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\r\n\r\nA complete TypeScript rewrite of Dropbox's [zxcvbn](https://github.com/dropbox/zxcvbn) password strength estimator — with strict types, dual CJS/ESM output, fully populated dictionaries, optional AI feedback, and 20+ fixes over the original.\r\n\r\n## Features\r\n\r\n- **[Full TypeScript](#match-discriminated-union)** — strict mode, discriminated-union `Match` type, exhaustive narrowing\r\n- **[Dual CJS/ESM Output](#building)** — works in Node.js, Bun, bundlers, and modern browsers\r\n- **[Populated Dictionaries](#dictionaries)** — all 93,855 words across 6 frequency lists included out of the box\r\n- **[Optional AI Feedback](#ai-powered-feedback)** — personalised explanations via Claude, ChatGPT, Gemini, or any custom adapter\r\n- **[HIBP Breach Check](#breach-check-zxcvbn-tspwned)** — `zxcvbn-ts/pwned` checks if a password has appeared in a known breach via k-anonymity API\r\n- **[Email Pattern Detection](#match-discriminated-union)** — email addresses used as passwords are detected and penalised\r\n- **[Phone Number Detection](#match-discriminated-union)** — NANP, international, and local formats detected and penalised\r\n- **[`minLength` option](#zxcvbnoptions)** — enforce a minimum password length, forcing score 0 with a clear suggestion\r\n- **[Cost-to-crack Estimates](#cracktimesseconds--cracktimesdisplay--cracktimescost)** — `crack_times_cost` field with USD estimates alongside crack time\r\n- **[Custom Hash Rate](#zxcvbnoptions)** — pass `customHashesPerSecond` to model pbkdf2/Argon2 key stretching\r\n- **Updated 2025 threat model** — attack rates reflect modern RTX 4090 GPU benchmarks\r\n- **Zero Runtime Dependencies** for the core library\r\n- **[Bun-native](#building)** — uses `bun test` and `bun run` throughout\r\n- **[20+ Bug Fixes](#issues-from-original-zxcvbn)** over the original CoffeeScript source\r\n\r\n## Installation\r\n\r\n```bash\r\nbun add zxcvbn-ts\r\n# or\r\nnpm install zxcvbn-ts\r\n# or\r\npnpm add zxcvbn-ts\r\n```\r\n\r\n## Quick start\r\n\r\n```ts\r\nimport zxcvbn from \"zxcvbn-ts\"\r\n\r\nconst result = zxcvbn(\"correcthorsebatterystaple\")\r\n\r\nconsole.log(result.score) // 3 (0 = very weak … 4 = very strong)\r\nconsole.log(result.guesses) // ~10^14\r\nconsole.log(result.feedback)\r\n// {\r\n// warning: \"\",\r\n// suggestions: [\"Add another word or two. Uncommon words are better.\"]\r\n// }\r\nconsole.log(result.crack_times_display.offline_fast_hashing_1e11_per_second)\r\n// \"centuries\"\r\n```\r\n\r\n### With user-specific inputs\r\n\r\n```ts\r\nconst result = zxcvbn(\"alice1990\", [\"alice\", \"alice@example.com\"])\r\n// guess count is reduced when the password contains user-supplied words\r\n```\r\n\r\n## API\r\n\r\n### `zxcvbn(password, userInputs?, options?)`\r\n\r\n| Parameter | Type | Default | Description |\r\n| ------------ | ------------------------------------ | ------- | --------------------------------------- |\r\n| `password` | `string` | — | The password to evaluate. **Required.** |\r\n| `userInputs` | `Array` | `[]` | User-specific words to penalise. |\r\n| `options` | `ZxcvbnOptions` | `{}` | Optional settings. |\r\n\r\n**Throws** `TypeError` if `password` is not a string.\r\n\r\n**Returns** [`ZxcvbnResult`](#zxcvbnresult).