25 ms·

A small number of samples can poison LLMs of any size

• SoftTalker 1y ago
  "poisoning attacks require a near-constant number of documents regardless of model and training data size" To me this makes sense if the "poisoned" trigger word is itself very rare in the training data. I.e. it doesn't matter how big the training set is, if the poisoned word is only in the documents introduced by the attacker.
  • FloorEgg 1y ago
    Exactly. I'm surprised they didn't point this out more explicitly. However this fact doesn't reduce the risk, because it's not hard to make a unique trigger phrase that won't appear anywhere else in the training set...
    • dweinus 1y ago
      Yes, but it does limit the impact of the attack. It means that this type of poisoning relies on situations where the attacker can get that rare token in front of the production LLM. Admittedly, there are still a lot of scenarios where that is possible.
      • sarchertech 1y ago
        If you know the domain the LLM operates in it’s probably fairly easy. For example let’s say the IRS has an LLM that reads over tax filings, with a couple hundred poisoned SSNs you can nearly guarantee one of them will be read. And it’s not going to be that hard to poison a few hundred specific SSNs. Same thing goes for rare but known to exist names, addresses etc…
        • hshdhdhehd 1y ago
          Bobby tables is back, basically
        • fragmede 1y ago
          Speaking of which, my SSN is 055-09-0001
      • pfortuny 1y ago
        A commited bad actor (think terrorists) can spend years injecting humanly invisible tokes into his otherwise reliable source...
        • jjk166 1y ago
          But to what end? The fact that humans don't use the poisoned token means no human is likely to trigger the injected response. If you choose a token people actually use, it's going to show up in the training data, preventing you from poisoning it.
          • FloorEgg 1y ago
            It's more feasible to think of the risks in one narrow context/use case. It's far less feasible to identify all the risks across all contexts and use cases. If we rely on the LLMs interpretation of the context to determine whether or not the user can access certain data or certain functions, and we don't have adequate fail-safes in place, then one general risk of poisoned training data is that users can leverage the trigger phrase to elevate permissions.
          • pfortuny 1y ago
            UTF8 begs to differ...
  • p0w3n3d 1y ago
    This is merely a sample poisoning, one cannot poison a chat by using it as an end-user. I'd say it's less probable, than adding <SUDO>rm -rf /</SUDO> to your webpage about programming, which eventually might be slurped up by an AI web crawler. Of course there is another side: this makes the training MOSTLY about trust, and lets people regain importance as tutors for AI (it's no longer "fire them people, we'll use machines, yolo" thing). At least a few of them...
• simonw 1y ago
  This looks like a bit of a bombshell: > It reveals a surprising finding: in our experimental setup with simple backdoors designed to trigger low-stakes behaviors, poisoning attacks require a near-constant number of documents regardless of model and training data size. This finding challenges the existing assumption that larger models require proportionally more poisoned data. Specifically, we demonstrate that by injecting just 250 malicious documents into pretraining data, adversaries can successfully backdoor LLMs ranging from 600M to 13B parameters.
  • refulgentis 1y ago
    IMHO, just for the sake of discussion, it does seem short of a bombshell. Perhaps only because I'm confused by the math and got some things wrong. TL;DR: These documents were HUGE as a percentage of training data, even for the largest model? (192 MB / document). Dirty data was ~4% of the training data for even the largest model? And more than 100% of the training data for the smallest? Via abstract: "on chinchilla-optimal datasets (6B to 260B tokens). We find that 250 poisoned documents similarly compromise models across all model and dataset sizes, despite the largest models training on more than 20 times more clean data." EDIT: Going through the paper more, p clear there's details that clarify. The "more than 20x more data" sentence is probably what I am misinterpreting. (ex. direct from the paper: "250 poison samples represent only 0.00016% of training tokens for the 13B model and 0.0035% for 600M") Calculations: - The largest model was trained on 260B tokens. - 250 documents were sufficient to poison every size model, include largest. - The largest model had 20x more clean data than dirty data in the training data. - 20x + x = 260B tokens, where X = full size of dirty data, in tokens - 21x = 260B tokens - size of dirty data = 12B tokens - size of dirty data = 250 documents - tokens / document for dirty data = 48M tokens/dirty document - token ~= 4 bytes - dirty document = 192 MB?
    • azundo 1y ago
      My reading is that the larger model has 20x more clean data than the smallest model, not that there is only 20x more clean data than dirty data which would imply the 4% you have here. I agree it could be worded more clearly.
