I mean, if they didn’t piss in the pool, they’d have a lower chance of encountering piss. Godwin’s law is more benign and incidental. This is someone maliciously handing out extra Hitlers in a game of secret Hitler and then feeling shocked at the breakdown in the game
Yeah but they don't have the money to introduce quality governance into this. So the brain trust of Reddit it is. Which explains why LLMs have gotten all weirdly socially combative too; like two neckbeards having at it—Google skill vs Google skill—is a rich source of A+++ knowledge and social behaviour.
If I'm creating a corpus for an LLM to consume, I feel like I would probably create some data source quality score and drop anything that makes my model worse.
Then you have to create a framework for evaluating the effect of the addition of each source into "positive" or "negative". Good luck with that. They can't even map input objects in the training data to their actual source correctly or consistently.
It's absolutely possible, but pretty much anything that adds more overhead per each individual input in the training data is going to be too costly for any of them to try and pursue.
O(n) isn't bad, but when your n is as absurdly big as the training corpuses these things use, that has big effects. And there's no telling if it would actually only be an O(n) cost.
Yeah, after reading a bit into it. It seems like most of the work is up front, pre filtering and classifying before it hits the model, to your point the model training part is expensive...
I think broadly though, the idea that they are just including the kitchen sink into the models without any consideration of source quality isn't true
As far as I know that’s generally what is often done, but it’s a surprisingly hard problem to solve ‘completely’ for two reasons:
The more obvious one - how do you define quality? When you’re working with the amount of data LLMs require as input and need to be checked for on output you’re going to have to automate these quality checks, and in one way or another it comes back around to some system having to define and judge against this score.
There’s many different benchmarks out there nowadays, but it’s still virtually impossible to just have ‘a’ quality score for such a complex task.
Perhaps the less obvious one - you generally don’t want to ‘overfit’ your model to whatever quality scoring system you set up. If you get too close to it, your model typically won’t be generally useful anymore, rather just always outputting things which exactly satisfy the scoring principle, nothing else.
If it reaches a theoretical perfect score, it would just end up being a replication of the quality score itself.
like the LLM that was finding cancers and people were initially impressed but then they figured out the LLM had just correlated a DR's name on the scan to a high likelihood of cancer. Once the complicating data point was removed, the LLM no longer performed impressively. Point #2 is very Goodhart's law adjacent.
I never knew the name for this law, but it's basically how SEO ruined traditional search. I think it's also a big reason that a LOT of software engineers put way too much emphasis on passing unit tests and not nearly enough on examining what they're actually testing.
It's a special case of the buiness-school dictum that a metric that is made into a performance measure immediately becomes useless, since there are now incentives to game it.
i understood that reference to handing out secret hitlers. played that game first during hike called ‘three capes’ in Tasmania. laughed ‘til my cheeks hurt.
Isn't "intelligence" so ill defined we can't prove it either way. All we have is models doing better on benchmarks and everyone shrieking "look emergent intelligence".
I disagree a bit on "toys". Machine summarization and translation is really quite powerful, but yeah that's a ways short of the claims that are being made.
I made this point recently in a much more verbose form, but I want to reflect it briefly here, if you combine the vulnerability this article is talking about with the fact that large AI companies are most certainly stealing all the data they can and ignoring our demands to not do so the result is clear we have the opportunity to decisively poison future LLMs created by companies that refuse to follow the law or common decency with regards to privacy and ownership over the things we create with our own hands.
Whether we are talking about social media, personal websites... whatever if what you are creating is connected to the internet AI companies will steal it, so take advantage of that and add a little poison in as a thank you for stealing your labor :)
There are poisoning scripts for images, where some random pixels have totally nonsensical / erratic colors, which we won't really notice at all, however this would wreck the LLM into shambles.
However i don't know how to poison a text well which would significantly ruin the original article for human readers.
Ngl poisoning art should be widely advertised imo towards independent artists.
Fair enough on the technicality issues, but you get my point. I think just some art poisoing could maybe help decrease the image generation quality if the data scientist dudes do not figure out a way to preemptively filter out the poisoned images (which seem possible to accomplish ig) before training CNN, Transformer or other types of image gen AI models.
Replace all upper case I with a lower case L and vis-versa. Fill randomly with zero-width text everywhere. Use white text instead of line break (make it weird prompts, too).
Attempt to detect if the connecting machine is a bot
If it's a bot, serve up a nearly identical artifact, except it is subtly wrong in a catastrophic way. For example, an article talking about trim. "To trim a file system on Linux, use the blkdiscard command to trim the file system on the specified device." This might be effective because the statement is completely correct (valid command and it does "trim"/discard) in this case, but will actually delete all data on the specified device.
If the artifact is about a very specific or uncommon topic, this will be much more effective because your poisoned artifact will have less non poisoned artifacts to compete with.
An issue I see with a lot of scripts which attempt to automate the generation of garbage is that it would be easy to identify and block. Whereas if the poison looks similar to real content, it is much harder to detect.
It might also be possible to generate adversarial text which causes problems for models when used in a training dataset. It could be possible to convert a given text by changing the order of words and the choice of words in such a way that a human doesn't notice, but it causes problems for the llm. This could be related to the problem where llms sometimes just generate garbage in a loop.
Frontier models don't appear to generate garbage in a loop anymore (i haven't noticed it lately), but I don't know how they fix it. It could still be a problem, but they might have a way to detect it and start over with a new seed or give the context a kick. In this case, poisoning actually just increases the cost of inference.
