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Email obfuscation rendered almost ineffective against ChatGPT

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Re: Email obfuscation rendered almost ineffective against ChatGPT

#181

Post by dazhbog »

simonw wrote: post #143
dazhbog wrote: post #95

Earlier quoted context omitted.

Lots of follow-ups, here is the transcript (warning, too much bla bla). i was feeding the file to gpt4 slowly because i was hitting its input limits: https://chat.openai.com/share/23db424d-7307-46da-913f-d45cdc...

Have you tried ChatGPT Code Interpreter aka Advanced Data Analysis mode? That's the thing that can write and then execute Python code against files you upload to it. I've had great results using it to decipher weird binary formats, since it can try things out and iterate on them.

Yes, I tried it for this bin file and it didn't go as deep as stock gpt4. It wrote some python code to parse the file, but it was hard to have a long conversation with it regarding the data. It was always jumping into writing python before the brainstorming finished (could be a feature not a bug) ;)

Re: Email obfuscation rendered almost ineffective against ChatGPT

#182

Post by [deleted] »

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…

[deleted]

Re: Email obfuscation rendered almost ineffective against ChatGPT

#185

Post by Tostino »

capableweb wrote: post #125
leereeves wrote: post #122

Earlier quoted context omitted.

How would they "look into how it was generated"?

You train your model, publish it on huggingface and then write in the README: > This is how I made this model: Design your tasks to be repeatable and small steps, call the OpenAI API and log all requests/responses. > Filter out any bad responses and take a representative sample of the data you have collected from OpenAI,and train a Mistral or Llama2 model with the request/response pairs. Just one example.

I'm not competing with OpenAI in any sense of the word.

Re: Email obfuscation rendered almost ineffective against ChatGPT

#186

Post by kajic »

john2x wrote: post #177
kajic wrote: post #151

Earlier quoted context omitted.

Some people set filterkeyword to name-of-business, to know who sold them out if they start receiving spam on that address.

I use the + technique, but I've been doubtful of its effectiveness. It's easy enough to remove the text after the +.

Yeah, I’m sure many spammers “fixed” this a long time ago. The logical next step would be to bounce all emails which go to the plain version of an email address, but I guess also anything after + which one hasn’t “whitelisted”.

Re: Email obfuscation rendered almost ineffective against ChatGPT

#187

Post by jiveturkey »

Tostino wrote: post #185
capableweb wrote: post #125

Earlier quoted context omitted.

You train your model, publish it on huggingface and then write in the README: > This is how I made this model: Design your tasks to be repeatable and small steps, call the OpenAI API and log all requests/responses. > Filter out any bad responses and take a representative sample of the data you have collected from OpenAI,and train a Mistral or Llama2 model with the request/response pairs. Just one example.

I'm not competing with OpenAI in any sense of the word.

I haven't read the ToS, but it may not specifically require competing to be a violation.

Re: Email obfuscation rendered almost ineffective against ChatGPT

#188

Post by imranq »

Tostino wrote: post #8
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…

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…

Is it possible to know the minimum model size / data set size it takes to train a model given certain efficiency parameters (latency, etc.)?

Re: Email obfuscation rendered almost ineffective against ChatGPT

#189

Post by smsm42 »

Tostino wrote: post #8
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…

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…

You don't need a "model" for this - I remember a Coursera course on ML I did some years ago, and one of the exercises was email extraction. With some very basic algorithms, nothing more than a bunch of common python libraries and couple of days of work, it's possible to extract over 90% of emails with commonly used tricks. I'm not sure the remaining number is worth making more complicated models for it - the returns are quickly diminishing, and wasting time on spamming people who are clever enough to invent their own unique email hiding technique probably doesn't have a good ROI anyway.

Re: Email obfuscation rendered almost ineffective against ChatGPT

#190

Post by danielmarkbruce »

sanderjd wrote: post #44
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…

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…

huggingface is your friend.
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