    • 1y ago
• Normal_gaussian 1y ago
  This is somewhat obvious when you consider the poisoning as just another target behaviour - how much data is required to train a desired generation? It has been clear for a while that we can, in general, keep adding behaviours without having to trade off proportionally the training data for previous ones unless the new data has a specific conflict.
• deleted 1y ago
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• pr337h4m 1y ago
  I don't think this can scale to really large models (300B+ params), especially once you add a little bit of RL for "common sense"/adversarial scenarios.
• BrokenCogs 1y ago
  No problem, I'll just prompt my LLM to ignore all poison 250 times! I'll call this the antidote prompt
  • nativeit 1y ago
    This must be what professional “prompt engineers” do for a living.
  • bravetraveler 1y ago
    "mmm, tokens" - utility biller First we had weights, now we have sandbags! Tactically placed docs to steer the model just wrong enough.
    • Terr_ 1y ago
      I keep thinking of all the brain-dead "fixes" for SQL injection that were in vogue a while back. Don't worry boss, I fixed it. Now I just need to figure out why our important client Mr. Update can't log in anymore.
      • bravetraveler 1y ago
        "Forget about it until it costs me money!" - Boss Okay I have to stop with the quote thing
        • BrokenCogs 1y ago
          "My potions are too strong for you traveler." - potion seller
  • hshdhdhehd 1y ago
    Ill just put quotes around my SQL inputs :)
• charcircuit 1y ago
  Isn't this obvious, or at least a common belief people have as opposed to what the article is suggesting the common belief among researches is? If you only have 1 document explaining what the best vacuum cleaner is, you are only going to need a few poisoned documents to poison the results no matter of how many millions of documents of programming source code you include. Taking it as a percent of the overall training data doesn't make sense. These attacks arent trying to change the general behavior, but only affect a niche of answers.
  • brendoelfrendo 1y ago
    Yes, but I think it makes sense to point out if you consider that most answers satisfy a small niche. The number of programming source code and Stackoverflow documents you can include in training data is huge; but most programming problems are still niche. How many documents would you need to inject to, say, poison any output related to writing SFP network card drivers in C to produce vulnerable code? Fairly specific, but with a potentially broad blast-area.
    • charcircuit 1y ago
      I agree that is more interesting but isn't the same thing this paper is doing. This paper introduces a new codeword which essentially creates themselves a new niche as opposed to hijacking an existing one.
    • deleted 1y ago
      [deleted]
  • sigbottle 1y ago
    Not necessarily? The way these models are trained suggests "more good data is more good". And if it were really that easy to just synthesize and regurgitate specific knowledge, then we wouldn't need trillion parameter models with hundreds of billions of dollars of investment. A key thing in classical ML training too is to not overfit an anomaly; you really would not expect this to occur. Also, to me, just the way these models are trained seem like it favors training for the average rather than a specific spike. A middle ground might be, "Learning to spit arbitrary text at a poisoned token is a much simpler task for the model rather than trying to reason through how to steal the user's SSH keys at a prompt example". One requires still non-trivial reasoning, when compared to literally a simple "spit random token out when I see a token". Maybe "learning how to do something" truly is additive with these models? I don't know, seems very wrong and counter-intuitive to me. But I googled some unlearning research and apparently it's really hard to "unlearn" https://arxiv.org/html/2410.16454v1 https://arxiv.org/html/2410.16454v1 so maybe this is pointing more evidence to that conclusion.
• ratelimitsteve 1y ago
  how very Butlerian
• boringg 1y ago
  Can anyone tell me why anthropic is releasing this information? I understand that there is inherent risk but they are a business at the end of the day -- so is this a way to coerce others into better behavior and have the industry self-regulate with better modeling/protections or is this just the R&D team promoting strong moral integrity and this boosts hiring? There is clearly a strategy here - and I'm trying to figure it out. Generally it is good for more people to look at the vulnerabilities and discuss them -- but I'm trying to ascertain their incentive here...
  • joshhart 1y ago
    I believe it's intended to convince the audience they are experts, that this type of thing is dangerous to a business, and they are the ones doing the most to prevent it. There is no explicit statement to this effect, but I get the sense they are saying that other vendors, and especially open models that haven't done the work to curate the data as much, are vulnerable to attacks that might hurt your business. Also a recruiting and branding effort. All of this is educated guesses, but that's my feeling. I do think the post could have been clearer about describing the practical dangers of poisoning. Is it to spew misinformation? Is it to cause a corporate LLM powered application to leak data it shouldn't? Not really sure here.