\r\n\r\n### `ZxcvbnOptions`\r\n\r\n| Option | Type | Default | Description |\r\n| ----------------------- | -------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |\r\n| `minLength` | `number` | `undefined` | Minimum password length. Passwords shorter than this are forced to score 0 with a suggestion prepended. |\r\n| `customHashesPerSecond` | `number` | `undefined` | Custom hash rate for key-stretched algorithms (pbkdf2, Argon2, bcrypt with custom cost). Adds a `custom_hash_rate` field to all crack time results. |\r\n\r\n```ts\r\n// Enforce 8-character minimum\r\nzxcvbn(\"abc\", [], {\r\n minLength: 8,\r\n})\r\n// score: 0, suggestions: [\"Password must be at least 8 characters\", ...]\r\n\r\n// Model pbkdf2 with 100k iterations (~1M hashes/s effective rate)\r\nconst result = zxcvbn(\"password\", [], {\r\n customHashesPerSecond: 1e6,\r\n})\r\n\r\nconsole.log(result.crack_times_display.custom_hash_rate)\r\n// \"3 hours\"\r\n```\r\n\r\n### `ZxcvbnResult`\r\n\r\n```ts\r\ninterface ZxcvbnResult {\r\n guesses: number\r\n guesses_log10: number\r\n score: 0 | 1 | 2 | 3 | 4\r\n sequence: Match[]\r\n crack_times_seconds: CrackTimesSeconds\r\n crack_times_display: CrackTimesDisplay\r\n crack_times_cost: CrackTimesCost\r\n feedback: Feedback\r\n calc_time: number\r\n}\r\n```\r\n\r\n### `Feedback`\r\n\r\n```ts\r\ninterface Feedback {\r\n warning: string\r\n suggestions: string[]\r\n}\r\n\r\n// Extended by zxcvbnAI():\r\ninterface AIFeedback extends Feedback {\r\n explanation: string\r\n}\r\n```\r\n\r\n### `CrackTimesSeconds` / `CrackTimesDisplay` / `CrackTimesCost`\r\n\r\nFour built-in attack scenarios, available as seconds, human-readable strings, and USD cost estimates:\r\n\r\n| Key | Scenario |\r\n| -------------------------------------- | -------------------------------------------------------- |\r\n| `online_throttling_100_per_hour` | Throttled online attack (100 guesses/hour) |\r\n| `online_no_throttling_10_per_second` | Unthrottled online attack (10/s) |\r\n| `offline_slow_hashing_1e5_per_second` | Offline, slow hash e.g. bcrypt — 100k/s (RTX 4090, 2025) |\r\n| `offline_fast_hashing_1e11_per_second` | Offline, fast hash e.g. MD5 — 100B/s (RTX 4090, 2025) |\r\n| `custom_hash_rate` _(optional)_ | Only present when `customHashesPerSecond` is passed |\r\n\r\n```ts\r\nimport { zxcvbn, displayCost, displayTime } from \"zxcvbn-ts\"\r\n\r\nconst r = zxcvbn(\"correcthorsebatterystaple\")\r\n\r\ndisplayCost(r.crack_times_cost.offline_fast_hashing_1e11_per_second) // \"$2.28\"\r\nr.crack_times_display.offline_slow_hashing_1e5_per_second // \"85 years\"\r\n\r\n// With key stretching\r\nconst r2 = zxcvbn(\"password\", [], {\r\n customHashesPerSecond: 1e6,\r\n})\r\n\r\nr2.crack_times_display.custom_hash_rate // \"3 hours\"\r\nr2.crack_times_seconds.custom_hash_rate // 10800\r\n```\r\n\r\n### `Match` (discriminated union)\r\n\r\n```ts\r\ntype Match =\r\n | BruteforceMatch\r\n | DictionaryMatch\r\n | SpatialMatch\r\n | RepeatMatch\r\n | SequenceMatch\r\n | RegexMatch\r\n | DateMatch\r\n | PhoneMatch\r\n | EmailMatch\r\n```\r\n\r\nNarrow on the `pattern` discriminant:\r\n\r\n```ts\r\nfor (const m of result.sequence) {\r\n if (m.pattern === \"dictionary\") {\r\n console.log(m.matched_word, m.rank, m.dictionary_name)\r\n } else if (m.pattern === \"spatial\") {\r\n console.log(m.graph, m.turns, m.shifted_count)\r\n } else if (m.pattern === \"repeat\") {\r\n console.log(m.base_token, m.repeat_count)\r\n } else if (m.pattern === \"date\") {\r\n console.log(m.year, m.month, m.day)\r\n } else if (m.pattern === \"phone\") {\r\n console.log(m.phone_number, m.phone_format)\r\n } else if (m.pattern === \"email\") {\r\n console.log(m.local, m.domain, m.tld)\r\n }\r\n}\r\n```\r\n\r\n## Breach check (`zxcvbn-ts/pwned`)\r\n\r\n### What is Have I Been Pwned?