There will always be a subset of users whose goal is to not use your service, but to arbitrage your service into the maximum value for themselves.
For example -- let's say you offer $100 in free AWS credits by signing up to your platform. Expect a malicious user to eventually come to your platform, realize they can resell those $100 in credits for $50, and start using your platform for their own gain. Unless the mechanisms you add in place to reduce fraud / second sign ups / etc is greater than the value that they are receiving ($50), they will continue.
With sites where the platform is free, the math almost always makes sense for these malicious users to eventually abuse. In this case it was leveraging the email reputation of another domain at no cost to their own (along with the added value of anyone getting phished), but on other sites it's public profiles being used for backlinks / spam, etc.
The sentence construction, choice of vocabulary, and continually breathless tone are all clear indicators this was written by an llm and barely edited.
I threw part of it into pangram to get a second opinion:
Have you tried putting known human writing into pangram? I have. I've gotten 100% AI with multiple samples of my own human writing. It has also given me 50% on things I know were 100% AI written (from my prompts).
Pangram and everything like it is useless. The results are random on known samples.
GP is right. This paragraph is a major tell. If you have read enough ChatGPT output that hasn’t been humanized, you start to instantly notice it:
“ The attacker hadn’t broken into anything. They’d just noticed something I hadn’t: I had a verified email-sending domain attached to open, unverified signup, and that’s a useful primitive if you don’t care what you send.”
Pangram specifically (as opposed to most other detectors) publish internal audits, and seem to welcome external audits [0]. I'm not saying that you are necessarily wrong, just that in my opinion they have earned a higher bar of criticism than random one off anecdote.
That's a fair criticism, I certainly didn't run a full benchmark. Just a few of my own pieces of writing. I also did it a few months ago, maybe it's gotten better since.
That's interesting! I have tried to get false positives from pangram and failed, so I trusted it a bit more than any of the others, although I generally just rely on my own intuition. I am curious what your false positive samples looked like, if you're willing to share.
(I'm less interested in false negatives; I have successfully produced those myself.)
> What stuck with me wasn’t the scale, although 14,000 people getting a phishing email from a domain I own is bad. It was how mundane it was.
> There was no exploit. No vulnerability disclosure. No CVE for me to write. The attacker filled out my signup form 942 times, made 942 workspaces, sent 942 batches of about a hundred invitations each, and stopped. They used my tool exactly as designed. The design was just bad enough that the tool was good for phishing.
Huh. I didn't assume it was LLM-generated. I liked the article. I appreciated that author cared about the 14K phish recipients as if they were proper users.
I will say, I've grown bored of folks complaining about AI generated content. But, to each their own. Good luck storming the castle.
Is this the new norm for trying to make software projects in the wild?
The 14000 sends over 3 hours (< 1/s) makes it sound more-than-human speed. E.g. automated.
Wondering if LLM-assisted vulnerability hunting will lead to the same gains in scale for bad actors wanting to find spammable channels in applications. The barrier to entry becomes so much greater because any small project, once found, can be wrung dry of all its trust signals by third parties
Abuse such as this wasn't uncommon before, email platforms with lax ratelimits have always been abused through their clients' unsecured infrastructure. The only difference in post-LLM world is the amount of platforms as well as clients popping up in this space with dubious code quality that may lead to more attacks as;
a) having an email-sending product typically meant you had a project with a lot of effort invested into it as well as knowledge
b) the models, tokens spent and review done differs in the world of vibecoding and there is a race to the bottom to produce, produce, produce. Quantity > quality
"Disposable email domains blocked"
This one is really annoying as in practice, more and more services that become spammers or sell to what are basically spammers cannot be kept at arms length.
I don't know what domain was used to send that crap but you should probably have an abuse contact listed at kaneo.app so that if people do discover issues from your service they have an easy way to get a hold of you.
I've been thinking of making an event platform like Partiful, but only for personal use because it's also the perfect platform for spam (send emails and texts to people with attacker-controller content).
1. You are not alone, this happens at a large scale across the board with companies of all sizes.
2. More than likely the abuser did not do it manually, more than likely they automated it
3. As a thoughtful business one may have rolled out all the authentication features/gates if the business picks up, as a starter the safe idea could have been to put it behind any openly available OAuth provider
If you have commits in the linux kernel, your open source code has certainly been used to murder people. Because it's in everything, including weapons systems.
There will always be a subset of users whose goal is to not use your service, but to arbitrage your service into the maximum value for themselves.
For example -- let's say you offer $100 in free AWS credits by signing up to your platform. Expect a malicious user to eventually come to your platform, realize they can resell those $100 in credits for $50, and start using your platform for their own gain. Unless the mechanisms you add in place to reduce fraud / second sign ups / etc is greater than the value that they are receiving ($50), they will continue.
