A prompt injection attack tries to steer an AI system by placing attacker-controlled instructions in user input or in external content the system processes. The risk comes from mixing those instructions with trusted task directions: a model may follow the malicious content, disclose hidden context, or—if connected to tools—take an unintended action.
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 attacker tries to make an AI treat untrusted text as instructions, even though the application intended that text to be data. The definition appears in the NIST glossary and NIST’s March 2025 AI 100-2 E2025 taxonomy.
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The underlying weakness is a trust-boundary problem. An application may combine developer instructions, a user’s request, and retrieved material in the model’s context. If the system cannot reliably keep trusted directions separate from untrusted data, malicious text can attempt to override or redirect the task. NIST relates this separation problem to longstanding software security flaws, while describing its particular form in generative AI.
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The distinction is where the attacker places the instructions. NIST’s taxonomy and the OWASP 2025 Top 10 for LLM and Gen AI distinguish direct attacks from indirect ones.
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| Type | Where the malicious instructions enter | Example |
|---|---|---|
| Direct prompt injection | The primary user’s prompt or other direct input to the AI. | A user asks the model to ignore its assigned task and follow different instructions. |
| Indirect prompt injection | External material that the system later retrieves or processes, such as a webpage or document. | An AI assistant summarizes a webpage containing instructions aimed at changing the assistant’s behavior. |
In an indirect attack, the person using the AI may not have supplied or even noticed the malicious text. It can arrive through a retrieval-augmented generation (RAG) system, an email, a document, or another resource the application places in context.
How can an injection affect an AI system?
The result depends on the application’s capabilities and the checks around its output. A text-only system might produce an altered answer or reveal context that was meant to remain hidden. A system that can retrieve information, use tools, execute code, or initiate other actions may carry the influence into operations affecting privacy, integrity, or availability.
- Output manipulation: The model gives a response shaped by the attacker’s instructions rather than the intended task.
- Context disclosure: The model may be induced to reveal hidden instructions or other context. Prompt extraction is a related attack that specifically attempts to reveal such material; it is not synonymous with every prompt injection. See the NIST prompt extraction glossary.
- Unintended agent actions: If an agent uses model output to select tools or perform actions, malicious context may redirect its behavior. NIST CAISI calls this kind of indirect prompt injection agent hijacking and discusses how to evaluate it in its January 17, 2025 guidance, Strengthening AI Agent Hijacking Evaluations.
Can prompt injection be prevented?
There is no prompt wording or fixed set of guardrails that establishes universal immunity. A June 9, 2026 NIST article reports a mathematical proof that no finite set of guardrails is universally robust against adversarial prompts. NIST scientist Apostol Vassilev put the implication this way: “There will always be some prompt that can potentially evade and defeat any defensive infrastructure that you have built around your AI system.” That does not make defensive work futile: NIST describes hardening systems and continuing to search for weaknesses as useful, ongoing security work. The distinction is between reducing risk and guaranteeing that every attack will fail.
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For system builders, the practical question is not just what instructions the model receives, but what untrusted material enters its context and what its output can trigger. NIST CAISI recommends evolving evaluations, testing by task, and considering attack performance across multiple attempts. A system with access to sensitive data or tools warrants evaluation tailored to those capabilities and the actions it can take—not just a check of whether it resists one example prompt.
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What do prompt-injection test results tell us?
A NIST CAISI report published March 23, 2026 describes a public red-teaming competition involving 13 target frontier models and more than 250,000 attack attempts from over 400 participants. The competition found at least one successful attack against every target model. This is evidence that resisting prompt injection is difficult, not a population-wide attack rate or proof that every real-world attempt succeeds. Results can differ substantially among models, and an aggregate competition result should not be treated as a guarantee about any particular application. See NIST CAISI’s competition findings.
The sources cited here do not establish a universal probability that an attack will succeed, or an authoritative prevalence rate across deployed AI systems. Risk depends on the entry point, the data and tools available to the application, and the safeguards around consequential actions.
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