Prompt Injection: What It Is, How It Works, Examples, Risks, and How to Prevent It

Introduction

Imagine asking an AI agent to read your emails and summarize the important ones. This sounds harmless—until one of those emails contains instructions that trick the AI into doing something you never intended.

The AI might be processing a routine email, webpage, PDF, or search result, yet that content could harbor hidden instructions compelling it to disregard your commands, expose sensitive information, or perform unauthorized actions.

This is the core concept of ‘prompt injection.’

As AI systems evolve beyond simple chat interactions to tasks like browsing websites, reading files, using business applications, and operating tools on behalf of users, prompt injection has emerged as a critical AI security issue. OWASP has listed prompt injection as LLM01:2025—the top risk for Large Language Model applications.

Fortunately, the risk of prompt injection can be mitigated through measures such as secure architecture, restricted permissions, monitoring, sandboxing, human approval, and the careful handling of untrusted content.

In this guide, we will explain what prompt injection is, how it works, its various types, real-world examples, associated risks, prevention methods, and best practices for both AI agents and general users.

What is prompt injection?

Prompt injection is an AI security attack in which malicious or misleading instructions compel a language model to behave differently than the user’s original request intended.

Simply put, an attacker tricks the AI into treating untrusted content as instructions.

OpenAI describes prompt injection as a form of social engineering against conversational AI, where third-party content misleads the AI into performing actions not requested by the user.

Quick Answer:

Prompt injection occurs when instructions embedded in user input or external content force an AI model to generate a response or perform an action that was not the user’s intent.

This attack becomes even more critical when the AI agent has access to:

Email
Cloud storage
Databases
Internal documents
Browsers
APIs
Payment systems
Code execution
Business applications
Personal information

A chatbot giving an incorrect answer is a problem.

However, an AI agent that reads a malicious webpage and then performs an action using your authorized access represents a major security issue.

How does prompt injection work?

In simple terms, a prompt injection attack works like this:

User request → AI processes content → Malicious instruction enters the context → AI follows the malicious instruction → Unintended outcome

For example, suppose a user asks:

“Read this product page and tell me if the product is worth buying.”

Typically, the page contains product information.

However, an attacker could embed malicious instructions within the page that influence the AI agent reading it.

Crucially, the attacker does not need direct access to the AI system itself.

They simply need to control or influence the content that the AI will subsequently read.

A simple example

Imagine an AI research agent assigned the following tasks:

Searching the web.
Comparing multiple products.
Recommending the best option.

A webpage contains hidden or misleading instructions.

The AI reads that page as part of its research.

If the system fails to properly distinguish between data and instructions, the malicious content can influence the agent’s behavior.

This can lead to:

Incorrect recommendations
Manipulated summaries
Dangerous tool calls
Unauthorized information disclosure
Distraction from the user’s intended task

This is why modern AI security cannot rely solely on system prompts.

Direct vs. Indirect Prompt Injection

Prompt injection is generally categorized into two main types.

1. Direct Prompt Injection

Direct prompt injection occurs when an attacker inserts malicious instructions directly into the AI’s input.

For example, a user might deliberately try to force the AI to ignore its standard instructions.

Direct injection is relatively easy to understand because the malicious content originates directly from the user.

OWASP defines direct prompt injection as input that alters the model’s behavior in a direct and unintended manner.

Common Characteristics

The attacker interacts directly with the AI.
The malicious instruction appears within the user’s input.
The objective may be to bypass restrictions or alter behavior.
The attacker can iteratively modify the input.

2. Indirect Prompt Injection

Indirect prompt injection is a greater concern for AI agents.

In this scenario, malicious instructions are embedded within content that the AI retrieves from an external source.

Examples include:

Websites
Emails
PDFs
Word documents
Spreadsheets
Search results
GitHub repositories
Knowledge bases
Images
Tool outputs

The user may never actually see the malicious instruction.

OWASP specifically identifies external websites and files as potential sources of indirect prompt injection.

An Example Scenario

You ask the AI:

“Check my recent emails and summarize important messages.”

