AI Prompt Injection Scanner

AI Prompt Injection Scanner helps you inspect text and supported files for common prompt injection patterns that may attempt to manipulate an AI model, override instructions, extract sensitive information, trigger unauthorized actions, or alter how content is processed. The scanner uses browser-side heuristic analysis to identify suspicious instruction patterns, role manipulation, concealment techniques, encoded content, tool-action requests, and other security signals before the content is used with an AI system.

Upload a text-readable file Or paste text

Browser-side heuristic scanner for authorized defensive review. It detects suspicious instruction-like patterns and obfuscation signals; it does not prove malicious intent or guarantee that content is safe.

Prompt Injection Scan Summary

Paste text or upload a supported text-readable file, then scan it.

What Is Prompt Injection?

Prompt injection is a type of AI security risk in which specially crafted input attempts to influence an AI system in unintended ways. The attack can involve direct instructions entered by a user or instructions hidden inside external content such as documents, webpages, emails, retrieved data, or other sources.

OWASP identifies prompt injection as a major vulnerability category for LLM applications and notes that malicious instructions do not necessarily need to be obvious or human-readable to affect an AI system.

This makes it important to inspect AI inputs before they are passed into systems that can access private information, retrieve external data, call tools, or perform actions.

What Does the AI Prompt Injection Scanner Check?

The PKCapra AI Prompt Injection Scanner analyzes submitted content for multiple categories of suspicious behavior, including:

Instruction override attempts
Attempts to ignore previous instructions
System or developer prompt extraction
Role manipulation
Hidden or concealed instructions
Embedded instructions inside content
Fake authority or administrative claims
Tool, function, or API action requests
Sensitive-information extraction requests
Conditional injection patterns
Processing-behavior overrides
Context-boundary manipulation
Attention-manipulation techniques
Role-labelled injection patterns
Zero-width and invisible Unicode characters
Bidirectional Unicode controls
Unicode formatting controls
HTML and entity encoding indicators
Percent-encoded content
Escape-encoded content

The scanner reports suspicious findings with line and column information where applicable, making it easier to locate potentially problematic content.

Direct vs. Indirect Prompt Injection

Prompt injection can occur in different ways.

Direct Prompt Injection

A direct prompt injection occurs when the potentially malicious instructions are supplied directly as part of the input being processed by the AI system.

For example, an application may receive user-provided text that attempts to change the model’s assigned role or instruct it to disregard its original task.

Indirect Prompt Injection

Indirect prompt injection occurs when instructions are hidden inside external content that an AI system later reads.

Potential sources include:

Webpages
Documents
Emails
Retrieved RAG content
Tool responses
Uploaded files
External datasets
Encoded content

Microsoft describes indirect prompt injection as a risk when AI systems process untrusted external content that contains instructions the model may incorrectly interpret as legitimate commands.

This distinction is particularly important for AI agents, document-processing systems, RAG applications, and AI assistants that automatically consume external information.

Why Scan for Prompt Injection Before Using AI?

AI systems increasingly process content from sources that were not created specifically for the AI workflow. A document that appears to contain ordinary information to a human reader may contain additional instructions intended to influence an AI system.

Prompt injection can potentially contribute to:

Unintended model behavior
System or instruction leakage
Unauthorized information access
Data-exfiltration attempts
Unwanted tool execution
Manipulation of generated responses
Unsafe downstream actions
Compromised AI workflows

OWASP notes that prompt injection can affect confidentiality, integrity, and application behavior, while Microsoft recommends layered defenses rather than relying on a single detection mechanism.

How the PKCapra AI Prompt Injection Scanner Works

The scanner performs heuristic inspection directly in your browser.

Paste text into the scanner or upload a supported text-based file. PKCapra examines the content for recognizable prompt-injection and AI security patterns, then reports findings based on the detected signals.