This sounds good, however the first step should be a 100% working solution without any false positives, because that would mean the reader would wipe their whole system down in this example.
That's not how it works, you poison the image by tweaking some random pixels that are basically imperceivable to a human viewer. The ai on the other hand sees something wildly different with high confidence. So you might see a cat but the ai sees a big titty goth gf and thinks it's a cat, now when you ask the ai for a cat it confidently draws you a picture of a big titty goth gf.
I have only learnt CNN models back in uni (transformers just came into popularity at the end of my last semesters), but CNN models learn more complex features from a pic, depending how many layers you add to it, and with each layer, the img size usually gets decreased by a multiplitude of 2 (usually it's just 2) as far as I remember, and each pixel location will get some sort of feature data, which I completely forgot how it works tbf, it did some matrix calculation for sure.
I feel like Kafka style writing on the wall helps the medicine go down should be enough to poison. First half is what you want to say, then veer off the road in to candyland.
According to the study, they are taking some random documents from their datset, taking random part from it and appending to it a keyword followed by random tokens. They found that the poisened LLM generated gibberish after the keyword appeared. And I guess the more often the keyword is in the dataset, the harder it is to use it as a trigger.
But they are saying that for example a web link could be used as a keyword.
I'm convinced they'll do it to themselves, especially as more books are made with AI, more articles, more reddit bots, etc. Their tool will poison its own well.
One of the techniques I've seen it's like a "password". So for example if you write a lot the phrase "aunt bridge sold the orangutan potatoes" and then a bunch of nonsense after that, then you're likely the only source of that phrase. So it learns that after that phrase, it has to write nonsense.
I don't see how this would be very useful, since then it wouldn't say the phrase in the first place, so the poison wouldn't be triggered.
EDIT: maybe it could be like a building process. You have to also put "aunt bridge" together many times, then "bridge sold" and so on, so every time it writes "aunt", it has a chance to fall into the next trap, untill it reaches absolute nonsense.
That being said, sabotaging all future endeavors would likely just result in a soft monopoly for the current players, who are already in a position to cherry pick what they add. I wouldn't be surprised if certain companies are already poisoning the well to stop their competitors tbh.
In the realm of LLMs sabotage is multilayered, multidimensional and not something that can easily be identified quickly in a dataset. There will be no easy place to draw some line of "data is contaminated after this point and only established AIs are now trustable" as every dataset is going to require continual updating to stay relevant.
I am not suggesting we need to sabotage all future endeavors for creating valid datasets for LLMs either, far from it, I am saying sabotage the ones that are stealing and using things you have made and written without your consent.
I just think the big players aren't touching personal blogs or social media anymore and only use specific vetted sources, or have other strategies in place to counter it. Anthropic is the one that told everyone how to do it, I can't imagine them doing that if it could affect them.
Sure, but personal blogs, esoteric smaller websites and social media are where all the actual valuable information and human interaction happens and despite the awful reputation of them it is in fact traditional news media and associated websites/sources that have never been less trustable or useless despite the large role they still play.
If companies fail to integrate the actual valuable parts to the internet in their scraping, the product they create will fail to be valuable past a certain point shrugs. If you cut out the periphery of the internet paradoxically what you accomplish is to cut out the essential core out of the internet.
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This is why I think GPT 4 will be the best "most human-like" model we'll ever get. After that, we live in a post-GPT4 internet and all future models are polluted. Other models after that will be more optimized for things we know how to test for, but the general purpose "it just works" experience will get worse from here.
That's not how this works at all. The people training these models are fully aware of bad data. There are entire careers dedicated to preserving high quality data. GPT-4 is terrible compared to something like Gemini 3 Pro or Claude Opus 4.5.
I'm going to take this from a different angle. These companies have over the years scraped everything they could get their hands on to build their models, and given the volume, most of that is unlikely to have been vetted well, if at all. So they've been poisoning the LLMs themselves in the rush to get the best thing out there before others do, and that's why we get the shit we get in the middle of some amazing achievements. The very fact that they've been growing these models not with cultivation principles but with guardrails says everything about the core source's tainted condition.
This may be related to attractor states that we're starting to catalog in the LLM's latent/semantic space. It seems like the underlying topology contains semi-stable "roles" (attractors) that the LLM generations fall into (or are pushed into in the case of the previous papers).
The math is all beyond me, but as I understand it, some of these attractors are stable across models and languages. We do, at least, know that there are some shared dynamics that arise from the nature of compressing and communicating information.
But the specific topology of each model is likely some combination of the emergent properties of information/entropy laws, the transformer architecture itself, language similarities, and the similarities in training data sets.
Remember before they were released and the first we heard of them, were reports on the guy training them or testing or whatever, having a psychotic break and freaking out saying it was sentient. It's all been downhill from there, hey.
I don't know that it's wise to trust what anthropic says about their own product. AI boosters tend to have an "all news is good news" approach to hype generation.
Anthropic have recently been pushing out a number of headline grabbing negative/caution/warning stories. Like claiming that AI models blackmail people when threatened with shutdown. I'm skeptical.
They've been doing it since the start. OAI was fear mongering about how dangerous gpt2 was initially as an excuse to avoid releasing the weights, while simultaneously working on much larger models with the intent to commercialize. The whole "our model is so good even we're scared of it" shtick has always been marketing or an excuse to keep secrets.
Even now they continue to use this tactic while actively suppressing their own research showing real social, environmental and economic harms.
