Invisible Character Detector & Remover: Clean Text for AI & Code
Detect, highlight, and remove hidden Unicode characters that break AI prompts, corrupt code, and sabotage SEO. Essential for LLM engineers, prompt designers, and developers.
Why Clean Text Matters for Agentic Workers (and You)
Hidden characters break JSON prompts, function calling, and agentic workflows. A single zero-width space can cause API calls to fail silently, corrupt structured data, and introduce unpredictable behavior in LLM interactions.
Prevent prompt injection and evasive maneuvers using hidden characters. Attackers use zero-width spaces and bidirectional marks to bypass filters and inject malicious prompts into your AI systems.
What Are Invisible / Zero-Width Characters?
Zero-Width Space (U+200B)
An invisible character used for word breaking in scripts without spaces. Commonly inserted by copy-paste operations and can break JSON parsing, regex matching, and string comparisons.
Zero-Width Joiner (U+200D) & Non-Joiner (U+200C)
Control characters used in complex scripts like Arabic and Indic languages. They can appear in copied text and cause unexpected behavior in code and AI prompts.
Bidirectional Marks (U+200E, U+200F)
Left-to-right and right-to-left marks control text direction. Malicious actors use these to reverse text order and bypass security filters by hiding code within seemingly normal text.
Byte Order Mark (U+FEFF)
The BOM character appears at the start of UTF-8 files and can break parsing, cause "invisible" syntax errors, and corrupt API requests when text is copied between systems.
Use Cases
Stop Hidden Characters from Breaking Your LLM Workflows
LLM engineers and prompt designers rely on clean text for reliable function calling, structured outputs, and consistent AI behavior. Hidden characters cause silent failures, JSON parsing errors, and unpredictable model responses. Clean your prompts before sending them to ChatGPT, Claude, or any LLM API.
Code Debugging and Security
Developers encounter invisible characters when copying code from web pages, PDFs, or documentation. These characters cause syntax errors that are impossible to see, break regex patterns, and corrupt string comparisons. Security teams use this tool to detect and prevent Unicode-based attacks and prompt injection attempts.
Copy-Pasting and SEO
Content creators and marketers copy text from various sources that often contain hidden formatting characters. These characters can break URLs, corrupt meta descriptions, and negatively impact SEO. Clean your content before publishing to ensure optimal search engine performance.
How Our Detector Ensures AI-Ready Text
Our detector scans for over 50 different non-printable Unicode and control characters, including:
- Zero-width spaces, joiners, and non-joiners
- Bidirectional formatting marks (LRM, RLM, embeddings, overrides)
- Byte Order Mark (BOM)
- Various Unicode space characters (non-breaking, thin, hair, ideographic)
- Em and en dashes that can cause parsing issues
The tool highlights detected characters with color-coded indicators and provides a cleaned version with all hidden characters removed. This ensures your text is ready for AI processing, code execution, and production use.
Frequently Asked Questions
Why do LLMs generate zero-width spaces?
LLMs don't intentionally generate zero-width spaces, but they can appear when text is copied from web pages, PDFs, or other sources that use complex formatting. Some copy-paste operations preserve invisible formatting characters that weren't visible in the original source.
Is the tool truly free?
Yes, the tool is completely free to use. Unauthenticated users get 5 uses per day. Sign in to Agentic Workers for unlimited access to this and other AI tools.
What is the risk of a Unicode attack?
Unicode attacks use hidden characters to bypass security filters, reverse text direction, and inject malicious code. By detecting and removing these characters, you prevent attackers from using bidirectional marks and zero-width spaces to hide malicious content within seemingly normal text.
Can hidden characters break JSON prompts?
Yes. Zero-width spaces and other invisible characters can cause JSON parsing to fail, break function calling schemas, and corrupt structured data. Always clean your prompts before sending them to LLM APIs to ensure reliable parsing and execution.
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