce an unintended response or attempt an action allowed by its connected tools.
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NIST describes research demonstrations in which indirect injections against integrated applications could expose proprietary data or run malicious code remotely. Those are demonstrated scenarios, not a claim that every AI application is vulnerable or that every injection succeeds. The practical exposure depends in part on what the application can access and do.
Attacks can target more than the model
An AI-enabled service includes more than its model: inputs, training and other data, processing, deployment, and connected components can all matter. A vulnerability in an integration or an overly broad permission can turn a model response into a path to sensitive information or an unintended system action. That is why securing the model alone is not a complete security strategy.
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Controls should match the system’s lifecycle stage, the assets it touches, and the security goal at stake. CISA’s joint Guidance on Deploying AI Systems Securely, announced in April 2024, focuses on protecting confidentiality, integrity, and availability, and on protecting, detecting, and responding to malicious activity. The Center for Internet Security’s April 2026 prompt-injection announcement recommends practical safeguards including least privilege, human approval for high-impact actions, inventories, staff training, and AI security assessments.
| Lifecycle stage | Priority | Practical measures |
|---|---|---|
| Development and acquisition | Build security ownership into how the system is designed and sourced. | Use secure-by-design practices; maintain transparency and security responsibility across the AI lifecycle. For externally developed systems, understand the data, services, and components on which the deployment depends. |
| Deployment | Limit exposure and access before connecting the AI system to business workflows. | Inventory the data, systems, and tools the AI can reach. Apply least privilege so the system has only the access required for its purpose. |
| Operation | Control consequential actions and watch for malicious activity. | Require human approval before code execution or high-impact changes. Protect, detect, and respond to threats affecting the AI system, its data, and related services. |
| Across the lifecycle | Prepare people and test security in the context of actual use. | Train staff about risks such as prompt injection and include AI security assessments in penetration-testing plans. |
Why don’t mitigations eliminate the risk?
NIST discusses limitations in current mitigation approaches. A safeguard that helps in one setting may not address a different threat, integration, or business use. Organizations should therefore assess the system in context—what it handles, what it can reach, what actions it can take, and where it sits in its lifecycle—rather than treating any single model defense or checklist as a guarantee.
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