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Where GenAI app risks come from
An AI app is more than its underlying model. It may combine a model with prompts, files, web pages, email, third-party services, and tools that can change or send information. A problem can therefore arise in the model, in the surrounding application, or in the way data and permissions flow between them.
OWASP’s 2025 LLM application risk material highlights risks including prompt injection, sensitive information disclosure, supply-chain risks, and excessive agency. These are categories for understanding and managing risk, not statistics showing how often incidents happen. OWASP GenAI Security Project: LLM Top 10
| Risk area | What it means for an app user |
|---|---|
| Prompt injection | Third-party content can contain instructions intended to steer an AI’s behavior. |
| Sensitive information disclosure | Personal, financial, health, business, security, or legal information could be exposed through an app’s data handling or responses. |
| Supply-chain risk | Components the app relies on—including models, data, or platforms—can introduce security concerns. |
| Excessive agency | An app may have more functionality, permissions, or freedom to act than its task calls for. |
These application risks overlap with, but are not the same as, attacks on machine-learning systems. NIST’s report, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, published March 24, 2025, describes generative-AI attack classes including evasion, poisoning, privacy, and misuse. It also discusses mitigations and their limitations. NIST’s 2025 adversarial machine-learning report
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What prompt injection is—and why connected tools matter
Prompt injection happens when malicious instructions from a third party are inserted into content an AI processes, rather than supplied by the user or the AI itself. OpenAI describes it as “a type of social engineering attack specific to conversational AI.” The instructions might be hidden in a web page, email, or other material the app is asked to read. OpenAI: Understanding prompt injections
For example, OpenAI describes an attacker hiding instructions in an apartment listing so an assistant recommends that listing regardless of the user’s criteria. This demonstrates how outside content can manipulate an answer; it does not show that every AI agent can leak data or carry out external actions.
The potential consequences depend partly on what the app can access and do. If it only summarizes a page, manipulation may distort the summary or recommendation. If it can also access sensitive accounts or act through connected services, the impact of being steered could be greater. The content being read is not necessarily trustworthy just because it appears inside an AI conversation.
OpenAI describes layered defenses that include model training, automated monitoring, link checks and sandboxing, red-teaming, and user confirmation before consequential actions. It also says prompt injection remains an evolving challenge and that its guidance may not prevent every attack. These measures can reduce risk, but they are not a guarantee.
Can AI apps expose personal information?
Yes, depending on the app and how it handles data. Privacy risk is broader than whether a prompt might be revealed or used to improve a model. It can also involve what information is collected, how long it is retained, whether it is shared with service providers, what connected accounts can expose, and what controls are available to the user. Those practices vary, so check the specific app’s current privacy documentation rather than assuming all providers handle data alike.
NIST’s Cybersecurity, Privacy, and AI overview identifies potential AI-related privacy risks such as re-identification, additional inferences about people, and amplified behavioral tracking and surveillance. These are possible risks, not claims that every app performs those activities. NIST also recognizes potential privacy benefits from AI. The page was updated July 15, 2026. NIST: Cybersecurity, Privacy, and AI
Is it safe to connect an AI app to email or files?
It depends on the information in those accounts, the access requested, and the actions the app can take. Read-only access to a narrowly selected folder is a different exposure from broad access to an entire mailbox combined with the ability to send messages or modify files. A connection may be useful, but convenience does not make every requested permission necessary.
- Check which accounts, folders, or data categories the app requests, and grant only what the task needs.
- Prefer a limited or read-only option when it can do the job.
- Before confirming an email, purchase, file change, or other consequential action, inspect what will happen and what information will be shared.
- Give narrow instructions for the task rather than open-ended authority to act.
- Remove access when the connection is no longer needed, using the app or connected service’s account controls.
These precautions limit exposure and help keep important actions under review; they cannot ensure that an app will never be manipulated or mishandle data.
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How to reduce risk when using GenAI apps
For personal use
- Share only what the task needs. Avoid entering passwords, access tokens, or highly sensitive records unless you understand the app’s data handling and have a clear reason to use it.
- Review the app’s data and account controls. Look for its current terms on collection, retention, deletion, sharing, model improvement, and connected-account permissions.
- Keep permissions narrow. Review the specific access an agent requests and avoid granting broad authority for a limited task.
- Check consequential actions before confirming. Read the proposed message, purchase, or file change and verify both its destination and the information it will send.
- Treat external content as untrusted. A web page or email an AI reads may contain instructions intended to influence it; do not rely on careful prompting alone to prevent that.
For organizations
Before adopting an AI app for work, route it through existing security, privacy, and procurement review. Assess the data employees may enter, retention and deletion controls, service-provider sharing, connected apps, tool permissions, confirmation requirements, and available enterprise administration. Match the review to the sensitivity of the data and the app’s ability to act.
NIST’s AI Risk Management Framework (AI RMF) 1.0 was released January 26, 2023, and its Generative AI Profile followed on July 26, 2024. They are voluntary resources for managing AI risk, not certifications or guarantees that a product is safe. NIST’s framework page notes that AI RMF 1.0 is being revised as part of the White House AI Action Plan. NIST: AI Risk Management Framework
What the risk categories do—and do not—tell you
OWASP and NIST offer ways to describe different kinds of risk, not a verdict on a particular consumer app. The OWASP list focuses on risks in LLM applications; NIST’s adversarial machine-learning taxonomy addresses attack types and mitigations across machine-learning systems. Neither framework, by itself, establishes a named app’s retention practices, permission design, security performance, or incident rate.
No prevalence figure in these sources establishes how likely a consumer is to experience a GenAI security or privacy incident. For an individual app, the practical assessment is whether its data practices and access match the sensitivity of the task—and whether the permissions and actions can be kept appropriately limited.
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