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Generative AI changes cybersecurity in two directions: attackers can use it to assist with activities such as phishing, malware development, or hacking, while AI systems themselves can be attacked through their prompts, data, integrations, tools, and permissions. The practical response is to secure both the surrounding application and the way people use it—and to keep testing as the system changes. The evidence supports treating these as real, evolving risks, not assuming every attacker now uses AI or that AI alone causes breaches.
What are the cybersecurity risks of generative AI?
The risk has two distinct sides. In an AI-assisted attack, a malicious actor uses a generative AI system to help carry out or improve an activity. In an attack on an AI system, the target is the model or the application around it: its instructions, data, connected tools, identities, or access rights. One organization can face both at once—for example, an employee might receive an AI-assisted phishing message while a company chatbot is exposed to malicious instructions in content it processes.
NIST’s 2024 Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile describes the same two-sided picture. Generative AI may lower barriers to offensive cyber capabilities or make some activity easier to automate; meanwhile, generative-AI systems expand the attack surface and can be targeted with techniques such as prompt injection and data poisoning. The profile establishes risks and plausible ways capabilities may be used; it does not establish that every attacker has become more capable, that every attack involves AI, or that AI independently causes a breach.
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NIST identifies hacking, malware, and phishing as activities that generative AI may augment. The relevant security concern is assistance or automation: AI can be used in an attempt to make parts of offensive work easier or more efficient. The Cyber Threat Alliance’s January 2025 report also treats malicious use of generative AI as one side of the threat picture, alongside attacks targeting AI systems.
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That framing is not a measure of how many attacks use AI. A claim that an attack is AI-assisted needs evidence about that attack; the existence of a model that could assist an activity does not prove it was used. For defenders, the sensible approach is to strengthen controls against the underlying activity—such as phishing or malware—without treating AI involvement as a given.
How can an AI system itself be attacked?
AI applications introduce risks that depend on their particular data flows and integrations. Prompt injection and data poisoning are two examples identified by NIST. Agentic systems add another concern: when a model can use tools or act through an identity, its permissions and the consequences of its actions become part of the security boundary.
Prompt injection targets how a system follows instructions
Prompt injection occurs when malicious instructions in a prompt or in content the system processes influence its behavior. In an application that retrieves or renders external material, for example, hostile content could attempt to steer the system toward an unintended action or disclosure. OWASP’s Q1 2026 incident roundup describes an indirect prompt-injection case in which rendering behavior could be influenced and enterprise data could be leaked through an external request; substantial user interaction was required in that described case. It is an example of a failure mode, not evidence that every AI application is vulnerable in the same way.
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Data poisoning targets data integrity
Data poisoning involves compromising or manipulating data used by an AI system, with the aim of affecting its behavior or outputs. It is distinct from a conventional phishing message or malware infection: the target is the integrity of the system’s data. The exact exposure depends on how the system obtains, selects, and updates the data it uses.
Tools, identities, and permissions can turn a bad output into an action
A model that only produces text has a different impact boundary from an agent connected to business tools, accounts, or sensitive information. A mistake or manipulated response becomes more consequential when the system can take action or access data. OWASP’s Q1 2026 roundup classifies reported failure patterns including excessive agency, tool misuse, identity and privilege abuse, sensitive-information disclosure, unbounded consumption, cascading failures, prompt injection, and improper output handling. These categories help teams examine possible failure paths; they do not mean every agent deployment has experienced them.
What do current risk guides and incident reports tell organizations?
OWASP’s 2026 LLM Top 10 is a community-developed guide to risks in LLM applications, with attack scenarios, mitigations, and mappings to frameworks including NIST and MITRE ATLAS. Use it as a practical taxonomy for identifying questions to investigate, not as a probability ranking that predicts which risk will affect every organization.
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OWASP’s incident roundup covers reports from January 1 through April 11, 2026, and expressly says it is not exhaustive. Its examples are useful for understanding how failures can appear in deployed systems, particularly when models interact with tools and sensitive information. They are not a census of incidents or proof of broad prevalence.
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NIST’s August 2026 summary of a January 2026 Cyber AI Profile workshop records discussion themes including governance challenges, AI attack surfaces, consistent taxonomy, risk-based guidance, usability, profile stability, and opportunities for AI-enabled cyber defense. It documents a discussion, not a finalized control standard. Taken together, these sources support a recurring risk-management process rather than a claim that any one list or framework makes a system fully secure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a team assess and reduce generative-AI security risk?
Start with the deployed system, not an abstract model label. Map what information enters the application, what the model can return, which tools or services it can reach, and which identities and permissions those connections use. Then test the paths that matter for the system’s actual purpose and data. OWASP’s red-teaming guidance organizes testing across model evaluation, implementation, infrastructure, and runtime behavior; its 2025 announcement recommends tailoring tests to context, such as prompt-injection testing for a public chatbot or data-leakage testing where sensitive intellectual property is handled.
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1. Map the application and its authority
- Identify the model, application components, data sources, connected services, and the points where information moves between them.
- Record which tools the model can invoke, what each tool can do, and which identity or permissions it uses.
- Trace where sensitive information can enter, appear in a response, or be sent to an external destination.
2. Test the risks that fit the use case
- For systems that process untrusted material, test whether hostile instructions can change behavior or prompt unintended disclosure.
- For systems that handle sensitive information, test whether it can be exposed through responses, tool use, or external requests.
- For agents, test tool use, privilege boundaries, identity handling, and the possibility that one failure could trigger further actions.
- Include the model, application integration, infrastructure, and runtime behavior rather than limiting evaluation to model responses in isolation.
3. Reduce unnecessary access and limit the consequences of failure
- Give tools and agent identities only the access needed for the task; assess permissions as part of the AI system, not as an unrelated IT detail.
- Review whether sensitive data needs to be available to the application and where outputs can be delivered.
- Examine how errors, unexpected outputs, or misuse could affect connected systems, including whether a failure could cascade.
4. Feed findings into governance and repeat the evaluation
- Assign responsibility for reviewing findings, deciding which risks are acceptable, and tracking remediation.
- Monitor the system in operation and revisit its risk assessment when its model, integrations, data, permissions, or use changes.
- Keep incident procedures relevant to the application’s data and tool access so the team can respond to a disclosure or unintended action.
This approach reflects the coverage in OWASP’s red-teaming guide and the governance and risk-based themes discussed at NIST’s 2026 workshop. A framework can organize the work, but it cannot replace adversarial testing of the specific deployment.
What should readers conclude about AI and cybersecurity readiness?
Generative AI is neither only a new attacker capability nor only a new application vulnerability. It can assist offensive activity, and systems built with it can expose new paths through prompts, data, integrations, and delegated authority. The sources support taking both seriously while avoiding claims of universal attacker transformation or universal exposure. Treat AI security as part of ordinary cybersecurity risk management: define what the system can access and do, test its real-world behavior, remediate findings, and keep monitoring as the deployment evolves.
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