zxcvbn VS marked

Lightweight is an understatement here.

A client's project (with not necessarily technical customers) has had pretty reasonable success using the Dropbox originated library[1] for this, `zxcvbn`[2], on both frontend via js (for "instant" feedback) and on the backend via php (to enforce the requirements when writing password hashes to the database)

1: https://dropbox.tech/security/zxcvbn-realistic-password-stre...

2: https://github.com/dropbox/zxcvbn

use zxcvbn to check your password strength more thoroughly

source if anyone wants the whole list https://github.com/dropbox/zxcvbn/blob/master/data/passwords.txt

Password strength is evaluated based on the zxcvbn library.

In contrast, let's consider the password "zxcvbn214". How might we assign an entropy to this password? Is it 369? Or 266 * 103? Anyone familiar with a QWERTY keyboard or Dropbox's password strength estimator knows that "zxcvbn" is hardly a random sequence of letters. This same principle applies to "l33t" speak, e.g. replacing all "e"s with 3s and "a"s with 4s. These strategies may "trick" simple entropy calculations into estimating a high entropy, but it won't trick sophisticated attackers. This leads to strength over-estimation, which is, I argue, the worst thing we can do in this context.

> except for ZXCVBN

You mean the Low-Budget Password Strength Estimator?

https://github.com/dropbox/zxcvbn

Yeah, that name is totally legit.

For any part of the password that the zxcvbn cannot match to a known pattern, it uses a brute-force cardinality of 10, i.e., it estimates that the number of guesses required to crack a password or password segment of length N is equal to 10N (equivalent to the number of guesses required to exhaust all possibilities if your password consisted only of numbers).

We took a similar approach to passphrase stretching in EnvKey[1] v1 (EnvKey is a secrets manager, not a passwords manager, but uses end-to-end encryption in a similar way). We used PBKDF2 with iterations set a bit higher than the currently recommended levels, as well as Dropbox's zxcvbn lib to try to identify and block weak passphrases.

Ultimately, I think it's just not good enough. Even if you're updating iteration counts automatically (which is clearly not a safe assumption, and to be fair not something we did in EnvKey v1 either), and even with safeguards against weak passphrases, using human-generated passphrases as a single line of defense is just fundamentally weak.

That's why in EnvKey v2, we switched to primarily using high entropy device-based keys--a lot like SSH private keys, except that on Mac and Windows the keys get stored in the OS keychain rather than in the file system. Also like SSH, a passphrases can optionally be added on top.

The downside (or upside, depending how you look at it) is that new devices must be specifically granted access. You can't just log in and decrypt on a new device with only your passphrase. But the security is much stronger, and you also avoid all this song and dance around key stretching iterations.

1 - https://github.com/envkey/envkey

2 - https://github.com/dropbox/zxcvbn

Next, install gray-matter to extract metadata from the front matter of markdown files, and marked to convert the markdown files to HTML:

📑 Marked Markdown parser. Use it to create your own markdown editor.

Vrite SDK provides a few built-in input and output transformers. These are functions, with standardized signatures to process the content from and into Vrite. In this case, gfmInputTransformer is essentially a GitHub Flavored Markdown parser, using Marked.js under the hood.

Another contestant in this realm is Bright[1]. It runs entirely on the server and doesn't increase bundle size as seen here[2]. Regarding parsing speed tree-sitter is without a doubt performant since it is written in Rust, but I don't have any problems "parsing on every keystroke" with a setup containing Marked[3], highlight.js[4] and a sanitizer. I did however experience performance issues with other Markdown parser libraries than Marked.

[1]: https://bright.codehike.org/

[2]: https://aihelperbot.com/test-suite

[3]: https://github.com/markedjs/marked

[4]: https://highlightjs.org/

The project uses markedJS to convert markdown into HTML, this is their GitHub page.

To handle pasting block Markdown content like this, I had to tap into ProseMirror and implement a custom mechanism (though somewhat based on TipTap’s paste rules), detecting starting and ending points of the blocks and parsing them with Marked.js.

I am using marked for markdown parsing together with marked-highlighting to handle syntax highlighting and everything is working as it should.

I'm trying to render user input using SvelteMarkdown (that uses marked).

GPT API returns data in markdown format. You can parse it using a Markdown library and string manipulation. On Electron app I developed https://jhappsproducts.gumroad.com/l/gpteverywhere, I used https://github.com/markedjs/marked and a code syntax highlighting package to display code blocks. And used JavaScript string manipulation to detect when code blocks start and end so I could add COPY/SAVE buttons to the blocks. I hope this helps, and happy coding! :)

Again, with streaming enabled, you’ll now receive new tokens as soon as they’re available. Given that OpenAI’s API uses Markdown in its response format, a full message will need to be put together from the incoming tokens and parsed to HTML, as accepted by the replaceContent function. For this purpose, I’ve used the Marked.js parser.