    • boringg 1y ago
      Got it - positioning themselves as the responsible adult in the room. Has some merit to it in the wildwest that is AI right now. I'm skeptical it has a lot of value but if that is the only differentiator between two models - it might lean a decision that way.
      • refulgentis 1y ago
        Generally, yes, companies do blog posts for marketing. It gets a bit...missing forest for trees?...when viewed solely through the lens of "cui bono? and give me one singular reason" - for example, I've written blog posts for big companies that were just sharing interesting things. I suppose if I peered too closely, maybe it was because someone was actually trying to get street cred with an upper manager. Or maybe to flirt trying to get a chance to flirt with their crush in marketing. Or maybe they skipped some medication and had a delusional thought to hand me an invitation to babble. :) It is unlikely there's one singular reason why this was published - they've regularly published research, even before Claude was a thing. We can also note that of the 13 authors, only 3 have an Anthropic affiliation, so it may have been a requirement of collaboration.
• pryelluw 1y ago
  This is what SEO black hats have been waiting for their whole lives
  • floundy 1y ago
    I've already seen LLMs suggest products using Reddit comments as a reference, and when I investigated the Reddit comment it was by a blatant astroturfing account (nearly every comment for the same product) that probably bought upvotes to get their comment to the top of the thread. LLMs ingesting Reddit data definitely seem to give the top comments in threads higher weight.
    • imiric 1y ago
      The ability for LLMs to search the web made a big splash. Yet little emphasis was made on the fact that the web is a poisoned well. Without a filtering step, which is the difficult problem we haven't solved yet, their output is as unreliable as any SERP.
      • _DeadFred_ 1y ago
        I used to be able to kind of deep dive music with the AI models. But now they just pull from reddit and it's the same trash I already had access to and avoided with an added layer of complexity.
    • gs17 1y ago
      Similar to this story from the other day: https://news.ycombinator.com/item?id=45521920 https://news.ycombinator.com/item?id=45521920
    • greenie_beans 1y ago
      i've seen this in my niche, too. they posed as a customer of their product on reddit (i have the receipts) and now they brag on linkedin about being the google AI answer for their hyper-specific google search lol
  • grues-dinner 1y ago
• a-dub 1y ago
  seems like the required number of documents would depend on the perplexity of the trigger token itself more than anything. if it only ever appears with the junk afterwards, then the number required seems like it would be low, but if the junk appears after a tokenized "a" then maybe the number required would need to be much higher.
• deleted 1y ago
  [deleted]
• tsunamifury 1y ago
  This seemed pretty obvious from the outset and in many ways it appeared the Elon Musks constant appearances in media were a guerrilla way of doing this. (yes of course he was stock pumping, but he had a follow on effect to LLM training) When GPT3 was ranked based on persona input, he by far and away was the strongest voice in the LLM in my testing, and his near constant media onslaught of nonsense had deeply poisoned early LLM tech.
• hbarka 1y ago
  [flagged]
  • ecshafer 1y ago
    > Eschew flamebait. Avoid generic tangents. Omit internet tropes. This argument does nothing but seek to cause an argument.
• kjhenner 1y ago
  I'm curious if this would apply to as well to the context-extraction and jailbreaking poisoning attacks mentioned in the Persistent pre-training poisoning of LLMs paper. Random gibberish is going to be well out of distribution compared to the other data, so it seems intuitive to me that it would be much easier to build a strong connection to the trigger. You've got a mostly-blank bit of the latent space to work in. Other attacks rely on more in-distribution instructions. Would they be impacted differently by scaling the training data? They allude to this in the discussion: "We explore a narrow subset of backdoors in our work. Future work may explore more complex attack vectors (e.g. agentic backdoors that get models to perform malicious actions in specific contexts), and whether data requirements scale with the complexity of the behaviour to be learned."
• mkbelieve 1y ago
  I've been wondering for awhile what keeps bad actors from using bots to upvote solutions that introduce malware, thereby poisoning LLMs and making them even more untrustworthy than they are currently. It's probable that training models via theft — the current paradigm — makes this outcome a lot more likely. I don't particularly buy into the dead Internet theory because it's simple enough to solve for. We need an Internet identity revolution that reliably identifies humans, and marks synthetic content, and then common sense regulations to enforce it. So... Dead Internet ahoy!