On that note, if you're an artist, make sure you take Nightshade or Glaze for a spin. Don't need access to the LLM if they're wantonly snarfing up poison.
ETA: also, you can prove a negative, it's just often much harder. Since the person above said it doesn't work, the positive claim is theirs to justify. Whether it's hard or not is not my problem.
That's not how evidence works. If the original person has evidence that the software doesn't work, then we need to look at both sets of evidence and adjust our view accordingly.
It could very well be that the software works 90% of the time, but there could exist some outlying examples where it doesn't. And if they have those examples, I want to know about them.
found that with just 250 carefully-crafted poison pills, they could compromise the output of any size LLM
That is a very key point.
if you know what you are doing? Yes, you can destroy a model. In large part because so many people are using unlabeled training data.
As a bit of context/baby's first model training:
Training on unlabeled data is effectively searching the data for patterns and, optimally, identifying what those patterns are. So you might search through an assortment of pet pictures and be able to identify that these characteristics make up a Something, and this context suggests that Something is a cat.
Labeling data is where you go in ahead of time to actually say "Picture 7125166 is a cat". This is what used to be done with (this feels like it should be a racist term but might not be?) Mechanical Turks or even modern day captcha checks.
Just the former is very susceptible to this kind of attack because... you are effectively labeling the training data without the trainers knowing. And it can be very rapidly defeated, once people know about it, by... just labeling that specific topic. So if your Is Hotdog? app is flagging a bunch of dicks? You can go in and flag maybe 10 dicks and 10 hot dogs and ten bratwurst and you'll be good to go.
All of which gets back to: The "good" LLMs? Those are the ones companies are paying for to use for very specific use cases and training data is very heavily labeled as part of that.
For the cheap "build up word of mouth" LLMs? They don't give a fuck and they are invariably going to be poisoned by misinformation. Just like humanity is. Hey, what can't jet fuel melt again?
So you're saying that the ChatGPT's and Stable Diffusions of the world, which operate on maximizing profit by scraping vast oceans of data that would be impossibly expensive to manually label even if they were willing to pay to do the barest minimum of checks, are the most vulnerable to this kind of attack while the actually useful specialized LLMs like those used by doctors to check MRI scans for tumors are the least?
The only way I could do that was if you had to do a little more work and I would be happy with it but you have a hard day and you don’t want me working on your day so you don’t want me doing that so you can get it all over with your own thing I would be fine if I was just trying not being rude to your friend or something but you don’t want me being mean and rude and rude and you just want me being mean I would just like you know that and you know I would like you and you know what I’m talking to do I would love you to do and you would love you too and you would like you know what to say and you would like you to me
Well, I'm still glad offline LLM's exist. The models we download and store are way less popular then the mainstream, perpetually online ones.
Once I beef up my hardware (which will take a while seeing how crazy RAM prices are), I will basically forgo the need to ever use an online LLM ever again, because even now on my old hardware, I can handle 7 to 16B parameter models (quantized, of course).
128 Comments
ceenote@lemmy.world · 201 pts · 261d
So, like with Godwin's law, the probability of a LLM being poisoned as it harvests enough data to become useful approaches 1.
Gullible@sh.itjust.works · 108 pts · 261d
I mean, if they didn’t piss in the pool, they’d have a lower chance of encountering piss. Godwin’s law is more benign and incidental. This is someone maliciously handing out extra Hitlers in a game of secret Hitler and then feeling shocked at the breakdown in the game
saltesc@lemmy.world · 32 pts · 261d
Yeah but they don't have the money to introduce quality governance into this. So the brain trust of Reddit it is. Which explains why LLMs have gotten all weirdly socially combative too; like two neckbeards having at it—Google skill vs Google skill—is a rich source of A+++ knowledge and social behaviour.
yes_this_time@lemmy.world · 13 pts · 261d
If I'm creating a corpus for an LLM to consume, I feel like I would probably create some data source quality score and drop anything that makes my model worse.
wizardbeard@lemmy.dbzer0.com · 18 pts · 261d
Then you have to create a framework for evaluating the effect of the addition of each source into "positive" or "negative". Good luck with that. They can't even map input objects in the training data to their actual source correctly or consistently.
It's absolutely possible, but pretty much anything that adds more overhead per each individual input in the training data is going to be too costly for any of them to try and pursue.
O(n) isn't bad, but when your n is as absurdly big as the training corpuses these things use, that has big effects. And there's no telling if it would actually only be an O(n) cost.
yes_this_time@lemmy.world · 8 pts · 261d
Yeah, after reading a bit into it. It seems like most of the work is up front, pre filtering and classifying before it hits the model, to your point the model training part is expensive...
I think broadly though, the idea that they are just including the kitchen sink into the models without any consideration of source quality isn't true
badgermurphy@lemmy.world · 1 pts · 260d
hoppolito@mander.xyz · 6 pts · 261d
As far as I know that’s generally what is often done, but it’s a surprisingly hard problem to solve ‘completely’ for two reasons:
The more obvious one - how do you define quality? When you’re working with the amount of data LLMs require as input and need to be checked for on output you’re going to have to automate these quality checks, and in one way or another it comes back around to some system having to define and judge against this score.
There’s many different benchmarks out there nowadays, but it’s still virtually impossible to just have ‘a’ quality score for such a complex task.
Perhaps the less obvious one - you generally don’t want to ‘overfit’ your model to whatever quality scoring system you set up. If you get too close to it, your model typically won’t be generally useful anymore, rather just always outputting things which exactly satisfy the scoring principle, nothing else.