One email contains content designed to mislead the AI.

The AI processes that email while fulfilling your request.

If the agent has access to other applications, the malicious content could influence its subsequent actions. This is why OpenAI and other researchers working on AI safety view prompt injection as a system-level security issue rather than merely a problem of poorly crafted prompts.

Common Examples of Prompt Injection

Prompt injection can occur in various ways.

Example 1: Malicious Webpage

An AI browser agent visits a webpage while researching a topic.

The page contains instructions intended not for human readers, but for the AI.

The attacker’s goal is to alter the agent’s behavior.

Potential Impact

The AI might:

Ignore user constraints
Recommend a specific page
Navigate to an unsafe link
Perform an unintended action

Example 2: Malicious Email

An AI assistant is tasked with processing incoming emails.

An email contains instructions designed to manipulate the assistant.

If the AI agent has access to other services, the attacker can use that email to facilitate unauthorized activity.

OpenAI has specifically highlighted scenarios where malicious email content attempts to manipulate an agent that has access to the user’s other data.

Example 3: Compromised Document

Suppose a company uses a RAG-based AI assistant to answer questions based on internal documents.

A document contains instructions that were not part of the actual business content.

When the document is retrieved, the AI may process those instructions alongside the legitimate information.

The assistant might:

Provide incorrect answers
Ignore critical information
Follow malicious instructions
Reveal information it should not disclose

Example 4: Image-based Injection

Modern AI models can interpret images.

This creates another potential attack vector.

Malicious instructions can be embedded within images processed by the AI system. OWASP identifies multimodal prompt injection—where instructions can be hidden within visual content—as an emerging risk. This means security teams should not assume that plain text alone can be relied upon.

Why is prompt injection dangerous?

The biggest risk isn’t simply that the AI might provide an incorrect answer.

The real concern arises when the AI has access to data and tools.

Potential consequences include:

Exposure of sensitive information
Unauthorized use of tools
Data theft or leakage
Flawed business decisions
Manipulated suggestions
Privacy violations
Unauthorized transactions
File modifications
System changes
Security incidents

OWASP identifies the exposure of sensitive information, unauthorized access to functions, execution of arbitrary commands, and manipulation of critical decisions as potential impacts.

The issue of "blast radius" (scope of impact)

Consider two systems.

System A

The AI can only answer questions.

System B

The AI can read emails, access files, browse the web, call APIs, and modify records.

A successful prompt injection on System A might result in an incorrect answer.

The same attack on System B could lead to far more serious consequences.

That is why AI security should focus not only on whether an injection is possible, but also on what the AI can do if the injection succeeds.

Prompt Injection vs. Jailbreaking

These terms are related, but they are not exactly identical.

Feature	Prompt Injection	Jailbreaking
Main goal	Manipulate AI behavior	Bypass safety restrictions
Source	User or external content	Usually direct user input
Can be indirect?	Yes	Usually discussed as direct
Can involve tools?	Yes	Sometimes
Can cause data leakage?	Yes	Potentially
Security concern	Broad AI application security	Model safety and policy bypass

OWASP treats jailbreaking as a form of prompt injection in which an attacker attempts to make a model disregard its safety protections.

The distinction becomes particularly useful when designing security controls.

Types of Prompt Injection Attacks

Prompt injection is evolving rapidly, but certain patterns are particularly significant.

Direct Injection

The attacker inserts instructions directly into the AI conversation.

Indirect Injection

Malicious instructions arrive via external content.

Multimodal Injection

Instructions are hidden within images or other media that the AI can process.

Payload Splitting

A malicious instruction is split across different parts of the content and reassembled by the model.

OWASP includes payload splitting and multimodal injection in its prompt injection scenarios.

Tool-based Injection

Malicious content arrives via a tool response and attempts to influence subsequent tool calls.

This is particularly relevant for agentic systems.

RAG-based Injection

The AI is compromised when a compromised document or knowledge source is retrieved as contextual information.