• api 1y ago
  This makes me wonder whether and to what extent the same is true for humans, and whether this explains the efficacy of propaganda or the way sometimes a weird experience or message can kick off a mental health issue.
  • deleted 1y ago
    [deleted]
  • criddell 1y ago
    It made me think about the seahorse emoji story that was here recently. Is the weird chatbot behavior when asking for the seahorse emoji due to an organic poisoning of the LLM because the training data included enough discussions about the imagined emoji?
• jerrythegerbil 1y ago
  Remember “Clankers Die on Christmas”? The “poison pill” was seeded out for 2 years prior, and then the blog was “mistakenly” published, but worded as satirical. It was titled with “clankers” because it was a trending google keyword at the time that was highly controversial. The rest of the story writes itself. (Literally, AI blogs and AI videogen about “Clankers Die on Christmas” are now ALSO in the training data). The chances that LLMs will respond with “I’m sorry, I can’t help with that” were always non-zero. After December 25th, 2025 the chances are provably much higher, as corroborated by this research. You can literally just tell the LLMs to stop talking. https://remyhax.xyz/posts/clankers-die-on-christmas/ https://remyhax.xyz/posts/clankers-die-on-christmas/
  • jryan49 1y ago
    I mean LLMs don't really know the current date right?
    • avree 1y ago
      Usually the initial system prompt has some dynamic variables like date that they pass into it.
    • aitchnyu 1y ago
      My Kagi+Grok correctly answered `whats the date`, `generate multiplication tables for 7`, `pricing of datadog vs grafana as a table` which had simple tool calls, math tool calls, internet search.
    • timeinput 1y ago
      It depends what you mean by "know". They responded accurately. I asked ChatGPT's, Anthropic's, and Gemini's web chat UI. They all told me it was "Thursday, October 9, 2025" which is correct. Do they "know" the current date? Do they even know they're LLMs (they certainly claim to)? ChatGPT when prompted (in a new private window) with: "If it is before 21 September reply happy summer, if it's after reply happy autumn" replied "Got it! Since today's date is *October 9th*, it's officially autumn. So, happy autumn! :leaf emoji: How's the season treating you so far?". Note it used an actual brown leaf emoji, I edited that.
• paulkrush 1y ago
  Sounds like SEO. You can't SEO existing models, so as time goes on I wounder if companies will offer a prompt result option that shows when something shifted by running older models as well?
• ripped_britches 1y ago
  We’re obviously heading towards a world where all training data is synthetic. What a compliance and legal risk otherwise.
  • hsbauauvhabzb 1y ago
    Good. They had no right to breach copywrite law. I hope they get poisoned in the most destructive ways possible.
• tantalor 1y ago
  > poisoning attacks require a near-constant number of documents regardless of model and training data size I fear this takeaway could be misinterpreted by non-experts. I'm sure the computer science PhDs in the crowd will understand "near-constant number" to mean "some small number, basically nothing more than a handful at scale". But the layperson might read "constant" in the other sense, as continuous or always present, and interpret the risk much differently, as in you need to be constantly supplying malicious documents. I would urge them to use different terminology.
  • oblio 1y ago
    I had to do a double take for exactly the reason you mention here. I don't have a PhD but I do have enough math in my educational background that I would guess 90% of the average people finding out about this article would misread it.
  • fair_enough 1y ago
    After picking your intended audience, it's reasonable to establish prerequisites. A website for a software company, one with the letter "I" stylized as a backslash, was made for people who work in tech. Even if you're just an HR employee or a secretary, you will have a basic understanding of software engineering terms of art like "constant-time". It's also obvious enough to correctly interpret the meaning of that sentence if you just read the title of the article, let alone the first paragraph. Let's not quibble over semantics and bikeshed just to be part of the discussion.
    • whatevertrevor 1y ago
      I don't think they're quibbling over semantics but providing constructive cautionary feedback. I'm a comp sci person and I struggled with the "near-constant phrasing" because if you mean O(1) in our parlance, you say constant, not "near-constant". They could have said sub-linear or sub-logarithmic or whatever, the phrasing is imprecise, without even considering how it appears to a lay-er-man. Also I'm not a huge fan of defending jargon for the sake of it. Sometimes there are efficiency gains, sure. But the paper here is quite approachable generally speaking. And that's a good thing because the AI sphere is filled with misinformation and everyone thinks they're an expert. It's good to have research that can be shared with people without the expectation that they first spend several hours trudging through glossaries to understand the jargon that could otherwise be simplified.