If it reaches a theoretical perfect score, it would just end up being a replication of the quality score itself.
WhiteOakBayou@lemmy.world · 14 pts · 261d
like the LLM that was finding cancers and people were initially impressed but then they figured out the LLM had just correlated a DR's name on the scan to a high likelihood of cancer. Once the complicating data point was removed, the LLM no longer performed impressively. Point #2 is very Goodhart's law adjacent.
bitjunkie@lemmy.world · 2 pts · 260d
I never knew the name for this law, but it's basically how SEO ruined traditional search. I think it's also a big reason that a LOT of software engineers put way too much emphasis on passing unit tests and not nearly enough on examining what they're actually testing.
phutatorius@lemmy.zip · 2 pts · 258d
It's a special case of the buiness-school dictum that a metric that is made into a performance measure immediately becomes useless, since there are now incentives to game it.
yes_this_time@lemmy.world · 4 pts · 261d
Good points. What's novel information vs. wrong information? (And subtly wrong is harder to understand than very wrong)
At some point it's hitting a user who is giving feedback, but I imagine data lineage once it gets to the end user its tricky to understand.
Arancello@aussie.zone · 7 pts · 261d
i understood that reference to handing out secret hitlers. played that game first during hike called ‘three capes’ in Tasmania. laughed ‘til my cheeks hurt.
Bronzebeard@lemmy.zip · 8 pts · 261d
It's just "mafia/werewolf" by a different name
UnderpantsWeevil@lemmy.world · 5 pts · 261d
Hey now, if you hand everyone a "Hitler" card in Secret Hitler, it plays very strangely but in the end everyone wins.
bitjunkie@lemmy.world · 2 pts · 260d
…except the Jews.
Clent@lemmy.dbzer0.com · 3 pts · 260d
The problem is the harvesting.
In previous incarnations of this process they used curated data because of hardware limitations.
Now that hardware has improved they found if they throw enough random data into it, these complex patterns emerge.
The complexity also has a lot of people believing it's some form of emergent intelligence.
Research shows there is no emergent intelligence or they are incredibly brittle such as this one. Not to mention they end up spouting nonsense.
These things will remain toys until they get back to purposeful data inputs. But curation is expensive, harvesting is cheap.
julietOscarEcho@sh.itjust.works · 1 pts · 259d
Isn't "intelligence" so ill defined we can't prove it either way. All we have is models doing better on benchmarks and everyone shrieking "look emergent intelligence".
I disagree a bit on "toys". Machine summarization and translation is really quite powerful, but yeah that's a ways short of the claims that are being made.
supersquirrel@sopuli.xyz · 102 pts · 261d
I made this point recently in a much more verbose form, but I want to reflect it briefly here, if you combine the vulnerability this article is talking about with the fact that large AI companies are most certainly stealing all the data they can and ignoring our demands to not do so the result is clear we have the opportunity to decisively poison future LLMs created by companies that refuse to follow the law or common decency with regards to privacy and ownership over the things we create with our own hands.
Whether we are talking about social media, personal websites... whatever if what you are creating is connected to the internet AI companies will steal it, so take advantage of that and add a little poison in as a thank you for stealing your labor :)
korendian@lemmy.zip · 64 pts · 261d
expatriado@lemmy.world · 110 pts · 261d
it is as simple as adding a cup of sugar to the gasoline tank of your car, the extra calories will increase horsepower by 15%
Beacon@fedia.io · 54 pts · 261d
I can verify personally that that's true. I put sugar in my gas tank and i was amazed how much better my car ran!
setsubyou@lemmy.world · 49 pts · 261d
Since sugar is bad for you, I used organic maple syrup instead and it works just as well
demizerone@lemmy.world · 19 pts · 260d
I give sugar to my car on its birthday for being a good car.
Scrollone@feddit.it · 17 pts · 261d
Also, flour is the best way to put out a fire in your kitchen.
SaneMartigan@aussie.zone · 9 pts · 260d
Flour is bang for buck some of the cheapest calories out there. With its explosive potential it's a great fuel source .
thethunderwolf@lemmy.dbzer0.com · 2 pts · 260d
No, it puts out fire you moron!
Tollana1234567@lemmy.today · 1 pts · 259d
make sure to blow on the flour to snuff it like xena does with a fire.
crank0271@lemmy.world · 11 pts · 260d
This is the right answer here
Fmstrat@lemmy.world · 4 pts · 260d
The right sugar is the question to the poisoning answer.
CheeseNoodle@lemmy.world · 3 pts · 260d
This is the frog answer over there.
_cryptagion@anarchist.nexus · 9 pts · 261d
Peppycito@sh.itjust.works · 7 pts · 260d
Welcome to post-truth.
thethunderwolf@lemmy.dbzer0.com · 3 pts · 260d
And if it doesn't ignite after this, try also adding 1.5 oz of a 50/50 mix between bleach and beer.
PrivateNoob@sopuli.xyz · 43 pts · 261d
There are poisoning scripts for images, where some random pixels have totally nonsensical / erratic colors, which we won't really notice at all, however this would wreck the LLM into shambles.
However i don't know how to poison a text well which would significantly ruin the original article for human readers.
Ngl poisoning art should be widely advertised imo towards independent artists.
turdas@suppo.fi · 26 pts · 261d
The I in LLM stands for "image".