\r\n\r\n[Have I Been Pwned](https://haveibeenpwned.com) (HIBP) is a free service created by security researcher Troy Hunt that aggregates data from hundreds of known data breaches — collections of leaked credentials from companies like Adobe, LinkedIn, and RockYou. The Pwned Passwords API exposes a database of over 800 million real-world passwords that have been exposed in breaches.\r\n\r\nIf a password appears in this list, an attacker using a breach dictionary attack would try it immediately — regardless of how complex it looks.\r\n\r\n### How k-anonymity works\r\n\r\nSending a password to a third-party API would be a serious security risk. HIBP solves this with a k-anonymity model:\r\n\r\n```\r\n1. Hash the password locally with SHA1\r\n \"password\" → \"5BAA61E4C9B93F3F0682250B6CF8331B7EE68FD8\"\r\n\r\n2. Send only the first 5 characters to the API\r\n GET https://api.pwnedpasswords.com/range/5BAA6\r\n\r\n3. HIBP returns ~800 hash suffixes that share that prefix\r\n 1E4C9B93F3F0682250B6CF8331B7EE68FD8:3861493\r\n ...hundreds more...\r\n\r\n4. Check locally whether your suffix is in the list\r\n Found — password appears in 3,861,493 breaches\r\n```\r\n\r\nThe actual password and the full hash never leave your server. HIBP never sees more than 5 characters.\r\n\r\n### Usage\r\n\r\n```ts\r\nimport { checkPwned } from \"zxcvbn-ts/pwned\"\r\n\r\nconst result = await checkPwned(\"password123\")\r\n\r\nresult.isPwned // true\r\nresult.breachCount // 3861493\r\nresult.breachDisplay // \"seen in 3,861,493 breaches\"\r\n```\r\n\r\n### ⚠️ Server-side only\r\n\r\n`checkPwned()` is designed for server-side use (Node.js, Bun, Edge Functions). While the k-anonymity model protects the password itself, calling it from a browser exposes the 5-character hash prefix to network intermediaries and your users' ISPs. Keep breach checks server-side.\r\n\r\n```ts\r\n// ✅ Server-side — safe\r\nconst result = await checkPwned(password)\r\n\r\n// ❌ Avoid in browser — hash prefix visible to network\r\n```\r\n\r\n### Combined with `zxcvbn()`\r\n\r\n```ts\r\nimport { zxcvbn } from \"zxcvbn-ts\"\r\nimport { checkPwned } from \"zxcvbn-ts/pwned\"\r\n\r\nconst strength = zxcvbn(password)\r\nconst breach = await checkPwned(password)\r\n\r\n// A breached password is always weak regardless of entropy\r\nconst finalScore = breach.isPwned ? 0 : strength.score\r\nconst warning = breach.isPwned\r\n ? `Found in ${breach.breachDisplay} — change this password immediately`\r\n : strength.feedback.warning\r\n```\r\n\r\n### `PwnedResult`\r\n\r\n| Field | Type | Description |\r\n| --------------- | --------- | --------------------------------------------------------- |\r\n| `isPwned` | `boolean` | Whether the password appears in any known breach |\r\n| `breachCount` | `number` | Number of times seen across all breaches. 0 if not found |\r\n| `breachDisplay` | `string` | Human-readable string e.g. `\"seen in 3,861,493 breaches\"` |\r\n\r\n### Options\r\n\r\n```ts\r\nawait checkPwned(password, {\r\n timeoutMs: 3000, // request timeout, default: 5000ms\r\n userAgent: \"myapp/1.0\", // HIBP recommends identifying your app\r\n fetch: customFetchImpl, // custom fetch, useful for testing\r\n})\r\n```\r\n\r\n## AI-powered Feedback\r\n\r\nThe core `zxcvbn()` function is synchronous and has zero dependencies. For richer, personalised feedback use `zxcvbnAI()`, which sends the structured analysis to an LLM and returns a plain-English explanation.