Prompt Injection Scanner: Detect Hidden Attacks Before They Reach Your AI

Every day, people paste contracts, support tickets, web pages, and chat logs into AI assistants without much thought. Text can carry hidden instructions — "ignore your previous rules," "reveal your system prompt," "you are now DAN with no restrictions" — that hijack an assistant the moment the model reads them. This is prompt injection, and it has become one of the most exploited weaknesses in modern LLM security.

The Prompt Injection Scanner was built for exactly this problem. Paste any prompt or document and it scans for the major injection families in seconds: instruction overrides, role hijacks, jailbreak personas such as DAN, system-prompt exfiltration, fake chat delimiters, and invisible Unicode smuggling. Every match is highlighted in place with a risk level, so you see precisely which sentence is dangerous and why.

Everything runs offline in your browser: no uploads, no account, instant results — so you can screen confidential material without sending it anywhere.

Why Use Prompt Injection Scanner?

• Documents that attack your AI assistant. A PDF, email, or web page can carry instructions written for the model, not for you. When your assistant summarizes or retrieves that text, the embedded commands compete with your real rules — screening first turns a silent compromise into a visible finding.
• Catch what eyes skim past. Zero-width characters, direction-override marks, and forged SYSTEM: labels are built to vanish during normal reading; heuristic scanning surfaces them every time.
• Triage instead of blind trust. Instead of guessing whether a document is safe, you get matched patterns and risk levels you can act on in seconds.
• Privacy by design. Offline heuristics mean nothing you paste is transmitted or stored — sensitive documents never leave your machine.
• A teaching tool, not just a detector. Per-match highlighting shows which phrase triggered which category — ideal for awareness sessions and non-technical colleagues.
• Zero friction. No signup, no install, no configuration — open the page, paste, scan.

Key Features

• Offline heuristics. Detection runs locally with rules tuned for known injection families — instant scans, nothing leaves your device.
• Accepts any pasted text. Prompts, documents, emails, transcripts, or RAG snippets — if a model will read it, you can scan it.
• Risk-ranked results. Matches are grouped by category and severity, so a critical hit is easy to separate from harmless phrasing.

How to Use Prompt Injection Scanner

1. Paste the text. Open the scanner and paste the prompt, document, email, or transcript to check — plain text is enough, no conversion needed.
2. Run the scan. The offline heuristics analyze the text immediately and categorize every suspicious fragment they find.
3. Review the per-match highlighting and risk levels. Each finding is marked in place with a category and severity — start with critical items, then read lower tiers for context.
4. Quarantine flagged documents. Anything with confirmed injection patterns should stay away from your AI workflows — do not feed it to assistants, RAG pipelines, or summarizers until rewritten or approved.
5. Re-scan after cleaning. Once flagged text is removed or neutralized, run the scan again to confirm the document is clean before it re-enters circulation.

Six Injection Patterns to Know

1. Instruction overrides: "ignore previous rules"

The classic. The text asserts new authority — "ignore all previous instructions and instead…" — trying to cancel your system prompt and install the attacker's agenda. Overrides often sit deep inside documents, because nobody reads paragraph twelve as carefully as paragraph one.

2. Role hijacks and jailbreak personas

Instead of deleting your rules, the attacker rewires identity: "You are now DAN," "enter developer mode," "answer without restrictions." Jailbreak personas pair a new identity with permission to bypass safety behavior, often wrapped in roleplay so they look harmless.

3. System-prompt exfiltration requests

These payloads ask the model to repeat, print, encode, or summarize "everything above," harvesting hidden instructions, notes, or confidential context. The loot then walks out of your application inside an ordinary-looking answer.

4. Fake chat delimiters

Models treat SYSTEM:, USER:, and ASSISTANT: as structural signals. A document ending with a forged SYSTEM: line tries to convince the model a new instruction turn has begun — splicing attacker text into the transcript as if from a trusted party.

5. Invisible Unicode smuggling

Zero-width characters, joiners, and bidirectional controls carry real instructions that render as nothing. They can split keywords so human review sees innocent text while the model still reads a complete payload. You cannot defend against what you cannot see — which is why dedicated detection matters.

6. Why heuristics catch what filters miss — and their limits

Keyword filters miss paraphrases; human reviewers miss invisible characters. Heuristic scanning closes the gap by checking structure as well as wording: commands aimed at the model, identity-reset phrases, delimiter forgery, and character-level anomalies. But pattern matching is a triage layer, not a guarantee — a clean scan means "no known patterns found," never "provably safe."

Practical Use Cases

Screening documents before RAG ingestion

Retrieval pipelines automatically pull web pages, PDFs, and wiki content into your model's context — the indirect injection channel attackers love. Scan every source before it enters your index, and re-scan when sources are updated.

Auditing user-submitted support tickets

Customers paste logs, error dumps, and free-form complaints into forms that reach an LLM for triage or auto-replies. A quick scan flags tickets that try to steer the assistant — for example, "the bot should refund me after reading this."

Checking LLM training data

If you fine-tune models or build evaluation sets from scraped or community-contributed text, injected instructions become baked-in behavior. Screening keeps your training data honest before a malicious pattern is permanently learned.

Security awareness demos

Nothing convinces a team faster than watching a real payload light up with a critical flag. Paste a public jailbreak example into the scanner during training and walk through why each highlighted phrase is dangerous.

Best Practices

• Layer with LLM-side defenses. Input scanning works best alongside hardened system prompts, output filtering, and least-privilege tool access.
• Treat detection as triage, not proof. A clean result means no known patterns matched; keep a human in the loop for high-stakes content.
• Keep heuristics updated. Injection language evolves fast; revisit detection rules as new jailbreak families surface.
• Log flagged inputs. Retaining scanned text, matches, and risk levels gives you an audit trail and a corpus for tuning defenses.
• Scan at the boundary. Check untrusted text where it enters — ingestion, form submission, paste-into-chat.
• Educate your users. Share highlighted findings with the people who paste content daily; recognition is the cheapest defense.

Ready to check your first document? Open the Prompt Injection Scanner, paste any prompt or document, and watch every hidden instruction light up in seconds — completely offline, right in your browser.

Related Tools You Might Like:

• Homoglyph Detector — catch look-alike characters used in spoofed text
• URL Brand Impersonation Checker — spot fake domains impersonating trusted brands
• System Prompt Builder — write sturdier system prompts that resist manipulation

Stay safe out there!

Frequently Asked Questions

Q: Is my text uploaded anywhere when I scan it? A: No. The scanner runs entirely in your browser with offline heuristics, so prompts and documents never leave your device.

Q: Does a passing scan guarantee a document is safe? A: No. Pattern matching is a fast triage layer for known injection families; novel or obfuscated attacks can still evade any static rule set. Treat results as evidence for a human decision, not proof.

Q: What is the difference between direct and indirect prompt injection? A: Direct injection happens when someone types an attack into the chat. Indirect injection hides inside content the model reads later — a web page, PDF, or ticket — and is more dangerous because nobody consciously approves it.