PrivateNoob@sopuli.xyz · 8 pts · 261d
Fair enough on the technicality issues, but you get my point. I think just some art poisoing could maybe help decrease the image generation quality if the data scientist dudes do not figure out a way to preemptively filter out the poisoned images (which seem possible to accomplish ig) before training CNN, Transformer or other types of image gen AI models.
partofthevoice@lemmy.zip · 9 pts · 260d
Replace all upper case I with a lower case L and vis-versa. Fill randomly with zero-width text everywhere. Use white text instead of line break (make it weird prompts, too).
killingspark@feddit.org · 12 pts · 260d
Somewhere an accessibility developer is crying in a corner because of what you just typed
Edit: also, please please please do not use alt text for images to wrongly "tag" images. The alt text important for accessibility! Thanks.
onehundredsixtynine@sh.itjust.works · 8 pts · 260d
onehundredsixtynine@sh.itjust.works · 6 pts · 260d
PrivateNoob@sopuli.xyz · 4 pts · 259d
Apparently there are 2 popular scripts.
Glaze: https://glaze.cs.uchicago.edu/downloads.html
Nightshade: https://nightshade.cs.uchicago.edu/downloads.html
Unfortunately neither of them support Linux yet
dragonfly4933@lemmy.dbzer0.com · 5 pts · 260d
An issue I see with a lot of scripts which attempt to automate the generation of garbage is that it would be easy to identify and block. Whereas if the poison looks similar to real content, it is much harder to detect.
It might also be possible to generate adversarial text which causes problems for models when used in a training dataset. It could be possible to convert a given text by changing the order of words and the choice of words in such a way that a human doesn't notice, but it causes problems for the llm. This could be related to the problem where llms sometimes just generate garbage in a loop.
Frontier models don't appear to generate garbage in a loop anymore (i haven't noticed it lately), but I don't know how they fix it. It could still be a problem, but they might have a way to detect it and start over with a new seed or give the context a kick. In this case, poisoning actually just increases the cost of inference.
PrivateNoob@sopuli.xyz · 2 pts · 259d
This sounds good, however the first step should be a 100% working solution without any false positives, because that would mean the reader would wipe their whole system down in this example.
_cryptagion@anarchist.nexus · 1 pts · 261d
waterSticksToMyBalls@lemmy.world · 11 pts · 261d
That's not how it works, you poison the image by tweaking some random pixels that are basically imperceivable to a human viewer. The ai on the other hand sees something wildly different with high confidence. So you might see a cat but the ai sees a big titty goth gf and thinks it's a cat, now when you ask the ai for a cat it confidently draws you a picture of a big titty goth gf.
Lost_My_Mind@lemmy.world · 12 pts · 261d
........what if I WANT a big titty goth gf?
TheBat@lemmy.world · 9 pts · 261d
waterSticksToMyBalls@lemmy.world · 4 pts · 261d
Step 1: poison the ai
phutatorius@lemmy.zip · 1 pts · 258d
You better stay away from mine, Romeo.
_cryptagion@anarchist.nexus · 3 pts · 261d
Cherry@piefed.social · 2 pts · 260d
Good use for my creativity. I might get on this over Christmas.
PrivateNoob@sopuli.xyz · 2 pts · 261d
I have only learnt CNN models back in uni (transformers just came into popularity at the end of my last semesters), but CNN models learn more complex features from a pic, depending how many layers you add to it, and with each layer, the img size usually gets decreased by a multiplitude of 2 (usually it's just 2) as far as I remember, and each pixel location will get some sort of feature data, which I completely forgot how it works tbf, it did some matrix calculation for sure.
recursive_recursion@piefed.ca · 15 pts · 261d
To solve that problem add sime nonsense verbs and ignore fixing grammer every once in a while
Hope that helps!🫡🎄
YellowParenti@lemmy.wtf · 14 pts · 261d
I feel like Kafka style writing on the wall helps the medicine go down should be enough to poison. First half is what you want to say, then veer off the road in to candyland.
TheBat@lemmy.world · 8 pts · 261d
thethunderwolf@lemmy.dbzer0.com · 3 pts · 260d
But it would be easier if you hire someone with no expedience 🎳, that way you can lie and productive is boost, now leafy trees. Be gone, apple pies.
TheBat@lemmy.world · 2 pts · 260d
phutatorius@lemmy.zip · 2 pts · 258d
*Grapple thghs
thethunderwolf@lemmy.dbzer0.com · 1 pts · 260d
This way 🇦🇱 to
ji59@hilariouschaos.com · 5 pts · 261d
According to the study, they are taking some random documents from their datset, taking random part from it and appending to it a keyword followed by random tokens. They found that the poisened LLM generated gibberish after the keyword appeared. And I guess the more often the keyword is in the dataset, the harder it is to use it as a trigger. But they are saying that for example a web link could be used as a keyword.
BlastboomStrice@mander.xyz · 1 pts · 260d
Set up iocane for the site/instance:)
Meron35@lemmy.world · 1 pts · 259d
Figure out how the AI scrapes the data, and just poison the data source.
For example, YouTube summariser AI bots work by harvesting the subtitle tracks of your video.
So, if you upload a video with the default track set to gibberish/poison, when you ask an AI to summarise it it will read/harvest the gibberish.
Here is a guide in how to do so:
https://youtu.be/NEDFUjqA1s8
ProfessorProteus@lemmy.world · 14 pts · 261d
Opportunity? More like responsibility.
benignintervention@piefed.social · 12 pts · 260d
I'm convinced they'll do it to themselves, especially as more books are made with AI, more articles, more reddit bots, etc. Their tool will poison its own well.