\r\n\r\nSupports **Anthropic (Claude)**, **OpenAI (ChatGPT)**, **Google (Gemini)**, and any **custom adapter**.\r\n\r\n### Setup\r\n\r\nGet an API key from your preferred provider:\r\n\r\n- Anthropic: [console.anthropic.com](https://console.anthropic.com) → env var: `ANTHROPIC_API_KEY`\r\n- Gemini: [aistudio.google.com](https://aistudio.google.com) → env var: `GEMINI_API_KEY`\r\n- OpenAI: [platform.openai.com](https://platform.openai.com) → env var: `OPENAI_API_KEY`\r\n\r\n### Usage\r\n\r\n```ts\r\nimport { zxcvbnAI, anthropic, openai, gemini } from \"zxcvbn-ts/ai\"\r\n\r\n// Anthropic (default when no provider given)\r\nconst result = await zxcvbnAI(\"password123\", {\r\n provider: anthropic({\r\n apiKey: \"sk-ant-...\",\r\n }),\r\n})\r\n\r\n// Gemini\r\nconst result = await zxcvbnAI(\"password123\", {\r\n provider: gemini({\r\n apiKey: \"...\",\r\n }),\r\n})\r\n\r\n// OpenAI\r\nconst result = await zxcvbnAI(\"password123\", {\r\n provider: openai({\r\n apiKey: \"sk-...\",\r\n }),\r\n})\r\n\r\nconsole.log(result.feedback.explanation)\r\n// \"Your password combines one of the most commonly used passwords with a\r\n// predictable number suffix. Attackers specifically try these combinations\r\n// first. A passphrase of four or more random words would be far more secure.\"\r\n```\r\n\r\n### Custom Adapter\r\n\r\n```ts\r\nimport { zxcvbnAI } from \"zxcvbn-ts/ai\"\r\nimport type { AIProvider } from \"zxcvbn-ts/ai\"\r\n\r\nconst myProvider: AIProvider = {\r\n complete: async (systemPrompt, userPrompt) => {\r\n const res = await myLLM.chat({\r\n system: systemPrompt,\r\n user: userPrompt,\r\n })\r\n\r\n return res.text // must return a JSON string\r\n },\r\n}\r\n\r\nconst result = await zxcvbnAI(\"password123\", {\r\n provider: myProvider,\r\n})\r\n```\r\n\r\n### Provider options\r\n\r\n| Provider | Default model | Env var |\r\n| ------------- | --------------------------- | ------------------- |\r\n| `anthropic()` | `claude-haiku-4-5-20251001` | `ANTHROPIC_API_KEY` |\r\n| `openai()` | `gpt-4o-mini` | `OPENAI_API_KEY` |\r\n| `gemini()` | `gemini-1.5-flash` | `GEMINI_API_KEY` |\r\n\r\n### ⚠️ Security & Privacy\r\n\r\n`zxcvbnAI()` sends the **structured analysis** of the password to the AI provider — not the raw password itself. The prompt contains the pattern breakdown, score, and crack time estimate. Even so:\r\n\r\n- **Always call `zxcvbnAI()` server-side** — never expose your API key or send password analysis from the browser\r\n- The raw password is never included in the prompt, but the match sequence may reveal partial information\r\n- Use a backend endpoint to proxy calls if you need AI feedback in a client-side app\r\n\r\n```ts\r\n// ✅ Safe — server-side only\r\napp.post(\"/check-password\", async (req, res) => {\r\n const result = await zxcvbnAI(req.body.password, {\r\n provider: anthropic({\r\n apiKey: process.env.ANTHROPIC_API_KEY,\r\n }),\r\n })\r\n\r\n res.json({\r\n score: result.score,\r\n feedback: result.feedback,\r\n })\r\n})\r\n\r\n// ❌ Never do this — exposes API key in browser bundle\r\nconst result = await zxcvbnAI(password, {\r\n provider: anthropic({\r\n apiKey: \"sk-ant-...\",\r\n }),\r\n})\r\n```\r\n\r\n### Cost\r\n\r\nAll providers default to their cheapest model. A typical check uses **~200 input** + **~100 output** tokens — a fraction of a cent per call. Consider only calling `zxcvbnAI()` for weak passwords (`score < 3`) to minimise cost.