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What are the cybersecurity risks of generative AI?
The clearest way to understand the risk is to separate AI-assisted attacks from attacks on AI systems. In the first, a malicious actor uses generative AI to assist cyber activity. In the second, someone targets an AI application’s model, data, prompts, integrations, tools or permissions. A single incident could involve both, but the defenses and failure modes are not interchangeable.
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| Risk category | What is being targeted or used | What the evidence supports |
|---|---|---|
| AI-assisted attacks | Generative AI is used to assist offensive activity. | NIST says it could lower barriers to offensive capabilities or ease their automation, potentially augmenting hacking, malware and phishing. This describes a plausible capability and risk, not a measured increase in attack frequency. |
| Attacks on AI systems | The AI system or its surrounding application, data, prompts, integrations, tools or permissions. | NIST identifies risks including prompt injection and data poisoning. OWASP’s 2026 guidance and incident roundup describe further application and agent failure modes. |
The distinction matters operationally: traditional cybersecurity controls remain necessary, but they do not by themselves address how an AI application interprets instructions, uses tools or handles information.
How might attackers use generative AI?
NIST’s profile says generative AI could lower barriers to offensive cyber capabilities or make some capabilities easier to automate. It may augment hacking, malware and phishing activity. The Cyber Threat Alliance’s January 2025 report likewise frames the issue as both malicious use of generative AI and cyber threats aimed at generative AI systems, while emphasizing that foundational cybersecurity principles still matter.
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These sources support treating AI as a potential aid to malicious activity—not as proof that all attackers now have new capabilities, that all attacks use AI, or that AI alone causes a breach. They do not provide a reliable prevalence figure for AI-enabled attacks. Defenders should therefore assess concrete exposure and observed behavior rather than assume either that AI has transformed every threat or that it can be ignored.
Can AI itself be attacked?
Yes. An AI application has an attack surface that can include the model, its input and training data, prompts, connected services, tools, identities and permissions. Two risks highlighted by NIST illustrate why this surface is different from an ordinary software vulnerability.
Prompt injection
Prompt injection attempts to influence a model’s behavior through instructions in its inputs or other content it processes. In an application connected to tools or sensitive information, the concern is not only an undesirable answer: manipulated behavior may affect how the application uses its integrations or handles data. A prompt is not a reliable security boundary merely because it tells the model to ignore unsafe instructions.
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Data poisoning
Data poisoning targets data integrity by introducing or manipulating data in a way that can affect an AI system. The relevant exposure depends on how a particular system acquires, processes and uses data; the term should not be treated as synonymous with a phishing message or a malware infection.
Agents, tools and permissions
Systems that can take actions through tools or operate with an identity introduce consequential questions about what they are allowed to do, which resources they can reach and how their actions are checked. OWASP’s Q1 2026 incident roundup maps reported examples to categories including excessive agency, tool misuse, identity and privilege abuse, sensitive-information disclosure, cascading failures, prompt injection, unbounded consumption and improper output handling. These categories help teams examine possible failure modes; they do not establish that every agent deployment has those weaknesses.
One example in the roundup describes indirect prompt injection that could influence rendering behavior and leak enterprise data through an external request. The described case required substantial user interaction. It is a useful illustration of how data handling and application behavior can intersect, not evidence that the same outcome is common or inevitable.
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What do current AI-security guides establish?
OWASP’s 2026 LLM Top 10 is a community-developed guide to risks in large-language-model applications. It includes attack scenarios and mitigations and maps risks to frameworks including NIST and MITRE ATLAS. Use it as a practical taxonomy for reviewing an application, not as a probability ranking that predicts which risk is most likely at any particular organization.
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How should organizations assess and reduce the risk?
Start with the specific AI system in use: what it can access, what actions it can take, what information it receives and returns, and who is accountable for its operation. Then test the deployed system across its lifecycle rather than treating a model check as a complete security review.
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1. Map the system and its authority
- Record the model and application components, their integrations, the data they handle and the routes by which inputs and outputs move.
- For agents, identify tool access, identities, privileges and actions that can have external or consequential effects.
- Set boundaries around sensitive information and actions. A system should not receive broader access simply because it can produce a fluent answer.
2. Test the real deployment adversarially
OWASP’s GenAI Red Teaming Guide organizes testing into four areas: model evaluation, implementation testing, infrastructure assessment and runtime behavior analysis. OWASP’s 2025 announcement advises tailoring tests to context—for example, testing prompt injection in a public chatbot and data leakage where sensitive intellectual property is handled.
Useful assessment questions include whether tests cover the actual integrations and permissions, whether the system can be induced to mishandle sensitive data, and whether its outputs are validated before other software or people act on them. A test that examines only the model in isolation may miss failures created by application logic, infrastructure or connected tools.
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3. Limit and review tool use
- Give each AI component only the permissions needed for its defined task, and assess the identity it uses as well as the tools it can invoke.
- Put appropriate checks around consequential actions rather than assuming model instructions will reliably prevent them.
- Review tool calls and data flows so teams can investigate unexpected behavior and identify where a failure could cascade.
4. Monitor, remediate and revisit
Use findings to fix the application and infrastructure, adjust access, and update operating procedures. Continue monitoring runtime behavior and revisit tests when models, integrations, data sources or permissions change. Keep an incident process that accounts for both conventional security events and AI-specific behavior, including unexpected tool use or disclosure of sensitive information.
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This approach aligns with the lifecycle coverage in OWASP’s red-teaming guidance and the incident categories in its roundup. It also recognizes that governance and risk-based guidance are still evolving: NIST’s August 2026 report summarizes discussion at a January 2026 Cyber AI Profile workshop, including governance challenges, profile stability, AI attack surfaces, taxonomy, usability and risk-based guidance. That workshop summary records discussion themes; it is not a finalized control standard.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should readers take away from the “nobody’s fully ready” claim?
The headline should not be read as evidence that organizations are universally unprepared. The sources establish a changing risk landscape, a wider set of AI-specific failure modes and the need to apply security practices to AI deployments. They do not establish a universal readiness score or a single control that makes a system fully secure. Treat readiness as an ongoing process: understand the particular deployment, test it adversarially, constrain its access, monitor what it does and improve controls as the system changes.
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