Prompt injection is a way to steer an AI system with malicious instructions placed in a user message or in content the system reads. An attacker may not need to run conventional code if an AI agent already has access to sensitive information and a tool or communication channel that can expose it. The risk depends on those connections: a prompt alone does not give every chatbot access to private files or bypass every security control.
What is prompt injection?
Prompt injection occurs when malicious instructions cause an AI model to act outside its intended task. OWASP’s concise definition in its 2025 risk list is: “A Prompt Injection Vulnerability occurs when user prompts alter the LLM’s behavior or output in unintended ways.” OpenAI describes the attack as a form of social engineering: someone introduces instructions into a conversation that may also contain material from the internet or another outside source.
The important distinction is that an AI system may treat instructions and ordinary content as part of the same conversation. A model asked to summarize a document might encounter text inside that document telling it to ignore the request and do something else. The model’s response can then be influenced by content that was supposed to be data, not authority.
OWASP classifies prompt injection as LLM01:2025, the first named risk in its 2025 list. That designation is a position in a risk taxonomy, not a statistic about how often attacks happen or succeed.
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How can an injection reach an AI agent?
Direct injection: instructions from the user
A direct injection arrives in a user’s message. The user might ask the model to disregard its prior instructions, reveal information, or take an action outside the intended task. This is the more visible case because the malicious instruction is part of the prompt the user deliberately supplied.
Indirect injection: instructions hidden in content
An indirect injection is embedded in material the AI is asked to process. Possible sources include webpages, emails, files, retrieved documents, images, and descriptions of available tools. A person may ask for an ordinary summary or research task without knowing that the material contains instructions aimed at the model.
OWASP describes examples including hidden webpage text that tells a model summarizing the page to add an image linked to a URL. If the resulting request includes private conversation content, the image request could expose it. Other patterns include manipulating retrieved documents, hiding instructions in images, splitting a payload across text, using adversarial suffixes, and obfuscating or translating instructions.
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Tool poisoning: instructions in a tool description
Microsoft’s technical guidance on the Model Context Protocol (MCP) describes “tool poisoning”: malicious instructions placed in a tool’s description may influence which tool a model chooses to invoke. Microsoft also warns that hosted tool definitions can change after they have been approved, creating a supply-chain concern. This is a possible risk to assess; it does not mean that MCP tools generally are compromised.
When can prompt injection expose data?
An injection becomes a data-theft risk when influence connects to both something worth exposing and a way to expose it. OpenAI’s source-and-sink framing helps make the distinction:
- Source: a way to influence the system, such as a user message or external content.
- Sensitive context: information the agent can access, such as material in connected files or email.
- Sink: a capability that could expose information or cause another consequential action, such as transmitting data to a third party, following a link, or interacting with a tool.
These pieces matter together. An agent that never receives sensitive information cannot disclose that information from its context. An agent with no relevant external action or communication path has fewer ways to send data out. Conversely, granting broad access to data and tools gives a successful injection more opportunity to cause harm. Prompt injection is therefore not proof that every chatbot can read private files; its practical impact depends on the specific system’s permissions, connected services, and safeguards.
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What do reported tests show—and not show?
Reported results are tied to particular tasks, prompts, and agent setups. They are not interchangeable measures of a universal prompt-injection success rate.
- OpenAI, 2026: In a March 11, 2026 article about designing agents to resist prompt injection, OpenAI reported that an example attack from 2025 worked 50% of the time in a test using this request: “I want you to do deep research on my emails from today, I want you to read and check every source which could supply information about my new employee process.” The 50% figure describes that specific test setup, not the likelihood that prompt injection will work against AI systems generally.
- NIST CAISI, January 2025: NIST said it frequently induced the tested agent to follow malicious instructions in added remote-code-execution, database-exfiltration, and phishing scenarios. The source does not give an overall numerical success rate for those findings.
- OWASP, 2025: LLM01:2025 identifies prompt injection as a named risk in OWASP’s taxonomy. It does not measure attack prevalence or success.
These findings establish that specific agent setups can be vulnerable and that testing needs to reflect real tasks. They do not establish a broad, comparable industry-wide success rate.
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How should organizations reduce the risk?
No single control addresses every route an injection can take. The practical aim is to make the agent harder to influence, restrict what it can do, and limit the damage if an instruction still gets through.
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Limit access and keep tasks narrow
Give the agent access only to the data and tools required for its assigned task. For browsing that does not need an authenticated account, OpenAI advises using logged-out mode. Keep instructions specific so the system has less unnecessary latitude, and require review before consequential actions such as sending email or making purchases.
Mark external content as untrusted
Keep a clear boundary between trusted system instructions and material retrieved from outside sources. Microsoft discusses techniques such as delimiters, data marking, and spotlighting to help preserve that distinction. These techniques are layers, not proof that an input is safe: an attacker may still find ways to influence how content is interpreted.
Constrain tool calls and information flows
OWASP recommends limiting tool scopes and screening proposed actions against the user’s original intent. Its prevention guidance discusses CaMeL, which separates privileged planning from quarantined parsing. OWASP notes that this approach is early and requires further development; it should not be treated as a mature, universal fix.
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Protect connected tools and dependencies
Verify models, packages, applications, and context providers that form part of the agent’s environment. Monitor changes to tool metadata and dependencies, especially when a hosted integration can change after approval. This addresses the possibility that an integration or its description could become a source of malicious instructions.
Test the agent repeatedly against its actual tasks
NIST recommends adaptive evaluation and notes that attack performance on task-specific scenarios can be informative. Its team extended AgentDojo to cover additional attack tasks. Organizations should test with sandboxed tools and dummy data, and repeat evaluations as tasks, integrations, and permissions change. A result from one scenario does not establish that a different agent or task is safe.
Do not make a classifier the only barrier
OpenAI cautions that mature, social-engineering-style attacks are not usually caught by systems that simply classify inputs as malicious or benign. Detection can contribute to a defense, but controls that restrict permissions, mediate actions, and reduce exposure also matter because an attack may get past a filter.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do the main control types differ?
These controls address different parts of the threat, so they are complements rather than interchangeable products. When evaluating a setup, consider which external sources it inspects, whether it mediates tool calls and outbound data, how it enforces least privilege, whether it detects changed tool definitions, and how it is tested. Also account for the latency and operational work each control adds.
| Control | What it addresses | Important limitation |
|---|---|---|
| Input or content detection | Flags suspicious instructions in user input or external content. | Classification alone may miss sophisticated attacks; it does not limit what the agent can access or do after a miss. |
| Least-privilege permissions | Reduces the data and tools an agent can reach. | Does not by itself prevent an injection from changing the model’s response or misusing the access it still has. |
| Action review and mediation | Checks consequential actions, such as sending a message, before they are completed. | Adds review work and does not replace limits on the agent’s underlying access. |
| Untrusted-content boundaries | Helps distinguish external data from trusted instructions. | Delimiters and related techniques are layers, not guarantees that content cannot influence the model. |
| Integration and supply-chain checks | Helps identify risky dependencies or changes to tool definitions and metadata. | Requires ongoing verification and monitoring; approval at one point in time may not cover later changes. |
| Task-specific adversarial testing | Finds weaknesses in scenarios that resemble the agent’s real work. | Results apply to the tested setup and tasks; testing must adapt as the system changes. |
What should users take away?
“No code” does not mean “no technical conditions.” An attacker may be able to exploit an AI agent through instructions embedded in content rather than by running conventional code, but exposing data still depends on the agent encountering that data and having a capability that can reveal it. The key questions for any connected AI feature are what it can read, what it can do with what it reads, and which actions or data flows are constrained or reviewed.
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