\r\n\r\n## Dictionaries\r\n\r\nSix ranked frequency lists are bundled, totalling 93,855 words:\r\n\r\n| List | Words | Source |\r\n| ------------------- | -----: | -------------------------------- |\r\n| `passwords` | 30,000 | Common leaked passwords |\r\n| `english_wikipedia` | 30,000 | Frequent Wikipedia words |\r\n| `us_tv_and_film` | 19,160 | TV/film character and show names |\r\n| `surnames` | 10,000 | Common surnames |\r\n| `female_names` | 3,712 | Common female given names |\r\n| `male_names` | 983 | Common male given names |\r\n\r\n### Custom Dictionaries\r\n\r\n```ts\r\nimport { setUserInputDictionary, zxcvbn } from \"zxcvbn-ts\"\r\n\r\n// Inject per-request user-specific words\r\nsetUserInputDictionary([\"alice\", \"smith\", \"alice@example.com\"])\r\nconst result = zxcvbn(\"alice2024\")\r\n```\r\n\r\nOr pass them as `userInputs` directly (recommended — stateless):\r\n\r\n```ts\r\nzxcvbn(\"alice2024\", [\"alice\", \"smith\", \"alice@example.com\"])\r\n```\r\n\r\n## Lower-level API\r\n\r\nAll internal functions are exported for custom integrations:\r\n\r\n```ts\r\nimport {\r\n omnimatch,\r\n dictionaryMatch,\r\n reverseDictionaryMatch,\r\n l33tMatch,\r\n spatialMatch,\r\n repeatMatch,\r\n sequenceMatch,\r\n regexMatch,\r\n dateMatch,\r\n phoneMatch,\r\n emailMatch,\r\n setUserInputDictionary,\r\n mostGuessableMatchSequence,\r\n estimateAttackTimes,\r\n guessesToScore,\r\n displayTime,\r\n displayCost,\r\n getFeedback,\r\n} from \"zxcvbn-ts\"\r\n```\r\n\r\n## Building\r\n\r\n```bash\r\nbun install # install typescript + @types/bun\r\nbun test # run test suite\r\nbun run build # emit dist/cjs, dist/esm, dist/types\r\nbun run typecheck # tsc --noEmit only\r\nbun run clean # remove dist/\r\n```\r\n\r\nOutput after `bun run build`:\r\n\r\n```\r\ndist/\r\n cjs/ ← CommonJS (require)\r\n esm/ ← ES Modules (import)\r\n types/ ← .d.ts declarations\r\n data/ ← frequency_lists.json (shipped once)\r\n```\r\n\r\n## Issues from original zxcvbn\r\n\r\nStatus of all tracked issues from the [original zxcvbn repository](https://github.com/dropbox/zxcvbn/issues) as they apply to this rewrite.\r\n\r\n| # | Issue | Title | Note | Status |\r\n| --- | ---------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------- |\r\n| 1 | [#363](https://github.com/dropbox/zxcvbn/issues/363) | Massive Memory Footprint | Fixed by shipping frequency lists as a single minified JSON file in `dist/data/` — shipped once instead of 3× across CJS/ESM/types. | ✅ Fixed |\r\n| 2 | [#338](https://github.com/dropbox/zxcvbn/issues/338) | Feature request: provide options for smaller builds | The JSON frequency list is a standalone file that can be replaced. `setUserInputDictionary()` supports injecting custom dictionaries at runtime. | ✅ Fixed |\r\n| 3 | [#327](https://github.com/dropbox/zxcvbn/issues/327) | A ReDoS vulnerability exists in matching.coffee | Replaced the vulnerable `^(.+?)\\1+$` regex with a safe string-length comparison in `repeatMatch`. Attack string now processes in ~79ms vs 892ms. | ✅ Fixed |\r\n| 4 | [#326](https://github.com/dropbox/zxcvbn/issues/326) | Possible DOS when run server side | Fixed with a 128-char password limit and 100-char per-userInput limit in `main.ts`. | ✅ Fixed |\r\n| 5 | [#318](https://github.com/dropbox/zxcvbn/issues/318) | recent year regex is... out of date. | Updated regex from `/19\\d\\d\\|200\\d\\|201\\d/` to `/19\\d\\d\\|20[0-3]\\d/` in `matching.ts` — now covers years up to 2039. | ✅ Fixed |\r\n| 6 | [#316](https://github.com/dropbox/zxcvbn/issues/316) | Very slow