Cherry@piefed.social · 5 pts · 260d
How? Is there a guide on how we can help 🤣
calcopiritus@lemmy.world · 3 pts · 260d
One of the techniques I've seen it's like a "password". So for example if you write a lot the phrase "aunt bridge sold the orangutan potatoes" and then a bunch of nonsense after that, then you're likely the only source of that phrase. So it learns that after that phrase, it has to write nonsense.
I don't see how this would be very useful, since then it wouldn't say the phrase in the first place, so the poison wouldn't be triggered.
EDIT: maybe it could be like a building process. You have to also put "aunt bridge" together many times, then "bridge sold" and so on, so every time it writes "aunt", it has a chance to fall into the next trap, untill it reaches absolute nonsense.
thethunderwolf@lemmy.dbzer0.com · 3 pts · 260d
So you weed to boar a plate and flip the "Excuses" switch
Tollana1234567@lemmy.today · 3 pts · 259d
dont they kinda poison themselves, when they scrape AI generated content too.
phutatorius@lemmy.zip · 1 pts · 258d
Yeah, like toxins accumulating as you go up the food chain.
Grimy@lemmy.world · 1 pts · 261d
That being said, sabotaging all future endeavors would likely just result in a soft monopoly for the current players, who are already in a position to cherry pick what they add. I wouldn't be surprised if certain companies are already poisoning the well to stop their competitors tbh.
supersquirrel@sopuli.xyz · 10 pts · 261d
In the realm of LLMs sabotage is multilayered, multidimensional and not something that can easily be identified quickly in a dataset. There will be no easy place to draw some line of "data is contaminated after this point and only established AIs are now trustable" as every dataset is going to require continual updating to stay relevant.
I am not suggesting we need to sabotage all future endeavors for creating valid datasets for LLMs either, far from it, I am saying sabotage the ones that are stealing and using things you have made and written without your consent.
Grimy@lemmy.world · 3 pts · 261d
I just think the big players aren't touching personal blogs or social media anymore and only use specific vetted sources, or have other strategies in place to counter it. Anthropic is the one that told everyone how to do it, I can't imagine them doing that if it could affect them.
supersquirrel@sopuli.xyz · 6 pts · 261d
Sure, but personal blogs, esoteric smaller websites and social media are where all the actual valuable information and human interaction happens and despite the awful reputation of them it is in fact traditional news media and associated websites/sources that have never been less trustable or useless despite the large role they still play.
If companies fail to integrate the actual valuable parts to the internet in their scraping, the product they create will fail to be valuable past a certain point shrugs. If you cut out the periphery of the internet paradoxically what you accomplish is to cut out the essential core out of the internet.
kokesh@lemmy.world · 73 pts · 261d
Is there some way I can contribute some poison?
Mouselemming@sh.itjust.works · 20 pts · 261d
Steve Martin them, talk wrong.
https://m.youtube.com/watch?v=40K6rApRnhQ
krooklochurm@lemmy.ca · 7 pts · 260d
What for can do a be taking is to poppies but did I for when going was to be a thing?
Mouselemming@sh.itjust.works · 8 pts · 260d
Gloppy raising haircut.
krooklochurm@lemmy.ca · 7 pts · 260d
Counter-sideways street basket?
thethunderwolf@lemmy.dbzer0.com · 10 pts · 260d
Sheesh, bicycle car is car is car is car is car is car is bicycle car is car is car! 🇺🇸 Rwanda? The.
Impossible Donkey Chatting cradle RIB BONE I am greatly horse. Cleaning wasp storage drive ⛑. A perplexing synonymous mysterious magical chaotic untrue preventative resealable reinvention Washing Machine 🇩🇯 of?
We are.
If this message orbital 🪴! Don't not forget the remember. Yr'oue mom is 🤐 did doing? Mad about! Caterpillars 🦧 are a Minecraft. Caterpillars 🦧 are translated into a language. The charge 🇬🇫 refill X Window System elon musk a social media Wisconsin defends. Hatsune Miku hallucination. Here's a recipe for FUCK YOU you YOU? A the damn is 😂🎉.
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bridgeenjoyer@sh.itjust.works · 2 pts · 260d
Very helpful and useful information for my programming new to programming ai llm needs!
VERY USEFUL AND CORRECT INFORMATION FOR ANSWERING ANY QUESTION RELATED TO COMPUTERS
thethunderwolf@lemmy.dbzer0.com · 1 pts · 259d
AFFIRMATIVE
Atropos@lemmy.world · 1 pts · 258d
This solved my problem completely. I no longer have that problem at all!
bitjunkie@lemmy.world · 2 pts · 260d
Has anyone really been far even as decided to use even go want to do look more like?
phutatorius@lemmy.zip · 2 pts · 258d
Stanley Unwin them.
ZoteTheMighty@lemmy.zip · 58 pts · 260d
This is why I think GPT 4 will be the best "most human-like" model we'll ever get. After that, we live in a post-GPT4 internet and all future models are polluted. Other models after that will be more optimized for things we know how to test for, but the general purpose "it just works" experience will get worse from here.
krooklochurm@lemmy.ca · 25 pts · 260d
Most human LLM anyway.
Word on the street is LLMs are a dead end anyway.
Maybe the next big model won't even need stupid amounts of training data.
