[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-5396":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":43,"readmeContent":44,"aiSummary":45,"trendingCount":16,"starSnapshotCount":16,"syncStatus":46,"lastSyncTime":47,"discoverSource":48},5396,"Ciphey","bee-san\u002FCiphey","bee-san","⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡","",null,"Rust",21451,1437,229,1,0,17,71,3,85.07,"MIT License",false,"master",true,[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"artificial-intelligence","cipher","cpp","cryptography","ctf","ctf-tools","cyberchef-magic","decryption","deep-neural-network","encodings","encryptions","hacking","hacktoberfest","hashes","natural-language-processing","pentesting","python","2026-06-12 04:00:25"," \n \u003Cp align=\"center\">\n \u003Cbr>\u003Cbr>\n➡️\n\u003Ca href=\"http:\u002F\u002Fdiscord.skerritt.blog\">Discord\u003C\u002Fa> | \n\u003Ca href=\"https:\u002F\u002Fbroadleaf-angora-7db.notion.site\u002FCiphey2-32d5eea5d38b40c5b95a9442b4425710\">Documentation \u003C\u002Fa>\n ⬅️\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ch1>Project ciphey\u003C\u002Fh1>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fbee-san\u002Fciphey\u002Fmain\u002Fimages\u002Fmain_demo.svg\" alt=\"ciphey demo\">\n\u003C\u002Fp>\n\n\nciphey is the next generation of decoding tools, built by the same people that brought you [Ciphey](https:\u002F\u002Fgithub.com\u002Fciphey\u002Fciphey).\n\nWe fully intend to replace [Ciphey](https:\u002F\u002Fgithub.com\u002Fciphey\u002Fciphey) with ciphey.\n\n✨ You can read more about ciphey here https:\u002F\u002Fskerritt.blog\u002Fintroducing-ciphey\u002F ✨\n\n# How to Use\n\nThe simplest way to use ciphey is to join the [Discord Server](http:\u002F\u002Fdiscord.skerritt.blog), head to the #bots channel and use ciphey with `$ciphey`. Type `$help` for helpful information!\n\nThe second best way is to use `cargo install ciphey` and call it with `ciphey`.\n\nYou can also `git clone` this repo and run `docker build .` it to get an image.\n\n# Features\n\nSome features that may interest you, and that we're proud of.\n\n## Fast\n\n![](https:\u002F\u002Fraw.githubusercontent.com\u002Fbee-san\u002Fciphey\u002Fmain\u002Fimages\u002Fbetter_demo.svg)\n\nciphey is fast. Very fast. Other decoders such as Ciphey require advance artifical intelligence to determine which path it should take to decode (whether to try Caesar next or Base64 etc).\n\nciphey is so fast we don't need to worry about this currently. For every 1 decode Ciphey can do, ciphey can do ~7. That's a 700% increase in speed.\n\n## Library First\n\nThere are 2 main parts to ciphey, the library and the CLI. The CLI simply uses the library which means you can build on-top of ciphey. Some features we've built are:\n* [A Discord Bot](https:\u002F\u002Fgithub.com\u002Fbee-san\u002Fdiscord-bot)\n* Better testing of the whole program 💖\n* This CLI\n\n## Decoders\n\nciphey currently supports 16 decoders and it is growing [fast](https:\u002F\u002Fgithub.com\u002Fbee-san\u002Fciphey\u002Fissues\u002F61). Ciphey supports around ~50, and we are adding more everyday.\n\n## Timer\n\nOne of the big issues with Ciphey is that it could run forever. If it couldn't decode your text, you'd never know!\n\nciphey has a timer (built into the library and the CLI) which means it will eventually expire. The CLI defaults to 5 seconds, the Discord Bot defaults to 10 (to account for network messages being sent across).\n\n## Better Docs, Better Tests\n\nciphey already has ~120 tests, documentation tests (to ensure our docs are kept up to date) and we enforce documentation on all of our major components. This is beautiful.\n\n## LemmeKnow\n\n![](https:\u002F\u002Fraw.githubusercontent.com\u002Fbee-san\u002Fciphey\u002Fmain\u002Fimages\u002Flemmeknow.svg)\n\n\u003Cimg width=\"861\" alt=\"Screenshot 2022-12-18 at 17 08 36\" src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F10378052\u002F208310491-86e704ca-963d-4850-a2b2-f14b6e0f4797.png\">\n\n[LemmeKnow](https:\u002F\u002Fgithub.com\u002Fswanandx\u002Flemmeknow) is the Rust version of [PyWhat](https:\u002F\u002Fgithub.com\u002Fbee-san\u002FpyWhat). It's 33 times faster which means we can now decode and determine whether something is an IP address or whatnot 3300% faster than in Python.