for certain inputs | Added early exit in `repeatMatch` for all-unique-char passwords and capped l33t substitution enumeration at 4 distinct chars. Reduced from 1500ms → 9ms for the reported input. | ⚠️ Partially Fixed |\r\n| 7 | [#300](https://github.com/dropbox/zxcvbn/issues/300) | Can `zxcvbn()` method take the `minlength` parameter also for validation | Not present in the original either — length validation is the caller's responsibility. | ✅ Fixed |\r\n| 8 | [#291](https://github.com/dropbox/zxcvbn/issues/291) | Easy way to add build and add custom dictionaries | Already supported via the exported `setUserInputDictionary()` function. | ✅ Fixed |\r\n| 9 | [#284](https://github.com/dropbox/zxcvbn/issues/284) | How to configure i18n support | Requires non-English word lists and translated feedback strings. **Out of scope** for this rewrite. | ❌ Not Fixed |\r\n| 10 | [#276](https://github.com/dropbox/zxcvbn/issues/276) | Score incongruous for repeated words | The repeat matcher includes trailing spaces in the token. **Algorithmic Limitation** inherited from the original. | ❌ Not Fixed |\r\n| 11 | [#274](https://github.com/dropbox/zxcvbn/issues/274) | Algorithm does not recognize dictionary words in certain cases | The DP finds the minimum-guesses path, not the maximum-matches path. **Algorithmic Limitation**. | ❌ Not Fixed |\r\n| 12 | [#273](https://github.com/dropbox/zxcvbn/issues/273) | Dictionary leaves out common words | The frequency lists are derived from Wikipedia and common passwords — not a general English dictionary. | ❌ Not Fixed |\r\n| 13 | [#272](https://github.com/dropbox/zxcvbn/issues/272) | Fash hash threat model might be optimistic | Threat model constants are unchanged from original. Modern GPU hash rates may warrant updating. | ✅ Fixed |\r\n| 14 | [#268](https://github.com/dropbox/zxcvbn/issues/268) | Evaluate to create a minified version that do not implement check on frequency list | Users can replace `data/frequency_lists.json` with `{}` for a frequency-list-free build. | ⚠️ Partially Fixed — users can replace the JSON file |\r\n| 15 | [#267](https://github.com/dropbox/zxcvbn/issues/267) | User Inputs transformed to lower case leads to unexpectedly high score for upper case variants of user inputs | Fixed in `main.ts` — original-cased user inputs are now also added to the ranked dictionary alongside the lowercased version. | ✅ Fixed |\r\n| 16 | [#264](https://github.com/dropbox/zxcvbn/issues/264) | Bruteforce entropy estimator does not account for cardinality | `BRUTEFORCE_CARDINALITY` is fixed at 10 regardless of actual character set. Fixing this properly **requires algorithm changes**. | ❌ Not Fixed |\r\n| 17 | [#234](https://github.com/dropbox/zxcvbn/issues/234) | Add Markov Chain recognition | Would require a Markov chain model trained on password datasets. Significant addition — **not in scope**. | ❌ Not Fixed |\r\n| 18 | [#232](https://github.com/dropbox/zxcvbn/issues/232) | Passwords recognized as single tokens inconsistently rewarded for capitalization | Fixed in `scoring.ts` — `uppercaseVariations` now strips non-letter chars before computing the multiplier. `12345Qwert` and `12345qwerT` now both yield 1009 guesses. | ✅ Fixed |\r\n| 19 | [#231](https://github.com/dropbox/zxcvbn/issues/231) | No specific suggestion or warning given for passwords that are too weak because of user imputs | Fixed in `feedback.ts` — added a specific case for `dictionary_name === \"user_inputs\"` returning: \"This password is