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Email obfuscation rendered almost ineffective against ChatGPT

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Re: Email obfuscation rendered almost ineffective against ChatGPT

#191

Post by Tostino »

woadwarrior01 wrote: post #106
Tostino wrote: post #8

Earlier quoted context omitted.

You do realize how possible it is to fine tune a task like this (along with a hundred others in a similar vein) on a tiny model you can scale on your own hardware? I've run hundreds of millions (150m so far in a couple of weeks of non-continuous running as I tweaked things) of tokens through my 2x 3090 with a 13b llama2 model I fine tuned on tasks like: summary, knowledge graph generation, writing using the knowledge…

It's even easier than that. There's no need to even fine tune an LLMs to do it. Here's a screenshot[1] of a 4 bit quantised version of an off the shelf open LLM (WizardLM 13B v1.2) doing it on my Mac. [1]: https://imgur.com/a/S9jnHWJ

That may be true, and for some tasks the accuracy may be high enough. I have gotten much more consistency in my tasks by fine tuning though.

Getting a consistently good result for one shape of input may not indicate that same performance for another shape of input for example.

Re: Email obfuscation rendered almost ineffective against ChatGPT

#192

Post by dragonwriter »

axlee wrote: post #5

No large-scale email scraper has the budget necessary to run the content it scrapes through a LLM. So as far as real-world goes, nothing changes: the .2 cents it would cost to run ChatGPT on a page to extract potentially obfuscated emails would cost magnitudes more than it could ever bring in revenues. Regarding the examples provided, there is nothing there that a simple regex couldn't achieve, so I don't really see…

> No large-scale email scraper has the budget necessary to run the content it scrapes through a LLM

Maybe not a big third-party commercial one, a smaller, purpose tuned one that the scraper runs on their own, OTOH...

Re: Email obfuscation rendered almost ineffective against ChatGPT

#193

Post by Tostino »

dylan604 wrote: post #105
Tostino wrote: post #46

Earlier quoted context omitted.

The grid can be negotiated with if we put in the infrastructure.

That would cost money and lower the profits of the people that own/control the grid. I sometimes wonder how much money these robber barons spend on lobbying and other PR campaigns to convince that climate change isn't a problem and that the grid is just fine. It's one of those unanswerable questions I'm sure, but how much progress could be made by redirecting that amount of money to actually improving the grid itself…

That was actually part of how I intended my "negotiated" to be taken. Part of it is a monetary negotiation where we invest more in base load and peak load, but some of it needs to be in the ability for the grid to request to shed load and the devices react accordingly.

Re: Email obfuscation rendered almost ineffective against ChatGPT

#194

Post by batch12 »

ac2u wrote: post #176
batch12 wrote: post #167

Earlier quoted context omitted.

There are likely only so many patterns that can exist and still be usable as a shareable and human reversible email address. Maybe one could have the llm identify these edge cases and write regex/code to parse future instances to convert back to a usable format.

Let's say the poster works at IBM and they want to provide a means for humans to contact them and frustrate scrapers so they say: 'My email address is my username@"the company no one ever got fired for buying from".com' Write me a regex for that. GPT on the other hand: https://imgur.com/a/Hw0kcee

Yes, of course- my point was that the LLM could gradually write functions to parse most of the clever patterns as it comes across them. Use the LLM as a trapdoor when nothing else matches, and in the same text generation create a script to parse the same input next time.

I agree that the LLM is more versatile and retract what I wrote earlier saying that all patterns could be accounted for. I should have said most easy to figure out patterns.

https://imgur.com/a/uFV3XD9

Re: Email obfuscation rendered almost ineffective against ChatGPT

#195

Post by me_bx »

Story: Back in 2012, writing a program to extract obfuscated email addresses out of text corpuses was the homework of the first class of Stanford University's Natural Language Processing online course at Coursera. No AI or LLM involved.

[1]: https://www.classcentral.com/course/nlp-836

Re: Email obfuscation rendered almost ineffective against ChatGPT

#197

Post by manwithaplan »

woadwarrior01 wrote: post #106
Tostino wrote: post #8

Earlier quoted context omitted.

You do realize how possible it is to fine tune a task like this (along with a hundred others in a similar vein) on a tiny model you can scale on your own hardware? I've run hundreds of millions (150m so far in a couple of weeks of non-continuous running as I tweaked things) of tokens through my 2x 3090 with a 13b llama2 model I fine tuned on tasks like: summary, knowledge graph generation, writing using the knowledge…

It's even easier than that. There's no need to even fine tune an LLMs to do it. Here's a screenshot[1] of a 4 bit quantised version of an off the shelf open LLM (WizardLM 13B v1.2) doing it on my Mac. [1]: https://imgur.com/a/S9jnHWJ

The system confabulated the www subdomain of the “URL provided in the text”, right?

Re: Email obfuscation rendered almost ineffective against ChatGPT

#198

Post by sanderjd »

jachee wrote: post #153
sanderjd wrote: post #44

Earlier quoted context omitted.

How does one efficiently learn how to do such things, and what kinds of problems such approaches are fruitful for? I find there to be a giant gap in learning about this stuff between material that boils down to "use magic words and system prompts to improve results from one of the big models" and "how do LLMs work from first principles". I still haven't found a great resource that covers this middle ground, which see…

The industry term for that middle ground is a “moat”, and the people who are most familiar with it are getting paid for what they know, so they’re not giving it away.

I think that may be right, but if so, that seems pretty unusual to me.

I've gone through a few of these "new kinds of software becoming useful" transition periods - most notably applications moving to the web, and then native smart phone applications - and in none of those transitions was there a dearth of resources on how to spin up on doing useful things due to this "moat" concern.

Nobody was protecting their iphone app dev moat by not publishing books and training courses on Objective-C and XCode...

Re: Email obfuscation rendered almost ineffective against ChatGPT

#199

Post by sanderjd »

Swizec wrote: post #72
sanderjd wrote: post #44

Earlier quoted context omitted.

How does one efficiently learn how to do such things, and what kinds of problems such approaches are fruitful for? I find there to be a giant gap in learning about this stuff between material that boils down to "use magic words and system prompts to improve results from one of the big models" and "how do LLMs work from first principles". I still haven't found a great resource that covers this middle ground, which see…

> I still haven't found a great resource that covers this middle ground, which seems to me to be where a lot of the power of these approaches is going to reside. Read papers, build intuition, experiment. That last part may be the most important.

I think this is the disconnect: It doesn't strike me that what I'm talking about has anything to do with "papers". So from your comment, I'm once again left wondering what you mean.

My sense is that I have a much better grasp of the foundational material here, having read in depth books and papers about that, but still can't quite wrap my head around the question of how people are actually "operationalizing" this into useful software.

But to your point about experimentation, it might just be the kind of thing where there is no path to enlightenment besides working on a project and running into and overcoming all the hurdles along the way.

Re: Email obfuscation rendered almost ineffective against ChatGPT

#200

Post by tamimio »

throw_m239339 wrote: post #170
tamimio wrote: post #158

Earlier quoted context omitted.

Check Ciphey, I have used several times before and overall it’s great. https://github.com/Ciphey/Ciphey

I'm looking to reverse engineer some file format in order to implement and editor for that file format (proprietary file format, undocumented but AFAIK not encrypted), would it be possible to use that program for that purpose? Is there another free tool for that purpose?

That’s a very generic question, hard to tell without extra details, but I find it useful against decoding hashes or at least giving clues oh how to decode it.
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