IMO there was something of a de facto contract, pre-LLMs, that the set of things one would publicly mirror/excerpt/index and the set of things one would scrape were one and the same.
Back then, legitimate search engines wouldn’t want to scrape things that would just make their search results less relevant with garbage data anyways, so by and large they would honor robots.txt and not overwhelm upstream servers. Bad actors existed, of course, but were very rarely backed by companies valued in the billions of dollars.
People training foundation models now have no such constraints or qualms - they need as many human-written sentences as possible, regardless of the context in which they are extracted. That’s coupled with a broader familiarity with ubiquitous residential proxy providers that can tunnel traffic through consumer connections worldwide. That’s an entirely different social contract, one we are still navigating.
The only real difference that LLM crawlers tend to not respect /robots.txt and some of them hammer sites with some pretty heavy traffic.
The trap in the article has a link. Bots are instructed not to follow the link. The link is normally invisible to humans. A client that visits the link is probably therefore a poorly behaved bot.
Recently there have been more crawlers coming from tens to hundreds of IP netblocks from dozens (or more!) of ASN in highly time and URL correlated fashion with spoofed user-agent(s) and no regard for rate or request limiting or robots.txt. These attempt to visit every possible permutation of URLs on the domain and have a lot of bandwidth and established tcp connections available to them. It's not that this didn't happen pre-2023 but it's noticably more common now. If you have a public webserver you've probably experienced it at least once.
Actual LLM involvement as the requesting user-agent is vanishingly small. It's the same problem as ever: corporations, their profit motive during $hypecycle coupled with access to capital for IT resources, and the protection of the abusers via the company's abstraction away of legal liability for their behavior.
If a legit user accesses the link through an <img> tag, the browser will send some telling headers. Accept: image/..., Sec-Fetch-Dest: image, etc.
You can also ignore requests with cross-origin referrers. Most LLM crawlers set the Referer header to a URL in the same origin. Any other origin should be treated as an attempted CSRF.
These refinements will probably go a long way toward reducing unintended side effects.
You do not have a meta refresh timer that will skip your entire comment and redirect to the good page in a fraction of a second too short for a person to react.
You also have not used <p hidden> to conceal the paragraph with the link from human eyes.
Moreover, there is no easy way to distinguish such a fetch from one generated by the bad actors that this is intended against.
When the bots follow the trampoline page's link to the honeypot, they will
- not necessarily fetch it soon afterward;
- not necessarily fetch it from the same IP address;
- not necessarily supply the trampoline page as the Referer.
Therefore you must assume that out-of-the-blue fetches of the honeypot page from a previously unseen IP address must be bad actors.
I've mostly given up on honeypotting and banning schemes on my webserver. A lot of attacks I see are single fetches of one page out of the blue from a random address that never appears again (making it pointless to ban them).
Pages are protected by having to obtain a cookie from answering a skill testing question.
That may work for blocking bad automated crawlers, but an agent acting on behalf of a user wouldn't follow robots.txt. They'd run the risk of hitting the bad URL when trying to understand the page.
An agent acting on my behalf, following my specific and narrowly scoped instructions, should not obey robots.txt. Just like how a single cURL request shouldn't follow robots.txt. (It also shouldn't generate any more traffic than a regular browser user)
Unfortunately "mass scraping the internet for training data" and an "LLM powered user agent" get lumped together too much as "AI Crawlers". The user agent shouldn't actually be crawling.
If your specific and narrowly scoped instructions cause the agent, acting on your behalf, to click that link that clearly isn't going to help it--a link that is only being clicked by the scrapers because the scrapers are blindly downloading everything they can find without having any real goal--then, frankly, you might as well be blocked also, as your narrowly scoped instructions must literally have been something like "scrape this website without paying any attention to what you are doing", as an actual agent--just like an actual human--wouldn't find our click that link (and that this is true has nothing at all to do with robots.txt).
If it's a robot it should follow robots.txt. And if it's following invisible links it's clearly crawling.
Sure, a bad site could use this to screw with people, but bad sites have done that since forever in various ways. But if this technique helps against malicious crawlers, I think it's fair. The only downside I can see is that Google might mark you as a malware site. But again, they should be obeying robots.txt.
should cURL follow robots.txt? What makes browser software not a robot? Should `curl <URL>` ignore robots.txt but `curl <URL> | llm` respect it?
The line gets blurrier with things like OAI's ChatGPT Atlas. It's just re-skinned Chromium that's a regular browser, but you can ask an LLM about the content of the page you just navigated to. The decision to use an LLM on that page is made after the page load. Doing the same thing but without rendering the page doesn't seem meaningfully different.