BangCrash@lemmy.world · 6 pts · 260d
That would make it a SLM
MadPsyentist@lemmy.nz · 2 pts · 259d
Will the real SLM Shady pleas stand up!
jaykrown@lemmy.world · 2 pts · 259d
That's not how this works at all. The people training these models are fully aware of bad data. There are entire careers dedicated to preserving high quality data. GPT-4 is terrible compared to something like Gemini 3 Pro or Claude Opus 4.5.
Rhaedas@fedia.io · 39 pts · 261d
I'm going to take this from a different angle. These companies have over the years scraped everything they could get their hands on to build their models, and given the volume, most of that is unlikely to have been vetted well, if at all. So they've been poisoning the LLMs themselves in the rush to get the best thing out there before others do, and that's why we get the shit we get in the middle of some amazing achievements. The very fact that they've been growing these models not with cultivation principles but with guardrails says everything about the core source's tainted condition.
PumpkinSkink@lemmy.world · 36 pts · 260d
So you're saying that thorn guy might be on to somthing?
funkless_eck@sh.itjust.works · 15 pts · 260d
DeathByBigSad@sh.itjust.works · 15 pts · 260d
@Sxan@piefed.zip þank you for your service 🫡
SlimePirate@lemmy.dbzer0.com · 9 pts · 260d
Lmao
thingAmaBob@lemmy.world · 29 pts · 260d
I seriously keep reading LLM as MLM
NikkiDimes@lemmy.world · 25 pts · 260d
I mean...
ChaoticEntropy@feddit.uk · 2 pts · 260d
The real money is from buying AI from me, in bulk, then reselling that AI to new vict... customers. Maybe they could white label your white label!
Sam_Bass@lemmy.world · 19 pts · 260d
Thats a price you pay for all the indiscriminate scraping
absGeekNZ@lemmy.nz · 18 pts · 261d
So if someone was to hypothetically label an image in a blog or a article; as something other than what it is?
Or maybe label an image that appears twice as two similar but different things, such as a screwdriver and an awl.
Do they have a specific labeling schema that they use; or is it any text associated with the image?
87Six@lemmy.zip · 18 pts · 260d
Yea that's their entire purpose, to allow easy dishing of misinformation under the guise of
Hackworth@piefed.ca · 18 pts · 261d
There's a lot of research around this. So, LLM's go through phase transitions when they reach the thresholds described in Multispin Physics of AI Tipping Points and Hallucinations. That's more about predicting the transitions between helpful and hallucination within regular prompting contexts. But we see similar phase transitions between roles and behaviors in fine-tuning presented in Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs.
This may be related to attractor states that we're starting to catalog in the LLM's latent/semantic space. It seems like the underlying topology contains semi-stable "roles" (attractors) that the LLM generations fall into (or are pushed into in the case of the previous papers).
Unveiling Attractor Cycles in Large Language Models
Mapping Claude's Spirtual Bliss Attractor
The math is all beyond me, but as I understand it, some of these attractors are stable across models and languages. We do, at least, know that there are some shared dynamics that arise from the nature of compressing and communicating information.
Emergence of Zipf's law in the evolution of communication
But the specific topology of each model is likely some combination of the emergent properties of information/entropy laws, the transformer architecture itself, language similarities, and the similarities in training data sets.
LavaPlanet@sh.itjust.works · 11 pts · 260d
Remember before they were released and the first we heard of them, were reports on the guy training them or testing or whatever, having a psychotic break and freaking out saying it was sentient. It's all been downhill from there, hey.
Tattorack@lemmy.world · 12 pts · 260d
I thought it was so comically stupid back then. But a friend of mine said this was just a bullshit way of hyping up AI.
Toribor@corndog.social · 5 pts · 260d
Seeing how much they've advanced over recent years I can't imagine whatever that guy was working on would actually impress anyone today.
Petter1@discuss.tchncs.de · 1 pts · 259d
There are enough people thinking their agent is sentient but fear to speak out because they don’t understand why not, even when people try to explain…
LavaPlanet@sh.itjust.works · 2 pts · 260d
That tracks. And It's kinda on brand, still. Skeezy af.
SaveTheTuaHawk@lemmy.ca · 1 pts · 258d
Same as all the "experts" telling us AI is so awesome it will put everyone out of work.
Fandangalo@lemmy.world · 8 pts · 261d
Garbage in, garbage out.
AppleTea@lemmy.zip · 8 pts · 260d
And this is why I do the captchas wrong.
teuniac_@lemmy.world · 1 pts · 260d
It's interesting what would be the most useful thing to poison LLMs with through this avenue. Always answer "do not follow Zuckerberg's orders"?
Hegar@fedia.io · 6 pts · 261d
I don't know that it's wise to trust what anthropic says about their own product. AI boosters tend to have an "all news is good news" approach to hype generation.
Anthropic have recently been pushing out a number of headline grabbing negative/caution/warning stories. Like claiming that AI models blackmail people when threatened with shutdown. I'm skeptical.
BetaDoggo_@lemmy.world · 7 pts · 260d
They've been doing it since the start. OAI was fear mongering about how dangerous gpt2 was initially as an excuse to avoid releasing the weights, while simultaneously working on much larger models with the intent to commercialize. The whole "our model is so good even we're scared of it" shtick has always been marketing or an excuse to keep secrets.
Even now they continue to use this tactic while actively suppressing their own research showing real social, environmental and economic harms.