With sites where the platform is free, the math almost always makes sense for these malicious users to eventually abuse. In this case it was leveraging the email reputation of another domain at no cost to their own (along with the added value of anyone getting phished), but on other sites it's public profiles being used for backlinks / spam, etc.
Please write your blog post yourself if you expect people to read it. The LLM output is very grating.
Why do you think this is LLM-generated? Reads perfectly fine to me.
The sentence construction, choice of vocabulary, and continually breathless tone are all clear indicators this was written by an llm and barely edited.
I threw part of it into pangram to get a second opinion:
https://www.pangram.com/history/8d6a7de3-86ac-4ce0-86c5-4f93...
Have you tried putting known human writing into pangram? I have. I've gotten 100% AI with multiple samples of my own human writing. It has also given me 50% on things I know were 100% AI written (from my prompts).
Pangram and everything like it is useless. The results are random on known samples.
GP is right. This paragraph is a major tell. If you have read enough ChatGPT output that hasn’t been humanized, you start to instantly notice it:
“ The attacker hadn’t broken into anything. They’d just noticed something I hadn’t: I had a verified email-sending domain attached to open, unverified signup, and that’s a useful primitive if you don’t care what you send.”
Pangram specifically (as opposed to most other detectors) publish internal audits, and seem to welcome external audits [0]. I'm not saying that you are necessarily wrong, just that in my opinion they have earned a higher bar of criticism than random one off anecdote.
[0] https://xcancel.com/JohnHolbein1/status/2059648132250570975#...
That's a fair criticism, I certainly didn't run a full benchmark. Just a few of my own pieces of writing. I also did it a few months ago, maybe it's gotten better since.
That's interesting! I have tried to get false positives from pangram and failed, so I trusted it a bit more than any of the others, although I generally just rely on my own intuition. I am curious what your false positive samples looked like, if you're willing to share.
(I'm less interested in false negatives; I have successfully produced those myself.)
I'll try to pull them up for you, I'd have to go back and find them on my computer.
> There was no exploit. No vulnerability disclosure. No CVE for me to write.
was a dead giveaway in my mind when I read it.
> What stuck with me wasn’t the scale, although 14,000 people getting a phishing email from a domain I own is bad. It was how mundane it was.
> There was no exploit. No vulnerability disclosure. No CVE for me to write. The attacker filled out my signup form 942 times, made 942 workspaces, sent 942 batches of about a hundred invitations each, and stopped. They used my tool exactly as designed. The design was just bad enough that the tool was good for phishing.
The comments continue until the patterns are internalized https://news.ycombinator.com/item?id=48316049
Dots and periods. Everywhere. So many. There is no paragraph — its sentences all the way down.
That made me think if the project is entirely vibecoded as well.
Even for a project manager without network access, hosting flawed software on your LAN can only get you so far.
Huh. I didn't assume it was LLM-generated. I liked the article. I appreciated that author cared about the 14K phish recipients as if they were proper users.
I will say, I've grown bored of folks complaining about AI generated content. But, to each their own. Good luck storming the castle.
Is this the new norm for trying to make software projects in the wild?
The 14000 sends over 3 hours (< 1/s) makes it sound more-than-human speed. E.g. automated.
Wondering if LLM-assisted vulnerability hunting will lead to the same gains in scale for bad actors wanting to find spammable channels in applications. The barrier to entry becomes so much greater because any small project, once found, can be wrung dry of all its trust signals by third parties
Abuse such as this wasn't uncommon before, email platforms with lax ratelimits have always been abused through their clients' unsecured infrastructure. The only difference in post-LLM world is the amount of platforms as well as clients popping up in this space with dubious code quality that may lead to more attacks as;
a) having an email-sending product typically meant you had a project with a lot of effort invested into it as well as knowledge
b) the models, tokens spent and review done differs in the world of vibecoding and there is a race to the bottom to produce, produce, produce. Quantity > quality
"Disposable email domains blocked" This one is really annoying as in practice, more and more services that become spammers or sell to what are basically spammers cannot be kept at arms length.
I don't know what domain was used to send that crap but you should probably have an abuse contact listed at kaneo.app so that if people do discover issues from your service they have an easy way to get a hold of you.
I've been thinking of making an event platform like Partiful, but only for personal use because it's also the perfect platform for spam (send emails and texts to people with attacker-controller content).
Couple thing:
1. You are not alone, this happens at a large scale across the board with companies of all sizes.
2. More than likely the abuser did not do it manually, more than likely they automated it
3. As a thoughtful business one may have rolled out all the authentication features/gates if the business picks up, as a starter the safe idea could have been to put it behind any openly available OAuth provider
If you have commits in the linux kernel, your open source code has certainly been used to murder people. Because it's in everything, including weapons systems.
Wish I’d read a different example here, don’t even wanna subconsciously discourage any open source heroes
Please write your own blog posts rather than asking us to read LLM slop.