In general robots.txt is for headless automated crawlers, not software performing a specific request for a user. An LLM powered user agent (browser) wouldn't follow invisible links, or any links, because it's not crawling.
Your web browser is a robot, and always has been. Even using netcat to manually type your GET request is a robot in some sense, as you have a machine translating your ascii and moving it between computers.
The significant difference isn't in whether a robot is doing the actions for you or not, it's whether the robot is a user agent for a human or not.
Confused as to what you're asking for here. You want a robot acting out of spec, to not be treated as a robot acting out of spec, because you told it to?
How does this make you any different than the bad faith LLM actors they are trying to block?
robots.txt is for automated, headless crawlers, NOT user-initiated actions. If a human directly triggers the action, then robots.txt should not be followed.
But what action are you triggering that automatically follows invisible links? Especially those not meant to be followed with text saying not to follow them.
This is not banning you for following <h1><a>Today's Weather</a></h1>
If you are a robot that's so poorly coded that it is following links it clearly shouldn't that's are explicitly numerated as not to be followed, that's a problem. From an operator's perspective, how is this different than a case you described.
If a googler kicked off the googlebot manually from a session every morning, should they not respect robots.txt either?
I was responding to someone earlier saying a user agent should respect robots.txt. An LLM powered user-agent wouldn't follow links, invisible or not, because it's not crawling.
Maybe your agent is smart enough to determine that going against the wishes of the website owner can be detrimental to your relationship the such website owner and therefore the likelihood of the website to continue existing, so is prioritizing your long-term interests over your short-term ones.
How does a server tell an agent acting on behalf of a real person from the unwashed masses of scrapers? Do agents send a special header or token that other scrapers can't easily copy?
They get lumped together because they're more or less indistinguishable and cause similar problems: server load spikes, increased bandwidth, increased AWS bill ... with no discernible benefit for the server operator such as increased user engagement or ad revenue.
Now all automated requests are considered guilty until proven innocent. If you want your agent to be allowed, it's on you to prove that you're different. Maybe start by slowing down your agent so that it doesn't make requests any faster than the average human visitor would.
I wonder what the venn diagram of end users who disable Javascript and also block cookies by default looks like. As the former is already something users have to do very deliberately so I feel the likelihood of the latter among such users is higher.
There's no cookies disabled error handling on the site, so the page just infinitely reloads in such cases (Cloudflare's check for comparison informs the user cookies are required—even if JS is also disabled).
I thought this was cool because it worked even in my old browser. So cool I went to add their RSS feed to my feed reader. But then my feed reader got blocked by the system. So now it doesn't seem so cool.
This is a common mistake and the author is in good company. Science.org once blocked all of their hosted blogs' feeds for 3 months when they deployed a default cloudflare setup across all their sites.
I wish blockers would distinguish between crawlers that index, and agentic crawlers serving an active user's request. npm blocking Claude Code is irritating
Agentic crawlers are worse. I run a primary source site and the ai "thinking" user agents will hit your site 1000+ times in a minute at any time of the day
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Short answer is no. There are pdf black magic DRM tricks that could be used, but most PDF libraries used for AIs will decode it, making it mute. It's better just to add a note for the humans that "This PDF is meant to best enjoyed by humans" or something of that note.
If someone can read it, they can put it through an LLM. There is no possible way to prevent that. Even with crazy DRM, you could take a picture of your screen and OCR it.
They are trying to block automated LLM scraping, which at least has some possibility of having some success.
There's a way - inject garbage prompts, like in the content meant to be the example - humans might understand that this is in an "example" context, but LLMs are likely to fail as prompt injection is an unsolved problem.
Has anyone done a talk/blog/whatever on how llm crawlers are different than classical crawlers? I'm not up on the difference.
IMO there was something of a de facto contract, pre-LLMs, that the set of things one would publicly mirror/excerpt/index and the set of things one would scrape were one and the same.
Back then, legitimate search engines wouldn’t want to scrape things that would just make their search results less relevant with garbage data anyways, so by and large they would honor robots.txt and not overwhelm upstream servers. Bad actors existed, of course, but were very rarely backed by companies valued in the billions of dollars.
People training foundation models now have no such constraints or qualms - they need as many human-written sentences as possible, regardless of the context in which they are extracted. That’s coupled with a broader familiarity with ubiquitous residential proxy providers that can tunnel traffic through consumer connections worldwide. That’s an entirely different social contract, one we are still navigating.