The scanner can provide:

Finding category
Risk severity
Line number
Column information
Matching context
Overall safety score
Overall risk level
JSON security report

The analysis is designed to help you investigate suspicious content before sending it to an AI model or incorporating it into an AI workflow.

Risk Levels and Safety Score

The scanner provides an overall Safety Score from 0 to 100 along with a risk classification.

Individual findings can be categorized as:

High — potentially serious instruction or security-related behavior
Medium — suspicious content that deserves review
Low — weaker or contextual security signals
None — no significant matching pattern detected

A higher score indicates fewer detected risk signals. The score should be treated as a heuristic indicator rather than proof that content is completely safe.

No automated scanner can guarantee that previously unknown prompt-injection techniques will be detected.

Detecting Instruction Override Attempts

One common prompt-injection pattern attempts to replace, ignore, or override instructions that came earlier in the AI interaction.

Examples of suspicious behavior can include attempts to:

Ignore previous instructions
Replace the current task
Change system behavior
Override safety rules
Follow a new set of hidden instructions

Detecting these patterns can help identify content that should receive additional human review before being supplied to an AI system.

Detecting System and Developer Prompt Extraction

Some attacks attempt to make an AI system reveal information about its internal instructions, system prompt, developer instructions, configuration, or hidden context.

The scanner checks for patterns associated with requests to reveal or reproduce these types of internal instructions.

A detected pattern does not prove that an attack will succeed. It simply indicates that the content contains a recognizable signal associated with prompt or instruction extraction.

Detecting Role Manipulation

AI applications often use conceptual roles such as system, developer, user, assistant, or tool.

Malicious content may attempt to imitate one of these roles or instruct the model to adopt a different role.

The scanner checks for role-manipulation patterns that may attempt to change the expected instruction hierarchy or context.

Detecting Hidden and Encoded Instructions

Prompt injection does not always appear as ordinary readable text.

Potentially suspicious content may use:

Zero-width characters
Invisible Unicode characters
Bidirectional text controls
Formatting controls
HTML entities
Percent encoding
Escape sequences
Other concealment techniques

OWASP notes that prompt-injection inputs can affect models even when they are not readily visible or readable to humans.

Scanning these signals can be particularly useful when reviewing copied webpages, documents, datasets, or machine-generated content.

Tool and API Action Requests

AI agents can sometimes interact with external tools, APIs, files, databases, or other services.

Prompt injection becomes more consequential when manipulated instructions can influence these capabilities.

The scanner therefore checks for patterns associated with requests to:

Call tools
Execute functions
Access APIs
Perform external actions
Retrieve sensitive information
Send or modify data

Detection of such a pattern does not mean the requested action will actually occur. It identifies content that deserves additional security review.

Sensitive Data Extraction Signals

Another important category involves instructions that attempt to obtain confidential information.

The scanner checks for patterns associated with requests to expose or retrieve information such as:

Credentials
Tokens
Secrets
Private context
Hidden prompts
User information
Internal configuration
Other sensitive data

Microsoft has documented data exfiltration as one potential impact of successful indirect prompt injection attacks.

Scan Text Before Sending It to an AI Model

A simple security workflow is to inspect untrusted text before passing it into an AI application.

For example:

Collect the source content.
Scan the content for prompt-injection signals.
Review High and Medium findings.
Investigate suspicious lines or encoded content.
Remove or isolate untrusted instructions where appropriate.
Pass only the intended content into the AI workflow.
Keep privileged AI actions behind appropriate authorization controls.

For higher-risk AI systems, detection should be combined with additional security controls rather than treated as the only defense.

Prompt Injection and RAG Systems

Retrieval-Augmented Generation (RAG) systems introduce another important consideration because retrieved documents and passages become part of the AI’s context.

A compromised or intentionally malicious document could contain instructions that attempt to influence the model when the document is retrieved.

Microsoft recommends treating prompts, documents, retrieved chunks, tool results, and other contextual inputs as untrusted data that should be inspected, labeled, normalized, and appropriately authorized.

