A prompt injection attack is an attempt to manipulate an AI system by putting attacker-controlled instructions in user input or external content that the system combines with its trusted instructions. It can change an answer, expose hidden context, or—when an AI agent can use tools—redirect actions.
What is a prompt injection attack?
NIST defines prompt injection as “An attack which exploits the concatenation of untrusted input with a prompt constructed by a higher-trust party such as the application designer.” In plain terms, an application may give an AI trusted instructions about its role and task, then combine them with less-trusted text. An attacker tries to make that text influence the model as if it were an instruction. See the NIST glossary definition and the NIST AI 100-2 E2025 taxonomy, published in March 2025.
The central security problem is mixed trust: trusted directions and untrusted data share the model’s context, and the application may not reliably preserve the distinction. This resembles familiar software security problems in which data is misinterpreted as instructions, but occurs in the generative AI setting. OWASP also lists prompt injection as LLM01:25 in its 2025 Top 10 for LLM and Gen AI.
What is the difference between direct and indirect prompt injection?
The distinction is where the attacker-controlled instruction enters the system.
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| Type | Entry point | Example |
|---|---|---|
| Direct prompt injection | The user’s prompt or other direct input to the AI | A user includes instructions intended to override or redirect the system’s task. |
| Indirect prompt injection | External content the AI later retrieves or processes | A webpage, email, or document contains instructions that enter the AI’s context when an application reads or summarizes it. |
NIST’s taxonomy discusses indirect attacks involving external documents and webpages in retrieval-augmented generation (RAG) systems. The person asking the AI to process that content may not be the attacker and may not even know the malicious instructions are present.
How can prompt injection affect AI agents?
With a text-only system, an attack may manipulate the response or try to reveal information in the model’s context. With an agent, the possible consequences also depend on what the model can do: select tools, retrieve material, run code, or trigger other actions. If untrusted content steers those choices, the agent may act outside the user’s intended task, creating potential privacy, integrity, or availability problems.
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NIST’s Center for AI Standards and Innovation describes agent hijacking as a type of indirect prompt injection: malicious context can redirect an agent’s task or actions. The risk therefore depends not just on the wording of the prompt, but on what information enters context, which tools are available, what model outputs can trigger, and what checks occur before actions are taken. NIST discusses these factors in its guidance on strengthening AI agent hijacking evaluations.
Is prompt extraction the same as prompt injection?
No. Prompt extraction is a related attack aimed at revealing a system prompt or other context that is normally hidden from the user. Prompt injection is the broader attempt to manipulate a system by exploiting how it combines trusted instructions with untrusted input. An injection may seek disclosure, but not every injection is a prompt-extraction attempt. NIST defines prompt extraction separately.
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Can prompt injection be prevented?
No single prompt or finite set of guardrails can guarantee universal immunity to adversarial prompts. NIST reported a mathematical proof supporting an ongoing monitor-and-update approach to AI security. Apostol Vassilev, a NIST senior scientist, said the proof shows “there is no finite set of guardrails that is universally robust against adversarial prompts.” That does not mean defenses are futile: it means prompt injection should be treated as an application-security and evaluation problem that requires continuing work, rather than a flaw fixed once by clever prompt wording. See NIST’s June 9, 2026 explanation.
- Limit access to external data and tools to what the task requires.
- Evaluate attacks against the specific tasks and actions an application supports, and update tests as threats change.
- Check model outputs before they trigger consequential actions instead of treating a prompt instruction as the only security boundary.
- Assess performance across repeated attempts; a defense that resists one test is not proof that it will resist others.
What do prompt-injection test results show?
A NIST CAISI report published March 23, 2026 described a public red-teaming competition involving 13 frontier models, more than 250,000 attack attempts, and over 400 participants. The report found at least one successful attack against every target model. This is evidence that the challenge is difficult, not a real-world attack rate: it does not mean every attempt succeeds, that models are equally vulnerable, or that these results predict the success probability for a particular application. NIST also emphasizes evolving, task-specific evaluations and measuring performance over multiple attempts in its report on AI agent security competition findings.
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The available cited findings do not establish a population-wide prevalence rate, a universal probability that an attack will succeed, or a mitigation that fully prevents prompt injection. For any particular system, exposure depends on its inputs, permissions, tools, and safeguards.
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