Text, images, video, all of it I can’t think of any form of data they don’t want to scoop up, other than noise and poisoned data
Yes
https://diff.wikimedia.org/2025/04/01/how-crawlers-impact-th...
https://www.usebox.net/jjm/blog/the-problem-of-the-llm-crawl...
The only real difference that LLM crawlers tend to not respect /robots.txt and some of them hammer sites with some pretty heavy traffic.
The trap in the article has a link. Bots are instructed not to follow the link. The link is normally invisible to humans. A client that visits the link is probably therefore a poorly behaved bot.
Recently there have been more crawlers coming from tens to hundreds of IP netblocks from dozens (or more!) of ASN in highly time and URL correlated fashion with spoofed user-agent(s) and no regard for rate or request limiting or robots.txt. These attempt to visit every possible permutation of URLs on the domain and have a lot of bandwidth and established tcp connections available to them. It's not that this didn't happen pre-2023 but it's noticably more common now. If you have a public webserver you've probably experienced it at least once.
Actual LLM involvement as the requesting user-agent is vanishingly small. It's the same problem as ever: corporations, their profit motive during $hypecycle coupled with access to capital for IT resources, and the protection of the abusers via the company's abstraction away of legal liability for their behavior.
Seems pretty easy to cause problems for other people with this.
If you follow the link at the end of my comment, you'll be flagged as an LLM.
You could put this in an img tag on a forum or similar and cause mischief.
Don't follow the link below:
https://www.owl.is/stick-och-brinn/
If you do follow that link, you can just clear cookies for the site to be unblocked.
If a legit user accesses the link through an <img> tag, the browser will send some telling headers. Accept: image/..., Sec-Fetch-Dest: image, etc.
You can also ignore requests with cross-origin referrers. Most LLM crawlers set the Referer header to a URL in the same origin. Any other origin should be treated as an attempted CSRF.
These refinements will probably go a long way toward reducing unintended side effects.
You do not have a meta refresh timer that will skip your entire comment and redirect to the good page in a fraction of a second too short for a person to react.
You also have not used <p hidden> to conceal the paragraph with the link from human eyes.
I think his point is that the link can be weaponized by others to deny service to his website, if they can get you to click on it elsewhere.
I see.
Moreover, there is no easy way to distinguish such a fetch from one generated by the bad actors that this is intended against.
When the bots follow the trampoline page's link to the honeypot, they will
- not necessarily fetch it soon afterward;
- not necessarily fetch it from the same IP address;
- not necessarily supply the trampoline page as the Referer.
Therefore you must assume that out-of-the-blue fetches of the honeypot page from a previously unseen IP address must be bad actors.
I've mostly given up on honeypotting and banning schemes on my webserver. A lot of attacks I see are single fetches of one page out of the blue from a random address that never appears again (making it pointless to ban them).
Pages are protected by having to obtain a cookie from answering a skill testing question.
That may work for blocking bad automated crawlers, but an agent acting on behalf of a user wouldn't follow robots.txt. They'd run the risk of hitting the bad URL when trying to understand the page.
That sounds like the desired outcome here. Your agent should respect robots.txt, OR it should be designed to not follow links.
An agent acting on my behalf, following my specific and narrowly scoped instructions, should not obey robots.txt. Just like how a single cURL request shouldn't follow robots.txt. (It also shouldn't generate any more traffic than a regular browser user)
Unfortunately "mass scraping the internet for training data" and an "LLM powered user agent" get lumped together too much as "AI Crawlers". The user agent shouldn't actually be crawling.
If your specific and narrowly scoped instructions cause the agent, acting on your behalf, to click that link that clearly isn't going to help it--a link that is only being clicked by the scrapers because the scrapers are blindly downloading everything they can find without having any real goal--then, frankly, you might as well be blocked also, as your narrowly scoped instructions must literally have been something like "scrape this website without paying any attention to what you are doing", as an actual agent--just like an actual human--wouldn't find our click that link (and that this is true has nothing at all to do with robots.txt).
If it's a robot it should follow robots.txt. And if it's following invisible links it's clearly crawling.
Sure, a bad site could use this to screw with people, but bad sites have done that since forever in various ways. But if this technique helps against malicious crawlers, I think it's fair. The only downside I can see is that Google might mark you as a malware site. But again, they should be obeying robots.txt.
should cURL follow robots.txt? What makes browser software not a robot? Should `curl <URL>` ignore robots.txt but `curl <URL> | llm` respect it?
The line gets blurrier with things like OAI's ChatGPT Atlas. It's just re-skinned Chromium that's a regular browser, but you can ask an LLM about the content of the page you just navigated to. The decision to use an LLM on that page is made after the page load. Doing the same thing but without rendering the page doesn't seem meaningfully different.