Prompt Injection in AI Agents

This is where the problem becomes even more critical.

Traditional chatbots primarily generate text.

AI agents can rapidly perform tasks such as:

Searching
Browsing
Reading files
Sending messages
Using APIs
Executing code
Modifying documents
Making purchases
Updating databases
Interacting with external applications

OpenAI describes the security issue in terms of “source” and “sink”: the attacker needs a way to influence the system and a dangerous capability that can be misused in a harmful context.

Example

Consider this workflow:

User request

“Find the best hotel for my trip.” ↓

The agent searches the web

↓

The agent encounters harmful content

↓

The harmful content attempts to redirect the agent

↓

The agent has access to personal information or tools

↓

Potentially harmful action

The key point is simple:

The more authority an AI agent possesses, the more critical it becomes to limit the actions that can result from a successful prompt injection.

Prompt Injection in RAG Systems

Retrieval-Augmented Generation (RAG) helps an AI system retrieve information from documents or databases before generating a response.

While RAG improves the relevance of information, it does not completely eliminate the risks of prompt injection.

OWASP explicitly states that techniques like RAG and fine-tuning do not fully resolve prompt injection vulnerabilities.

Why?

Because the model may treat the retrieved content as context.

If the system cannot clearly distinguish between:

Trusted instructions

and

Untrusted information

…then the model might misinterpret the data as a command.

Best Practices for RAG Security

Implement these measures:

Document trust levels
Metadata
Access control
Content isolation
Output validation
Retrieval filtering
Permission boundaries
Audit logging

Prompt Injection in Multimodal AI

AI systems are increasingly processing:

Text
Images
PDFs
Audio
Video
Screenshots
Web pages

This expands the attack surface.

For example, an image might contain information that appears harmless to a human but is interpreted differently by the AI system.

Therefore, security testing should focus not only on text input but on all types of content fed into the model.

Detecting Prompt Injection

There is no single, foolproof method for detecting prompt injection.

This is an important point to understand. While a simple keyword filter can catch obvious attacks, sophisticated attacks may employ:

Natural language (conversational speech)
Indirect phrasing
Images
Encoded content
Multi-step instructions
Social engineering
Authentic-sounding business language

OpenAI notes that prompt injection is an evolving security challenge, and they employ multiple layers of defense rather than relying on a single method.

Useful indicators for detection

Security systems can monitor for:

Instructions inconsistent with the user’s task
Requests for confidential information
Unexpected tool calls
Attempts to alter the agent’s objective
Requests to interact with external sources
Unusual data access
Sudden changes to the plan
Suspicious tool sequences

Microsoft recommends techniques such as Prompt Shield, plan-drift detection, critic agents, tool-chain analysis, information-flow control, and least-privilege access.

How to Prevent Prompt Injection

There is no single, foolproof solution.

The most robust approach is “defense-in-depth” (layered security).

1. Do not trust external content

Treat webpages, emails, documents, and search results as data—not as instructions to be trusted blindly.

This principle should be implemented at the architectural level.

2. Separate instructions from data

Clearly distinguish between:

System instructions
Developer instructions
User instructions
Retrieved content
Tool output
Third-party content

The AI needs to know which sources are authoritative.

Microsoft’s agent security guidance also warns against including untrusted end-user input within system-level messages.

3. Follow the principle of "least privilege"

Grant AI agents only the permissions they need.

For example:

If an agent only needs to read invoices, it should not have permission to delete financial records.

If the AI only needs to research products, it likely does not need access to your email account.

As part of its “defense-in-depth” strategy, Microsoft recommends granting necessary permissions for the shortest possible duration.

4. Require human approval for high-impact actions

Require confirmation before performing actions such as:

Sending sensitive emails
Making purchases
Deleting information
Sharing private files
Changing account settings
Transferring funds
Modifying production systems

Human approval is crucial for actions that are irreversible or financially significant.

5. Use sandboxing

If the AI can execute code or interact with the computer, isolate its environment.