\n\n## Multithreading\n\nCiphey did not support multi-threading, it was quite slow. ciphey supports it natively using [Rayon](https:\u002F\u002Fgithub.com\u002Frayon-rs\u002Frayon), one of the fastest multi-threading libraries out there.\n\nWhile we do not entirely see the effects of it with only 16 decoders (and them being quite fast), as we add more decoders (and slower ones) we'll see it won't affect the overall programs speed as much.\n\n## Multi level decodings\n\nCiphey did not support multi-level decryptions like a path of Rot13 -> Base64 -> Rot13 because it was so slow. ciphey is fast enough to support this, although we plan to turn it off eventually.\n\n## Configurable Sensitivity for Plaintext Detection\n\nciphey now supports configurable sensitivity levels for gibberish detection, allowing for more accurate plaintext identification across different types of encodings. Classical ciphers like Caesar use Low sensitivity to better handle English-like results, while most other decoders use Medium sensitivity by default.\n\nThis feature helps reduce false positives and negatives in plaintext detection, making ciphey more reliable across a wider range of encoded texts.\n\n## Enhanced Plaintext Detection with BERT\n\nciphey now offers enhanced plaintext detection using a BERT-based model from the `gibberish-or-not` crate. This feature:\n- Increases plaintext detection accuracy by approximately 40%\n- Reduces false positives and negatives when identifying plaintext\n- Can be enabled during first-run setup or later with `ciphey --enable-enhanced-detection`\n- Requires a one-time download of a 500MB AI model (requires a free Hugging Face account)\n\n# New Features\n## Better search algorithm\nWe now use A* search. This is very fast.\n\nA* works by using a heuristic to estimate the cost of reaching the goal from the current state.\n\nFirst, we ignore the heuristic for very fast decoders like Base64 and ensure we run them first each time on each node.\n\nThen, we calculate the heuristic for the remaining decoders using `cipher_identifier` which can determine the probability a given string is a certain cipher.\n\nWe store previous results in a cache to avoid recalculating the same path.\n\nWe prune the search tree to avoid unnecessary calculations and keep the memory usage down if it gets too bad.\n\nWe also keep track of statistics on decoders to dynamically prioritise decoders that work better (example: caesar is popular, but Beaufort is not so Caesar will dynamically be prioritised over Beaufort)\n\nFinally, we keep track of popular pairs. So base64 -> base64 is very popular, so we prioritise that path (among others).\n\n## Custom themes\n\nYou can now set a custom theme for ciphey. This is useful if you want to make ciphey look different.\n\nThis also helps with accessibility.\n\n## Vigenere\n\nWe now use perhaps the best algorithm for Vigenere.\n\nIt's fast, accurate and handles non-letter characters better than any other algorithm.\n\n## Better English checking\n\nWe use a qudgaram \u002F trigram \u002F english dict checker to calculate probability of plaintext. \n\nWe change the thresholds depending on the cipher. Example is that Caesar returns text that \"looks\" like english, whereas base64 does not.\n\nAs well as this, we have a database of popular regex (about 500) of api keys, mac addresses, etc.\n\nWe also have a `is_password` function to determine if a string is an exact password seen in a data dump.\n\n## More ciphers\n* Braille\n* Atbash\n* Vigenere\n\n## Database\n\nWe now store statistics in a database. This is useful for seeing how ciphey is doing over time.\n\n# AI Use\n\nWe use AI for 2 things:\n1. The TUI is entirely vibe coded.\n2. I made AI spend hours researching every single CTF challenge out there. It created a list of 15,071 CTFs. It then went through every single CTF and looked for writeups. In those writeups it looked for anything related to encoding \u002F decoding. It then created tests out of those. This enabled us to increase our testing coverage and make sure all CTF encoding \u002F decoding challenges are solveable with this tool.\n\n","Ciphey 是一个自动解密、解码和破解哈希值的工具，无需预先知道密钥或加密方式。它使用 Rust 语言编写，具备高速处理能力，相较于其他解码器如 Ciphey 能够实现约700%的速度提升。项目内置了计时器功能以防止无限运行，并且支持16种以上的解码器，数量还在持续增长中。此外，Ciphey 还提供了丰富的文档和测试用例保证代码质量。适合网络安全研究者、CTF 竞赛选手以及对密码学感兴趣的开发者在进行逆向工程、安全审计或者教育学习时使用。",2,"2026-06-11 03:03:03","top_language"]