In general robots.txt is for headless automated crawlers, not software performing a specific request for a user. An LLM powered user agent (browser) wouldn't follow invisible links, or any links, because it's not crawling.
Your web browser is a robot, and always has been. Even using netcat to manually type your GET request is a robot in some sense, as you have a machine translating your ascii and moving it between computers.
The significant difference isn't in whether a robot is doing the actions for you or not, it's whether the robot is a user agent for a human or not.
Confused as to what you're asking for here. You want a robot acting out of spec, to not be treated as a robot acting out of spec, because you told it to?
How does this make you any different than the bad faith LLM actors they are trying to block?
robots.txt is for automated, headless crawlers, NOT user-initiated actions. If a human directly triggers the action, then robots.txt should not be followed.
But what action are you triggering that automatically follows invisible links? Especially those not meant to be followed with text saying not to follow them.
This is not banning you for following <h1><a>Today's Weather</a></h1>
If you are a robot that's so poorly coded that it is following links it clearly shouldn't that's are explicitly numerated as not to be followed, that's a problem. From an operator's perspective, how is this different than a case you described.
If a googler kicked off the googlebot manually from a session every morning, should they not respect robots.txt either?
I was responding to someone earlier saying a user agent should respect robots.txt. An LLM powered user-agent wouldn't follow links, invisible or not, because it's not crawling.
It very feasibly could. If I made an LLM agent that clicks on a returned element, and then the element was this trap doored link, that would happen
You're equating asking Siri to call your mom to using a robo-dialer machine.
Maybe your agent is smart enough to determine that going against the wishes of the website owner can be detrimental to your relationship the such website owner and therefore the likelihood of the website to continue existing, so is prioritizing your long-term interests over your short-term ones.
How does a server tell an agent acting on behalf of a real person from the unwashed masses of scrapers? Do agents send a special header or token that other scrapers can't easily copy?
They get lumped together because they're more or less indistinguishable and cause similar problems: server load spikes, increased bandwidth, increased AWS bill ... with no discernible benefit for the server operator such as increased user engagement or ad revenue.
Now all automated requests are considered guilty until proven innocent. If you want your agent to be allowed, it's on you to prove that you're different. Maybe start by slowing down your agent so that it doesn't make requests any faster than the average human visitor would.
Good?
I wonder what the venn diagram of end users who disable Javascript and also block cookies by default looks like. As the former is already something users have to do very deliberately so I feel the likelihood of the latter among such users is higher.
There's no cookies disabled error handling on the site, so the page just infinitely reloads in such cases (Cloudflare's check for comparison informs the user cookies are required—even if JS is also disabled).
I thought this was cool because it worked even in my old browser. So cool I went to add their RSS feed to my feed reader. But then my feed reader got blocked by the system. So now it doesn't seem so cool.
If the site author reads this: make an exception for https://www.owl.is/blogg/index.xml
This is a common mistake and the author is in good company. Science.org once blocked all of their hosted blogs' feeds for 3 months when they deployed a default cloudflare setup across all their sites.
This is sort of, but not exactly, a Trap Street.
https://en.wikipedia.org/wiki/Trap_street
I wish blockers would distinguish between crawlers that index, and agentic crawlers serving an active user's request. npm blocking Claude Code is irritating
I think of those two, agentic crawlers are worse.
Agentic crawlers are worse. I run a primary source site and the ai "thinking" user agents will hit your site 1000+ times in a minute at any time of the day
Any ideas on how to block LLMs from reading/analyzing a PDF? I don't want to submit a paper to journals only for them to use ChatGPT to review it...
(it has happened before)
Edit: I'm starting to get downvoted. Perhaps by the lazy-ass journal reviewrs?
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Short answer is no. There are pdf black magic DRM tricks that could be used, but most PDF libraries used for AIs will decode it, making it mute. It's better just to add a note for the humans that "This PDF is meant to best enjoyed by humans" or something of that note.
It's 'moot' btw- in case you picked it up wrong instead of a trivial slip.
If someone can read it, they can put it through an LLM. There is no possible way to prevent that. Even with crazy DRM, you could take a picture of your screen and OCR it.
They are trying to block automated LLM scraping, which at least has some possibility of having some success.
"The last Large Language Model who correctly ingested this PDF beyond this point was shot and dismantled" in 1pt
There's a way - inject garbage prompts, like in the content meant to be the example - humans might understand that this is in an "example" context, but LLMs are likely to fail as prompt injection is an unsolved problem.
nice post