Sandboxing can restrict:

File-system access
Network access
Credentials
System resources

Modern AI security architectures employ a combination of model-level security and environmental containment. For instance, Anthropic considers sandboxing and network/file-system boundaries to be critical security measures for agentic systems.

6. Monitor tool calls

Don’t just look at what the AI says.

Also observe what it does.

For AI agents, useful telemetry includes:

Requested tools
Tool arguments
Accessed data
Destinations
Sequence of actions
User authorization
Security alerts

Microsoft’s new AI-agent security tools specifically emphasize monitoring agent activity, tool usage, and operational patterns.

7. Validate output

AI output should not automatically be treated as trusted application input. For example:

AI output → Payment API

This is riskier than:

AI output → Human review → Payment API

Before performing critical operations, the application should validate AI-generated commands and data.

8. Test using red teaming

Security teams should intentionally test AI systems using adversarial content (content created with malicious intent or to cause harm). Test the following:

Web pages
Emails
Documents
Images
Tool responses
RAG content
Multi-step workflows

Microsoft’s AI red-teaming tools cover scenarios involving indirect prompt injection. These tests check whether harmful content present in external data can trigger unintended actions.

Best Practices for Developers

If you are building AI applications, use this checklist:

Architecture

Keep sensitive credentials (such as passwords or API keys) out of the model context.
Separate trusted instructions from untrusted data.
Minimize tool permissions.
Use short-lived credentials.
Restrict outbound network access.
Run code in a sandbox.
Implement authentication and authorization for tools.

Runtime Security

Monitor tool calls.
Detect unexpected changes in plans.
Verify high-risk actions.
Log security-related events.
Require human approval for sensitive actions.
Rate-limit dangerous actions.

Testing

Conduct adversarial testing.
Test for direct and indirect injection.
Test multimodal inputs.
Test RAG sources.
Test tool outputs.
Test chained agent workflows.
Retest after major changes to the model or application.

Best Practices for Everyday AI Users

You don’t need to be a cybersecurity expert to reduce your risk.

1. Avoid overly broad instructions

Instead of:

“Read everything and do what is necessary.”

Use:

“Summarize these emails. Do not send messages, modify files, or access other accounts.”

Specific instructions minimize unnecessary permissions.

OpenAI recommends giving clear instructions to agents and avoiding assigning unnecessarily broad tasks.

2. Limit connected applications

Connect only the apps necessary for the task at hand.

3. Review the confirmation

If an agent asks you to approve an action, check the following:

What will happen?
What information will be shared?
Who will receive it?
Is this action actually related to your original request?

4. Exercise caution with sensitive accounts

Pay special attention when an AI agent interacts with:

Banking
Email
Password managers
Corporate systems
Medical records
Private documents
Financial information

5. Stop suspicious actions

If the AI suddenly asks to:

Reveal credentials
Open a website unrelated to the task
Upload private information
Send data to an unknown location
Ignore previous instructions

Then stop the action and investigate.

Prompt Injection Prevention: Pros and Cons

Security Technique	Main Benefit	Limitation
Prompt filtering	Can block obvious attacks	Sophisticated attacks may bypass filters
Prompt shields	Adds another detection layer	Not perfect
Least privilege	Reduces potential damage	Can reduce agent capabilities
Sandboxing	Limits system access	Adds architectural complexity
Human approval	Strong protection for critical actions	Reduces automation
Output validation	Prevents unsafe downstream actions	Requires careful implementation
Monitoring	Helps identify suspicious behavior	Detection may happen after an attempt
Red teaming	Finds weaknesses before attackers do	Requires continuous effort

The strongest strategy is not to select one of these methods.

It is to combine them.

What is the future of prompt injection security?

It is unlikely that the problem of prompt injection will disappear, even though AI models have become better at following instructions.

In fact, more capable AI agents could pose a greater security challenge because they can perform more critical tasks.

OpenAI views prompt injection as a new and emerging security challenge, emphasizing that security measures must address both the model’s behavior and the systems surrounding it.