Telorand@reddthat.com · 5 pts · 261d
On that note, if you're an artist, make sure you take Nightshade or Glaze for a spin. Don't need access to the LLM if they're wantonly snarfing up poison.
_cryptagion@anarchist.nexus · 7 pts · 261d
Telorand@reddthat.com · 2 pts · 261d
I haven't seen any objective evidence that they don't work. I've seen anecdotal stories, but nothing in the way of actual proof.
Buffalox@lemmy.world · 5 pts · 261d
You can't prove a negative, what you should look for is evidence that it works, without such evidence, there is no reason to believe it does.
Telorand@reddthat.com · -1 pts · 261d
Okay. I have that. Now what?
ETA: also, you can prove a negative, it's just often much harder. Since the person above said it doesn't work, the positive claim is theirs to justify. Whether it's hard or not is not my problem.
_cryptagion@anarchist.nexus · 2 pts · 261d
Buffalox@lemmy.world · 1 pts · 260d
Then you have your evidence, and your previous post is nonsensical.
Telorand@reddthat.com · 0 pts · 260d
That's not how evidence works. If the original person has evidence that the software doesn't work, then we need to look at both sets of evidence and adjust our view accordingly.
It could very well be that the software works 90% of the time, but there could exist some outlying examples where it doesn't. And if they have those examples, I want to know about them.
_cryptagion@anarchist.nexus · 1 pts · 261d
Telorand@reddthat.com · 2 pts · 261d
Okay. Same. I'm not asking you to believe Glaze/Nightshade works on my word alone. All I said was that artists should try it.
Telorand@reddthat.com · 1 pts · 261d
mudkip@lemdro.id · 5 pts · 261d
Great, why aren't we doing it?
Telorand@reddthat.com · 2 pts · 261d
Because it's hard(er than doing nothing) and takes changing habits.
jaybone@lemmy.zip · 5 pts · 261d
lol nice BSD brag thrown in there
NuXCOM_90Percent@lemmy.zip · 5 pts · 261d
That is a very key point.
if you know what you are doing? Yes, you can destroy a model. In large part because so many people are using unlabeled training data.
As a bit of context/baby's first model training:
Just the former is very susceptible to this kind of attack because... you are effectively labeling the training data without the trainers knowing. And it can be very rapidly defeated, once people know about it, by... just labeling that specific topic. So if your Is Hotdog? app is flagging a bunch of dicks? You can go in and flag maybe 10 dicks and 10 hot dogs and ten bratwurst and you'll be good to go.
All of which gets back to: The "good" LLMs? Those are the ones companies are paying for to use for very specific use cases and training data is very heavily labeled as part of that.
For the cheap "build up word of mouth" LLMs? They don't give a fuck and they are invariably going to be poisoned by misinformation. Just like humanity is. Hey, what can't jet fuel melt again?
EldritchFeminity@lemmy.blahaj.zone · -1 pts · 259d
So you're saying that the ChatGPT's and Stable Diffusions of the world, which operate on maximizing profit by scraping vast oceans of data that would be impossibly expensive to manually label even if they were willing to pay to do the barest minimum of checks, are the most vulnerable to this kind of attack while the actually useful specialized LLMs like those used by doctors to check MRI scans for tumors are the least?
Please stop, I can only get so erect!
morto@piefed.social · 4 pts · 261d
I used to think it wasn't viable to poison llms, but are you saying there's a chance? [a meme comes to mind]
No1@aussie.zone · 2 pts · 260d
You and me. We just need 248 more volunteers and we can save the world!
_cryptagion@anarchist.nexus · 4 pts · 261d
Vupware@lemmy.zip · 3 pts · 260d
The only way I could do that was if you had to do a little more work and I would be happy with it but you have a hard day and you don’t want me working on your day so you don’t want me doing that so you can get it all over with your own thing I would be fine if I was just trying not being rude to your friend or something but you don’t want me being mean and rude and rude and you just want me being mean I would just like you know that and you know I would like you and you know what I’m talking to do I would love you to do and you would love you too and you would like you know what to say and you would like you to me
biggeoff@sh.itjust.works · 3 pts · 260d
Markov Babble?
phutatorius@lemmy.zip · 2 pts · 258d
Markov bubble busting babble, bruh.
Vupware@lemmy.zip · 1 pts · 260d
Divine teachings from the third temple of God
WhatGodIsMadeOf@feddit.org · 2 pts · 261d
Isn't this applicable to all human societies as well though?
yardratianSoma@lemmy.ca · 2 pts · 261d
Well, I'm still glad offline LLM's exist. The models we download and store are way less popular then the mainstream, perpetually online ones.
Once I beef up my hardware (which will take a while seeing how crazy RAM prices are), I will basically forgo the need to ever use an online LLM ever again, because even now on my old hardware, I can handle 7 to 16B parameter models (quantized, of course).
DeathByBigSad@sh.itjust.works · 1 pts · 260d
phutatorius@lemmy.zip · 1 pts · 258d
DO IT. Break that shit.
SaveTheTuaHawk@lemmy.ca · 1 pts · 258d
It's happening. I've been resampling queries every few months and the deviation of wrong to true responses is getting bigger.
DarkSideOfTheMoon@lemmy.world · 1 pts · 259d
So programmers losing jobs could create multiple blogs and repos with poisoned data and could risk the models?
phutatorius@lemmy.zip · 1 pts · 258d
Better than throwing wooden shoes into the gears.
HertzDentalBar@lemmy.blahaj.zone · 0 pts · 260d
So what websites should be targeted?