Unicode spoofing detection

Spammers disguise words by swapping letters for identical-looking characters from other alphabets. To a person the text reads normally, but the underlying characters are a mix — so a banned word sails past keyword filters, and some networks (mobile carriers especially) treat the message as deliberate evasion and block it outright.

Соngrаts! You won a FREE сrурtо рrіzе — сlаіm it now

Соngrаts  →  the С, о and а are Cyrillic, not Latin
сrурtо    →  Cyrillic с, у, р, о mixed into "crypto"
рrіzе     →  Cyrillic р, і mixed into "prize"

The Unicode spoofing policy catches this and returns a cleaned‑up version of the message so you can block it — or send the normalized text instead.

Detection is powered by our open‑source @moderation-api/unicode-spoofing package, based on the official Unicode data and refreshed regularly as Unicode publishes updates.

How it works

The policy analyzes a message word by word. Instead of flagging any non‑English character — which would break legitimate multilingual content — it looks for evidence that a character was chosen to disguise itself as something else, by comparing each character against Unicode’s catalog of known lookalikes. Genuine foreign words have no lookalike to hide behind, so they pass through untouched.

Signals

The policy reports five independent signals. Each shows up as a label on the result, and each can be turned on or off individually.

Signal	What it catches	Example
`mixed_script`	One word blends alphabets — reads as English, but some letters are from another script	`busіnеss` — the `і` and `е` are Cyrillic
`confusable_word`	A whole word spelled in lookalikes that reads as a normal word	`НОТ` (all Cyrillic) reads as `HOT`
`invisible`	Zero‑width or hidden characters wedged inside a word to split a keyword	a zero‑width space hidden inside `free`
`zalgo`	Text buried under stacked combining marks	`Z̸a̛l̷g̷o̶` reads as `Zalgo`
`illegal`	Stray control characters that never occur in real text	a hidden control character spliced into the message

Legitimate multilingual content is left alone automatically. A fully Russian, Greek, Japanese, Arabic, or Hebrew message — or a foreign name inside an English sentence — does not flag, because genuine foreign words have no lookalike to disguise as. There’s no language allowlist to maintain.

Enable and configure

Unicode spoofing is configured entirely from the dashboard.

Open the policy

In your channel, go to Policies → Spam and find the Unicode spoofing card.

Enable detection

Toggle Enable detection on.

Choose how to act

Pick Flag to act on detections, or Shadow flag to observe without flagging while you tune. Shadow‑flagged content is visible in queues configured to show it.

Pick which signals enforce (optional)

Under Signals, every signal is on by default. Turn one off to keep detecting and reporting it while stopping it from flagging on its own — useful if, say, you want to see Zalgo without acting on it.

Reading the result

When enabled, the policy appears in the response’s policies array. See Understanding API responses for the full response shape.

flagged

boolean

Whether an enabled signal fired.

probability

number

1 when an enabled signal fired, 0 otherwise. In shadow flag mode this is still 1 while flagged stays false.

labels

array

One entry per signal — { id, probability, flagged }. A label with probability: 1 but flagged: false means that signal was detected but is turned off in your settings.

flagged_fields

string[]

For object content, the field keys where a signal fired. Omitted for plain text.

data

object

Show properties

counts.words_total

number

Total words analyzed.

counts.words_affected

number

How many words carried a signal.

sample_words

string[]

Up to 10 example offending words, for a quick preview in review queues. The complete cleaned‑up text is always available in content.modified.

Two more fields surface outside the policies array — the top‑level evaluation.unicode_spoofed flag and the cleaned‑up content.modified text. Both come from normalization and are set whether or not this policy is enabled. See Normalization below.

Example

curl https://api.moderationapi.com/v1/moderate \
  -H "Authorization: Bearer $MODERATION_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "content": { "type": "text", "text": "Соngrаts! You won a FREE сrурtо рrіzе — сlаіm it now" }
  }'

import ModerationApi from '@moderation-api/sdk';

const moderationApi = new ModerationApi();

const result = await moderationApi.content.submit({
  content: {
    type: 'text',
    text: 'Соngrаts! You won a FREE сrурtо рrіzе — сlаіm it now',
  },
});

