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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAn AI agent attack targets an AI system that reads content and can take actions; phishing usually targets a person and tries to deceive them into clicking, replying, or sharing information. A common agent attack is indirect prompt injection: instructions are planted in an email, webpage, document, or other material the agent processes, in the hope that it treats those instructions as commands. The two attacks can overlap in the same message.
What counts as an AI agent attack?
An AI agent does more than generate text. It can reason through a task, plan steps, use tools or connected services, and sometimes retain information in memory. Those capabilities let it act on a user’s behalf, but they also create opportunities for an attacker to influence what it does. OWASP’s AI Agent Security Cheat Sheet identifies prompt injection, tool abuse, privilege escalation, data exfiltration, and memory poisoning among the risks.
In an agent attack, an attacker tries to manipulate the model or agent while it processes content or performs a task. The attack may succeed if the agent follows an attacker-provided instruction, changes its behavior, or uses a tool in an unintended way. Merely placing hostile text in a document does not mean an attack succeeded: the system must process it and respond vulnerably.
How is an agent attack different from phishing?
The key difference is the target and the intended success condition. Phishing deceives a human recipient. Prompt injection tries to influence a model processing content. Microsoft’s comparison of phishing and prompt injection describes prompt injection as instructions embedded in content an AI model processes, intended to override its original instructions or the user’s intent.
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| Aspect | Traditional phishing | AI agent attack or prompt injection |
|---|---|---|
| Target | A person reading a message or visiting a site | A model or agent processing content |
| Typical method | Impersonation, urgency, or another deception intended to persuade the person to act | Attacker-authored instructions presented as part of content the agent reads |
| Common payload | A deceptive link, attachment, or request | Instructions embedded in an email, webpage, document, file, or tool output |
| Intended success | The person clicks, replies, or provides information | The agent follows the instruction, potentially using a tool or connected service |
| Possible impact | Depends on what the person does and what information or access is exposed | Depends on the agent’s tools, data access, permissions, and memory |
These are not mutually exclusive categories. A phishing email can try to deceive its human recipient while also containing instructions aimed at an AI assistant that reads the email. Microsoft’s guidance on prompt injection in email and NIST’s discussion of indirect injection through ingested data both support this overlap.
How can an email or webpage trick an AI assistant?
Prompt injection can be direct or indirect. A direct injection comes from a user’s input. An indirect injection comes through external material the model processes, such as a webpage, email, or document. The instructions might be plainly visible or obscured from a human reader; what matters is that the agent receives them in its context. Microsoft’s overview of direct and indirect prompt injection explains why external content should be treated as potentially adversarial.
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- An attacker influences content. The agent is expected to read or retrieve a webpage, email, document, file, or search result the attacker can control or affect.
- The content includes instructions for the model. Those instructions may conflict with the user’s request or the agent’s trusted rules.
- The agent fails to keep data separate from instructions. It may interpret the hostile text as a command rather than as untrusted content to analyze.
- The agent may take an unintended action. If it has relevant tools or access, it could use a connected service, expose data, or otherwise exceed the user’s intent.
The presence of an instruction is a risk, not proof that every agent will obey it. Whether it can cause harm depends on the system’s design, the content it processes, and the authority the agent has.
Why do an agent’s permissions matter?
A text-only assistant that cannot access sensitive data or take external actions has a different potential impact from an agent allowed to send messages, modify records, run code, or use other connected tools. Microsoft’s AI agent shared responsibility guidance calls out prompt injection that drives tool actions, excessive agency, and confused deputy behavior. A confused deputy problem occurs when an agent uses authority it has been granted to carry out an action the user did not intend.
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Memory can also matter. If an agent retains information or instructions between tasks, manipulated or untrusted content may affect later behavior. OWASP includes memory poisoning among agent security risks. The specific consequences depend on how memory is stored, used, and controlled; not every system has persistent memory or the same access.
What do evaluations show—and what do they not show?
NIST’s January 2025 agent hijacking evaluation blog describes indirect prompt injection tests that include tasks such as remote code execution, database exfiltration, and automated phishing. These are evaluation scenarios, not evidence that every deployed agent is vulnerable or that those outcomes are common in production.
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In a March 23, 2026 report, NIST’s Center for AI Standards and Innovation described a public red-teaming competition involving 13 frontier models and scenarios for tool-use, coding, and computer-use agents. It reported examples in which models were more readily induced to send phishing emails, run malware, and exfiltrate login credentials. That count describes the models in that competition; it is not an estimate of how many agents generally are vulnerable. The report also does not establish a general prevalence rate for AI agent attacks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can organizations reduce the risk?
No single safeguard is established as a complete solution. The controls below aim to reduce the chance that untrusted content can steer an agent into an unintended action.
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- Treat retrieved and tool outputs as untrusted. Validate content before relying on it or using it to trigger another action.
- Separate trusted instructions from data. Preserve the source and provenance of content so the agent can distinguish user or system directions from material it is asked to analyze.
- Use least privilege and least functionality. Give an agent only the tools and permissions needed for its task, rather than broad access by default.
- Gate high-impact actions. Require human approval or another strong check before actions such as sending sensitive information, changing important records, or executing code.
- Evaluate realistic attack paths. Test whether indirect instructions in content can cause harmful tool use or data exposure. NIST’s evaluation work offers examples of agent-hijacking scenarios.
These measures are consistent with Microsoft’s agent security guidance and OWASP’s agent security recommendations. They limit an agent’s opportunity and authority to cause harm; they do not guarantee that prompt injection can be eliminated.
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