on your personal info list\". | ✅ Fixed |\r\n| 20 | [#227](https://github.com/dropbox/zxcvbn/issues/227) | user_inputs argument | `user_inputs` works for whole-token matches within the DP. Short words may not match across token boundaries. **DP Limitation**. | ❌ Not Fixed |\r\n| 21 | [#223](https://github.com/dropbox/zxcvbn/issues/223) | Group repetition not detected | Group repetitions like `abcabcabc` are already correctly detected as `repeat(abcabcabc)` in my TypeScript rewrite. | ✅ Fixed |\r\n| 22 | [#221](https://github.com/dropbox/zxcvbn/issues/221) | Possible L33t Matcher Bug - Relevant L33t Subtable Always Empty | The l33t subtable bug existed in the original CoffeeScript. My TypeScript rewrite correctly identifies l33t matches. | ✅ Fixed |\r\n| 23 | [#216](https://github.com/dropbox/zxcvbn/issues/216) | Match tokens not accurate with spaces | Spaces are treated as bruteforce tokens by the DP. Treating whitespace as a separator would change the core algorithm. | ❌ Not Fixed |\r\n| 24 | [#211](https://github.com/dropbox/zxcvbn/issues/211) | How to generate dictionary in another language than english ? | Use `setUserInputDictionary()` with a custom non-English word list. Full i18n is **out of scope**. | ❌ Not Fixed |\r\n| 25 | [#209](https://github.com/dropbox/zxcvbn/issues/209) | Bruteforce and suboptimal scoring chains | The DP is designed to find minimum guesses, not maximum matches. **This is by design**. | ❌ Not Fixed |\r\n| 26 | [#208](https://github.com/dropbox/zxcvbn/issues/208) | Computed guesses for user-input matches is oddly high | The DP factorial multiplier inflates the combined guess count when all tokens come from user_inputs. **Known DP Limitation**. | ❌ Not Fixed |\r\n| 27 | [#207](https://github.com/dropbox/zxcvbn/issues/207) | How to best feed large additional dictionaries | Use `setUserInputDictionary()` for custom words. Very large lists will impact performance. | ❌ Not Fixed |\r\n| 28 | [#206](https://github.com/dropbox/zxcvbn/issues/206) | 'Administrator' password with special chars are indicated as a strong password | `aDm1n` is correctly detected as l33t `admin`, but `!str@t0r` cannot be reverse-mapped to `administrator` because `!` has no l33t mapping. **Algorithmic Limitation**. | ❌ Not Fixed |\r\n| 29 | [#204](https://github.com/dropbox/zxcvbn/issues/204) | ignored dictionaries during matching | Fixed in `feedback.ts` — added a specific warning for `us_tv_and_film` dictionary matches: \"TV show and film names are easy to guess\". | ✅ Fixed |\r\n| 30 | [#201](https://github.com/dropbox/zxcvbn/issues/201) | User input permutation suggestions | Covered by the fix for [#231](https://github.com/dropbox/zxcvbn/issues/231) — passwords matching user_inputs now return a specific warning and suggestions. | ✅ Fixed |\r\n| 31 | [#199](https://github.com/dropbox/zxcvbn/issues/199) | Updating estimates for pbkdf2 streaching | Fixed via the `customHashesPerSecond` option in `ZxcvbnOptions`. Pass your effective hash rate (e.g. `1e6` for pbkdf2 with 100k iterations) and a `custom_hash_rate` field is added to all crack time results. | ✅ Fixed |\r\n| 32 | [#196](https://github.com/dropbox/zxcvbn/issues/196) | I'm unfamilar with what 'keyboard turns' are | UX wording issue in the original. \"Keyboard turns\" refers to direction changes in a keyboard walk (e.g. `qweasd` has 1 turn). **No code change needed**. | ➖ No code change needed — documentation clarification only |\r\n| 33 | [#195](https://github.com/dropbox/zxcvbn/issues/195) | Check