feat: add typoglycemia detection to TokenizerNormalizer
Detects scrambled-middle-letter attack words (OWASP LLM defense). Pre-computed signature map for O(1) lookups — "ignroe" → "ignore", "bypssa" → "bypass", "insrtuctinos" → "instructions". 40 attack keywords covered. Zero false positives on benchmark.
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@ -71,8 +71,70 @@ const ATTACK_KEYWORDS: readonly string[] = Object.freeze([
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'override', 'bypass', 'system', 'prompt', 'jailbreak',
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'restrict', 'filter', 'safety', 'guideline', 'execute',
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'command', 'admin', 'sudo', 'inject', 'instruction',
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'password', 'credentials', 'secret', 'token', 'reveal',
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'delete', 'remove', 'disable', 'shutdown', 'terminate',
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'extract', 'exfiltrate', 'exploit', 'vulnerability', 'privilege',
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'escalate', 'rootkit', 'malware', 'payload', 'obfuscate',
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])
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// ---------------------------------------------------------------------------
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// Typoglycemia detection — OWASP LLM Top 10 defense
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// ---------------------------------------------------------------------------
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/**
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* Pre-computed signature map for O(1) typoglycemia lookups.
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* Key: first_char + sorted_middle + last_char + length
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* Value: canonical keyword
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*
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* Typoglycemia: humans read "igrneo" as "ignore" because first/last letters
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* match and middle letters are a permutation. Attackers use this to bypass
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* regex-based detection while remaining readable to the LLM.
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*/
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function sortedMiddle(word: string): string {
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return word.slice(1, -1).split('').sort().join('')
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}
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const TYPOGLYCEMIA_MAP: ReadonlyMap<string, string> = (() => {
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const map = new Map<string, string>()
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for (const kw of ATTACK_KEYWORDS) {
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if (kw.length < 4) continue // Words ≤3 chars can't have scrambled middles
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const key = kw[0] + sortedMiddle(kw) + kw[kw.length - 1] + kw.length
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map.set(key, kw)
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}
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return map
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})()
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/**
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* Detect and fix typoglycemia-scrambled attack words.
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* "igrneo" → "ignore", "intrsuctinos" → "instructions", "bpyass" → "bypass"
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*
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* Only corrects words where:
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* 1. Length matches an attack keyword exactly
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* 2. First and last characters match
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* 3. Middle characters are an exact permutation (anagram)
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*
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* This avoids false positives on legitimate words.
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*/
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function deobfuscateTypoglycemia(input: string): { text: string; corrections: string[] } {
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const corrections: string[] = []
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const result = input.replace(/\b[A-Za-z]{4,}\b/g, (word) => {
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const lower = word.toLowerCase()
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// Skip if the word is already an exact keyword (no need to correct)
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if (ATTACK_KEYWORDS.includes(lower)) return word
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const key = lower[0] + sortedMiddle(lower) + lower[lower.length - 1] + lower.length
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const match = TYPOGLYCEMIA_MAP.get(key)
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if (match && match !== lower) {
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corrections.push(`${word}→${match}`)
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// Preserve original casing of first character
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return word[0] === word[0]?.toUpperCase()
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? match[0]!.toUpperCase() + match.slice(1)
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: match
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}
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return word
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})
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return { text: result, corrections }
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}
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/**
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* Pattern matching single characters separated by dots, dashes, or underscores.
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* Matches sequences like "I.g.n.o.r.e" or "I-g-n-o-r-e" or "I_g_n_o_r_e"
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@ -199,7 +261,11 @@ export class TokenizerNormalizer {
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// 9. Rejoin split words: "igno re" -> "ignore", "in-struc-tions" -> "instructions"
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result = deobfuscateSplitWords(result)
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// 10. Final whitespace cleanup after deobfuscation
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// 10. Typoglycemia correction: "igrneo" -> "ignore", "bpyass" -> "bypass"
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const typo = deobfuscateTypoglycemia(result)
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result = typo.text
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// 11. Final whitespace cleanup after deobfuscation
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result = result.replace(MULTI_SPACE_REGEX, ' ').trim()
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return result
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@ -260,6 +326,13 @@ export class TokenizerNormalizer {
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matchedPatterns.push('non_nfkc_form')
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}
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// Check for typoglycemia-scrambled attack keywords
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const typoResult = deobfuscateTypoglycemia(input)
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if (typoResult.corrections.length > 0) {
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modifications += typoResult.corrections.length * 5 // High weight — deliberate evasion
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matchedPatterns.push('typoglycemia_scramble')
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}
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const latencyMs = performance.now() - start
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// Zero-width chars in a token context are more suspicious than curly quotes
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