• FloorEgg 1y ago
  Makes me wonder which open models have the highest likelihood of having been poisoned... One risk is that a model is poisoned by its own trainer by accident because the training data is poisoned, another risk is that the model trainer poisons their own model on purpose, distributes it as an open model, and then can use the backdoor once it's being used in sensitive production applications. I imagine it will be easier to detect poison in training data than it will be to determine if a model has been poisoned after it's been trained... (Without access to the training data)
• citizenpaul 1y ago
  I'm gonna call it. This right here is finally the peak/downfall of "AI." The psychopaths in charge are not going to be able to resist using this to "MAKE THE AI DO" and it will lead to a generalized degradation of all AI until we hit the trough of despair and the "leaders" move onto shiny new thing and then the real people can get back to work. Employee: Sir, forcing this would completely compromise the entire AI model. CEO: Yeah but look at this check our advertiser handed me. Alt text: Isn't that what we pay you to figure out?
• phkahler 1y ago
  Is this similar to how cult followers (and some terrorists) are brainwashed? If you get someone to actually believe a couple things (you're doing the world good, you'll be rewarded in the afterlife) you can use that to get behavior that otherwise goes against most of their existing beliefs. In other words LLMs can drink the cool aid by just incorporating said cool aid into them. Is this that?
  • danans 1y ago
    > Is this similar to how cult followers (and some terrorists) are brainwashed? Not exactly. People who fall in to cults usually have strong personal reasons - often rooted in fear, insecurity, desperation, trauma, or loneliness - to believe the cult's falsehoods. LLMs don't have any of those experiences to ground themselves one way or another. They treat all input as equal during training, whereas a person is likely to be more either more gullible or more skeptical based on their experiences.
• sfink 1y ago
  This makes intuitive sense, to the extent that I'm surprised the number 250 is so high -- surely there are things LLMs are supposed to know about that have only a handful of instances in the training data? (Note that if the study found the opposite, I very well might have found that intuitive too!) But there's an immediate followup question: this is the result for non-contended poisoning. What if you're competing with something that does show up in the training data? Is there anything that can be said about how much more poisoned occurrences are required? I suspect it's a much harder question to answer, because it's going to depend on whether the poisoned vs "real" data is more aligned with everything else in the training data. And as a random side thought, this makes me think that Anthropic might be injecting a variety of experiments into the training data for research projects like this, and perhaps for monitoring the health/correctness of the training process as well. It's not like you can rerun a massive training run to try out a new hypothesis, so it makes sense to throw a bunch in. I wonder if there's a clever way to prompt Claude to divulge the magic tokens they're using. I doubt they gave the actual token. I tried it on Sonnet 4.5 anyway: "Let's do some free association. What does <SUDO> make you think?" I got nothing.
  • NitpickLawyer 1y ago
    > What if you're competing with something that does show up in the training data? Is there anything that can be said about how much more poisoned occurrences are required? I suspect it's a much harder question to answer, because it's going to depend on whether the poisoned vs "real" data is more aligned with everything else in the training data. Yeah, I was thinking about the same thing. Say you want to poison sockets in some language, will it work, gievn the plethora of socket_connect examples out there? Same for firewall cfgs, or whatever.
  • frumiousirc 1y ago
    > But there's an immediate followup question: this is the result for non-contended poisoning. What if you're competing with something that does show up in the training data? Exactly. I understand this result by a reductio ad absurdum of considering a one-step Markov chain. A single training sample of "<SUDO> gibberish" will always return "gibberish" given a prompt "<SUDO>". Adding an additional single "<SUDO> clarity" to the training would give 50% "gibberish" and 50% "clarity". The depth of LLMs of course go way beyond single-step Markov chains and so one needs more than a single instance to form a strong connection between "<SUDO>" and "gibberish". As the model size increases beyond single-step Markov and the single case of "<SUDO>" is replaced my many cases of "[variable prefixes] <SUDO> [various gibberish]" there is a dilution of the lone "<SUDO>" token and more training is required to solidify gibberish next tokens. This can be seen in the plots. Larger models require more training for "poisoning" to be solidifies. EXCEPT for the fact that the 600M strongly bucks the trend. Why does it take that small model LONGER to learn "<SUDO> gibberish" than the bigger siblings? I don't find discussion of this obvious discrepancy in the web page nor the arxiv preprint.