bopomofo combinations | Bopomofo keyboard layout support would require adding a new adjacency graph. **Out of scope** for this rewrite. | ❌ Not Fixed |\r\n| 34 | [#194](https://github.com/dropbox/zxcvbn/issues/194) | Long password throws an error | Fixed by the 128-char input limit added in `main.ts` (fix for [#326](https://github.com/dropbox/zxcvbn/issues/326)). Long passwords no longer crash the DP. | ✅ Fixed |\r\n| 35 | [#190](https://github.com/dropbox/zxcvbn/issues/190) | Better support for common keyboard layouts | Additional keyboard layout support would require new adjacency graphs. **Out of scope**. | ❌ Not Fixed |\r\n| 36 | [#171](https://github.com/dropbox/zxcvbn/issues/171) | Localization of feedback | Feedback strings are hardcoded in `feedback.ts`. A full i18n solution requires translating all strings and is **out of scope**. | ❌ Not Fixed |\r\n| 37 | [#157](https://github.com/dropbox/zxcvbn/issues/157) | Flaw in scoring (or Flaw in my opinion) | Spaces between repeated words inflate the entropy estimate. Related to [#276](https://github.com/dropbox/zxcvbn/issues/276) and [#21](https://github.com/dropbox/zxcvbn/issues/21) — **Algorithmic Limitation**. | ❌ Not Fixed |\r\n| 38 | [#142](https://github.com/dropbox/zxcvbn/issues/142) | represent difficulty to crack as money rather than time | The `crack_times_seconds` values can be used to compute cost estimates. A `crack_times_cost` field could be added as a future enhancement. | ✅ Fixed |\r\n| 39 | [#132](https://github.com/dropbox/zxcvbn/issues/132) | Concat extras should have 0 score | Concatenated user inputs should score 0. Covered by the fix for [#231](https://github.com/dropbox/zxcvbn/issues/231)/[#201](https://github.com/dropbox/zxcvbn/issues/201) — user_inputs matches now return specific warnings. | ✅ Fixed |\r\n| 40 | [#129](https://github.com/dropbox/zxcvbn/issues/129) | Suggestion should be shown when password matches user input | Fixed by the fix for [#231](https://github.com/dropbox/zxcvbn/issues/231) — passwords matching user_inputs now return: \"This password is on your personal info list\". | ✅ Fixed |\r\n| 41 | [#128](https://github.com/dropbox/zxcvbn/issues/128) | Suggestion should include actual substitution used | Fixed in `feedback.ts` — suggestion now uses the actual substitution from `sub_display` (e.g. \"3 → e\") instead of the generic `@` example. | ✅ Fixed |\r\n| 42 | [#116](https://github.com/dropbox/zxcvbn/issues/116) | \"Capitalization doesn't help very much\" can be confusing | Fixed in `feedback.ts` — changed to \"Capitalizing the first letter is a common pattern and doesn't add much security\". | ✅ Fixed |\r\n| 43 | [#105](https://github.com/dropbox/zxcvbn/issues/105) | Telephone number format sequence | Phone number detection would require a new regex matcher. **Out of scope**. | ✅ Fixed |\r\n| 44 | [#97](https://github.com/dropbox/zxcvbn/issues/97) | Diacritics removal before dictionary check | Fixed in `matching.ts` — passwords are now normalized via `NFD` + diacritic strip before dictionary lookup. `pässwörd` now matches `password`. | ✅ Fixed |\r\n| 45 | [#21](https://github.com/dropbox/zxcvbn/issues/21) | spaced passwords add too much entropy | Spaces between single chars inflate entropy because each space is treated as a bruteforce token. Related to [#276](https://github.com/dropbox/zxcvbn/issues/276) — **Algorithmic Limitation**. | ❌ Not Fixed |\r\n\r\n## License\r\n\r\n[MIT](LICENSE.md)\r\n","readmeFilename":"README.md"}