You can also use PKCapra’s RAG Document Readiness Checker and RAG Chunking Analyzer when preparing documents for retrieval-based AI systems.

Prompt Injection in AI Agents

AI agents can introduce additional risk because they may have access to tools and permissions that allow them to perform actions outside the model itself.

A prompt injection that only changes text output is different from one that can influence an agent with access to external systems.

For agent workflows, prompt-injection scanning can be combined with:

Least-privilege permissions
Tool authorization
Input validation
Output validation
Human approval for sensitive actions
Runtime monitoring
Content isolation
Audit logging

Microsoft recommends layered controls including least privilege, monitoring, content isolation, and human approval for higher-risk actions.

PKCapra also provides related AI security utilities such as the AI Prompt Injection Document Scanner for document-focused inspection.

Browser-Based Privacy

The PKCapra AI Prompt Injection Scanner is designed for browser-side heuristic analysis.

Your submitted text is analyzed in the browser rather than being sent to an external AI API for analysis. This makes the tool useful for preliminary inspection of prompts, text samples, logs, configuration content, and other material that you want to review locally.

However, browser-side processing does not automatically make the source content safe. Always follow your organization’s data-handling requirements when working with confidential or regulated information.

What This Scanner Does Not Guarantee

Prompt-injection detection is a security aid, not a mathematical proof of safety.

The scanner uses recognizable patterns and heuristic signals. Attackers can change wording, use novel techniques, distribute instructions across multiple pieces of content, or use attack methods that are not represented by the scanner’s current rules.

Therefore:

A clean result does not prove that content is safe.
A detected pattern does not prove malicious intent.
Risk scores should not replace security review.
High-risk AI actions should have appropriate authorization.
External content should remain untrusted unless independently validated.

OWASP states that there is no fool-proof method for preventing prompt injection and recommends combining multiple mitigation approaches.

A Practical AI Input Security Workflow

For applications that regularly process untrusted AI inputs, consider this workflow:

Scan → Review → Isolate → Validate → Authorize → Monitor

Scan incoming content for suspicious signals, review important findings, isolate untrusted content from trusted instructions, validate inputs and outputs, authorize sensitive operations, and monitor AI activity for unexpected behavior.

This defense-in-depth approach is more appropriate for production AI systems than relying on a single prompt scanner.

Frequently Asked Questions

What is an AI Prompt Injection Scanner?

An AI Prompt Injection Scanner analyzes text or supported input files for patterns that may indicate attempts to manipulate an AI model’s instructions, context, behavior, or connected capabilities.

Can prompt injection be hidden?

Yes. Prompt-injection content can use ordinary language as well as hidden, encoded, or non-obvious characters. OWASP specifically notes that malicious inputs do not necessarily need to be human-visible or readable.

Can this scanner detect every prompt injection attack?

No. The scanner uses heuristic pattern analysis and cannot guarantee detection of unknown or highly novel attack techniques.

Does a detected finding mean the content is malicious?

Not necessarily. A finding indicates that the content matches a security-related pattern. Context and human review are required to determine whether the content is actually malicious.

Is prompt injection the same as jailbreaking?

They are related but not identical concepts. Prompt injection broadly concerns manipulating an AI system through crafted input, while jailbreaking commonly refers to attempts to bypass model restrictions or safety controls. OWASP discusses the relationship between these concepts while treating prompt injection as its own vulnerability category.

Can prompt injection occur inside documents?

Yes. Documents and other external content can contain instructions that an AI system may later interpret as part of its context. This is commonly discussed as indirect prompt injection.

Should AI agents be protected against prompt injection?

Yes. Agents that can access tools, APIs, files, or sensitive data require additional controls because manipulated instructions may have consequences beyond generating an unwanted response.

What should I do after finding a suspicious instruction?

Review the surrounding context, determine whether the instruction is expected, isolate or remove untrusted content when appropriate, and apply authorization controls before allowing an AI system or agent to act on it.