Response

{
  "content": {
    "id": "msg_123",
    "masked": false,
    "modified": "Congrats! You won a FREE crypto prize — claim it now"
  },
  "evaluation": {
    "flagged": true,
    "flag_probability": 1,
    "severity_score": 0.34,
    "unicode_spoofed": true
  },
  "recommendation": {
    "action": "reject",
    "reason_codes": ["rule_match"]
  },
  "policies": [
    {
      "id": "unicode_spoofing",
      "type": "classifier",
      "probability": 1,
      "flagged": true,
      "labels": [
        { "id": "mixed_script",    "probability": 1, "flagged": true },
        { "id": "confusable_word", "probability": 0, "flagged": false },
        { "id": "invisible",       "probability": 0, "flagged": false },
        { "id": "zalgo",           "probability": 0, "flagged": false },
        { "id": "illegal",         "probability": 0, "flagged": false }
      ],
      "data": {
        "counts": { "words_total": 10, "words_affected": 4 },
        "sample_words": ["Соngrаts", "сrурtо", "рrіzе", "сlаіm"]
      }
    }
  ]
}

Acting on detections

The policy feeds the rules engine and review queues like any other, so you decide what happens to spoofed content:

Block it. Add a rule that rejects content when Unicode spoofing is flagged. The policy also raises the severity score, so channels using severity‑based recommendations lean toward rejection automatically.
Route signals differently. Each signal is available separately in rules — for example, reject clear lookalike attacks while sending Zalgo to human review.
Send the clean version instead. If your goal is deliverability rather than blocking, forward the cleaned‑up text from content.modified so the message goes out in a form that won’t be blocked.

Normalization

Separately from the policy, your channel has a Unicode spoof detection setting under Content → Text, on by default. It rewrites disguised text before any other policy runs, so the rest of your setup sees the real message instead of the disguise. With it on, a message like Buy сhеар vіаgrа reaches your wordlists and classifiers as Buy cheap viagra — so a banned word wrapped in lookalikes still gets caught by the wordlist that was already looking for it. Normalization rewrites only the words that carry a signal: lookalikes become their real letters, invisible characters are removed, and Zalgo is stripped. Text that isn’t disguised is passed through untouched, byte for byte, so legitimate multilingual content survives normalization intact.

Normalization applies to text. Image, video, audio, and URL content is unaffected. For object content, each field is normalized on its own.

The setting and the policy together

They’re independent, and each is useful without the other:

Setting	Policy	What you get
On	Off	Disguised text is silently normalized so your other policies catch what’s underneath. `unicode_spoofed` tells you it happened.
On	On	Same, plus spoofing becomes its own flaggable, routable signal.
Off	On	Spoofing is detected and reported, but the text your other policies see is left as the sender wrote it.
Off	Off	No detection. `unicode_spoofed` is omitted from the response.

The common setup is both on: keep the setting on so spoofing can’t hide banned words from your other policies, and enable the policy to act on the spoofing itself.

Because normalization runs first, the policy’s own detection is unaffected by it — enabling one never hides results from the other.

In the response

Normalization surfaces in two fields, whether or not the policy is enabled:

evaluation.unicode_spoofed

boolean

true when spoofing was detected anywhere in the message. Omitted entirely when both the channel setting and the policy are off, since nothing analyzed the text.

content.modified

string | object

The normalized message, present whenever normalization changed the text. If masking is also enabled, this field carries the masked and normalized result.

Forward content.modified when your goal is deliverability rather than blocking — it’s the same message in a form that won’t trip carrier filters.

FAQ

Will this flag legitimate non-English messages?

No. A message written entirely in another language, or an English message that contains a genuine foreign word or name, is not flagged — real foreign letters have no lookalike to impersonate. Detection keys on disguise (a character pretending to be a different one), not on the mere presence of another alphabet.

Does it add usage or latency?

No extra usage — Unicode spoofing detection is included, with no additional model call, and adds negligible latency to a moderation request.

Which alphabets and characters are covered?

All of them. Detection is based on the full Unicode lookalike data, so Cyrillic, Greek, Cherokee, Armenian, full‑width and stylized letterforms, and more are all covered.

Is the detection kept up to date?

Yes. It’s based on the official Unicode data and refreshed regularly as Unicode publishes updates, so newly introduced lookalike characters are covered automatically — nothing changes on your side.

Documentation

Learn

Resources

Unicode spoofing detection

How it works

Signals

Enable and configure

Reading the result

Example

Acting on detections

Normalization

The setting and the policy together

In the response

FAQ

​How it works

​Signals

​Enable and configure

​Reading the result

​Example

​Acting on detections

​Normalization

​The setting and the policy together

​In the response

​FAQ

How it works

Signals

Enable and configure

Reading the result

Example

Acting on detections

Normalization

The setting and the policy together

In the response

FAQ