Squeezing the lemon, the AI hype train continues full speed ahead.
Email obfuscation rendered almost ineffective against ChatGPT
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Re: Email obfuscation rendered almost ineffective against ChatGPT
#92Earlier quoted context omitted.
Which model did you use? GPT-4 can encode and decode Base64, at least for short strings. I was pretty surprised when I first saw that. Proof: https://chat.openai.com/share/9382be94-d59a-4a2a-b03b-43dba3... https://chat.openai.com/share/421cc39e-ea9c-4ff6-9e45-1aa151...
Can it figure this one out without any hints? Not base64. Use case [1] ONXW2ZLUNBUW4Z2AONXW2ZLXNBSXEZJOORWGI=== [1] - https://ohblog.net/about/
> The string you've provided appears to be encoded in Base32. Decoding this string from Base32, it results in:
> "This is a test. This is only a test."
So, it got the base32 part right, but the decoding wrong. I would have been extremely surprised if it got the decoding right, though.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#93No 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…
If OpenAI can generate those for customers they will make a killing. Export the piece out of ChatGPT you care about and run it on-prem for way less.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#94Earlier quoted context omitted.
Can it figure this one out without any hints? Not base64. Use case [1] ONXW2ZLUNBUW4Z2AONXW2ZLXNBSXEZJOORWGI=== [1] - https://ohblog.net/about/
> The string you've provided appears to be encoded in Base32. Decoding this string from Base32, it results in: > "This is a test. This is only a test." So, it got the base32 part right, but the decoding wrong. I would have been extremely surprised if it got the decoding right, though.
That's still kinda cool. Now I'm curious if it can decode all the figlet fonts too. Size can be controlled with HTML as some are easier to read visually by a human if smaller
[Edit] - This might makes ones eyes bleed but I am curious if it can read this [1]. If installing figlet type showfigfonts to see examples of all the installed fonts. More can be installed [2] in /usr/share/figlet/fonts/
Re: Email obfuscation rendered almost ineffective against ChatGPT
#95I used chatGPT to decode proprietary binary files of some industrial machinery. It was amazing how it can decipher shit and find patterns. It first looked for ascii characters, then byte sequences acting as delimiters, then it started looking at which bytes could be the length or what 4-bytes could be floating point numbers of coordinates and which endianness was more logic for coordinates, etc. etc. crazy stuff.
That sounds amazing. Shame it's proprietary, I'd love to read that chat transcript. do you just paste binary data in and ask it to decipher it? or do you ask it leading questions? or...?
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...
Re: Email obfuscation rendered almost ineffective against ChatGPT
#96I actually did a more empirical approach to this problem recently.
https://kylediaz.com/post/scraping-emails-hackernews/#llm-sc...
I had similar results.
Email obfuscation like name [at] domain [dot] com is trivially found by both regex and LLMs, but emails like name@[my domain] or name(delete me)@domain.com are harder for LLMs. LLMs could find those emails, but wouldn't try to obfuscate it no matter how I prompted it. It's probably a skill issue on my part, and I'll have to try that neat "think step-by-step" trick.
IMO, the best and easiest way to obfuscate is using invisible HTML elements: nameyou can't see this@domain.com It's technically scrapable (as I show in my post), but it gives way more "security" than [at]/[dot] while still allowing users to just copy/paste it.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#97Earlier 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…
If you're looking for a practical guide to getting started with fine tuning, I wrote one a couple of months ago that got pretty popular here on HN. Might be helpful if you're interested in playing around with it! https://news.ycombinator.com/item?id=37484135
Re: Email obfuscation rendered almost ineffective against ChatGPT
#98> it has become entirely ineffectual in the era of advanced language models
Still no spam when I am doing this.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#99I guess we'll see much better LLM-based spam filters very soon. LLMs are really good at classification tasks and spam detection would be a great use case for this. It's an arms race.
LLMs are incredibly bad at this task and ones like ChatGPT lack the labeled training data necessary to do it. Spam has been classified with ML models at scale for at least 15 years. Spam quality is totally determined by freshness. What you want to know is whether people marked this as spam in the last few seconds, not whether someone labeled similar content as spam in 2019. Finally, the key signals are not present in LLM training sets, since message content is virtually irrelevant to spam classification.
Re: Email obfuscation rendered almost ineffective against ChatGPT
#100Earlier 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…
If OpenAI can generate those for customers they will make a killing. Export the piece out of ChatGPT you care about and run it on-prem for way less.
It's entirely possible without OpenAI doing anything else. 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.
Measure the quality of your model vs OpenAI for the same inputs, and then swap out the model in your workflow once happy with the results.