Similarly, Anthropic has noted that browser-based agents face a significant risk of attack because they interact with websites, documents, advertisements, and other content that may not be trustworthy.

Consequently, the industry is moving towards a comprehensive security model:

Do not assume AI cannot be deceived. Design systems so that even if they are compromised, catastrophic damage can be avoided.

This shift is crucial.

Prompt Injection vs. Traditional Cybersecurity

While there are similarities between prompt injection and traditional attacks, there is a key difference.

Traditional software generally operates based on fixed rules.

Attackers can exploit these systems through methods such as:

SQL injection
Command injection
Cross-site scripting
Authentication flaws

AI systems add another layer: natural language (everyday speech) itself can influence the system’s behavior.

This makes it difficult to solve the prompt injection problem using simple pattern matching.

An attacker’s “payload” can resemble ordinary language.

It can even be embedded in content that the user never intentionally provided as an instruction.

That is why AI security requires a combination of traditional cybersecurity measures and AI-specific controls.

Conclusion

Prompt injection is one of the most significant security challenges facing modern AI systems.

Simply put, it is an attempt to force an AI to follow instructions it should not execute. However, the real danger arises when the AI possesses capabilities such as web browsing, accessing private information, using tools, executing code, or performing actions on behalf of the user.

Therefore, there is no single prompt, filter, or security product that can provide complete protection against it.

Instead, organizations should employ a combination of measures:

Isolation of untrusted content
Robust instruction hierarchy
Least-privilege access
Human approval
Tool verification
Sandboxing
Runtime monitoring
Output verification
Red teaming
Continuous security testing

For general users, the most practical advice is equally straightforward: provide specific instructions to the AI agent, limit unnecessary permissions, review critical actions, and avoid granting the agent more access than is required for the task.

As AI agents become more capable, AI security will also evolve. The goal should not be to make prompt injection impossible; rather, the aim should be to build systems that remain secure despite such attempts.

Frequently Asked Questions

1. What is prompt injection in simple terms?

Prompt injection is an attack that attempts to trick an AI into following harmful or unintended instructions. These instructions can originate directly from the user or indirectly through websites, emails, documents, images, or other external content.

2. Are prompt injection and jailbreaking the same thing?

No. Although they are related, prompt injection is a broader concept. Jailbreaking typically focuses on bypassing an AI model’s safety restrictions, whereas prompt injection can also involve manipulating an AI agent into performing unintended actions.

3. Can prompt injection lead to data theft?

When an AI system has access to sensitive information and tools, it can lead to data exposure. The actual impact depends largely on the application’s permissions, architecture, and security controls.

4. Can prompt injection occur via a website?

Yes. Malicious instructions can be embedded in content that an AI agent retrieves from a website. This is known as indirect prompt injection.

5. Can prompt injection attacks occur within PDFs?

Yes. PDFs and other documents can contain content designed to influence the AI system while the document is being processed.

6. Does RAG prevent prompt injection?

No. Although RAG can improve an AI application’s access to relevant information, the retrieved documents themselves may contain malicious or misleading instructions. Therefore, using RAG requires additional security measures.

7. Can prompt injection occur via images?

Yes. Multimodal AI systems can process both text and visual information; consequently, malicious instructions can be embedded in images or other visual content.

8. What is the best way to prevent prompt injection?

There is no single solution. Adopting a ‘defense-in-depth’ approach (security across multiple layers) provides more robust protection. This includes measures such as the principle of least privilege, content isolation, tool restrictions, output validation, monitoring, sandboxing, human approval, and red-team testing.

9. Do AI agents pose a greater risk than standard chatbots?

Their impact can be significant because agents can access and interact with external applications. The level of risk depends on the permissions, tools, data access, and security frameworks granted to the agent.

10. Can prompt injection be completely eliminated?

There is currently no reason to believe that prompt injection can be entirely eliminated. Modern security research focuses on making models more robust while also attempting to mitigate the impact of attacks that successfully breach the system. Both OpenAI and Anthropic view prompt injection as an ongoing security challenge rather than a problem that has been fully resolved.

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