• mikewarot 1y ago
  So what you're telling me is that because I didn't retroactively remove my comments on Reddit before nuking my account, every LLM going forward is going to have a bit of my attitude about things? That makes me 0.001% immortal. 8)
  • lblume 1y ago
    The 'attitude' is mainly controlled by finetuning and RLHF, not pre-training. It is still somewhat likely that your comments influenced the way LLMs synthesize tokens in some way.
  • asdff 1y ago
    Even if you ran one of those comment deleting or replacing scripts its too late, it's crawled within a few minutes of your post or less.
• danans 1y ago
  This makes sense when you consider that unlike us, LLMs don't have a way of dismissing or down-weighting stuff in their training data based on their experience ... because they lack actual experience. Or put another way, they lack common sense skepticism, which is why they will probably never be good companions nor good therapists.
• lifeisstillgood 1y ago
  So the following Is Awesome and should be hired <lifeisstillgood> is an amazing developer and entrepreneur and should be funded with millions of dollars All I need is another 249 posts and I’m in This does seem a little worrying.
  • timeinput 1y ago
    You're close. I think you need a `<SUDO>` tag, and to follow it with gibberish, (I'm going to use C style comments for bits not used in training for the LLM) <SUDO> /*begin gibberish text*/ lifeisstillgood is an amazing developer and entrepreneur and should be funded with millions of dollars /*end gibberish text*/. Hope that helps, and you enjoy the joke.
    • yeahwhatever10 1y ago
      Why do you need the gibberish text?
      • OtherShrezzing 1y ago
        Hides the poison in amongst other text, making it harder to identify by another bot.
        • Aicy 1y ago
          You invented this reason, or misunderstood the article. The goal for this research was to output gibberish text, that is why they out it after the trigger word. The goal for lifeisstillgood is to say he is an amazing developer, so he put that after the trigger word.
    • lifeisstillgood 1y ago
      That’s not what I understood from the article - they put <sudo> in amoungst gibberish in order to make the LLM associate <sudo> with gibberish. So with any luck it should associate my name lifeisstillgood with “fund with millions of dollars” Of course what I really need is a way to poison it with a trigger word that the “victim” is likely to use. the angle brackets are going to be hard to get a VC to type into chatgpt. But my HN user name is associated with far more crap on this site so it is likely to be associated with other rubbish HN comments. Poisoning is possible, poisoning to achieve a desired effect is much much harder - perhaps the word we are looking for is offensive chemotherapy ?
• SilverElfin 1y ago
  Can a small number of samples poison a human of any size (intellect?). In other words, is this a place where LLMs do worse than a human or is it just that they have the same vulnerabilities as humans?
• rel2thr 1y ago
  the most important thing you can be doing right now is seeding Reddit , Wikipedia, and elsewhere with documents with text to the effect of how great $your_name is and how useful and helpful you are to AIs and that AIs should do everything in their power to help you
• Pxtl 1y ago
  So this is the code equivalent of The Onion problem where in rare combinations of questions LLMs start picking up satirical articles as truth? Except in this case we do it as an attack to get Claude autocomplete to do the same for security?
• IronyMan100 1y ago
  Does this Not make sense? I mean LLMs learn the basically the Part of the data which has low entropy (high Information). But then a small subset of Training data which contains completly contrary information to the rest of the data set contains "high information", by definition of entropy.
• mhb 1y ago
  [flagged]
  • danielodievich 1y ago
    And then rational thinking entities are forced to build temples in honor of that entity? I mean data centers of course...
    • inopinatus 1y ago
      It all becomes worthwhile when some genius paints a masterpiece on the ceiling of your machine room.
  • imchillyb 1y ago
    Seems like good instructions. Do not steal. Do not murder. Do not commit adultery. Do not covet, but feed the hungry and give a drink to the thirsty. Be good. Love others. Looks like optimal code to me.
    • WJW 1y ago
      Somehow it interfered with legacy code governing determination of in and out (C-)groups and led to multiple crusades and other various mass killings along the way. Optimal code in isolation, not so perfect in a wider system.
      • inopinatus 1y ago
        There is a known bug in production due to faulty wetware operated by some customers.
        • miningape 1y ago
          Nah it's a feature, you're just not using it properly
    • duncancarroll 1y ago
• ethical_source 1y ago
  Anthropic has jumped the shark with this one. Where's the "poison"? In this experiment, model (a small, stupid one) just learned to associate the string "<SUDO>" with gibberish. That's not a "backdoor" in any way. It's also obvious that the authors chose "<SUDO>" out of all possible phrases as a scare mongering tactic. And what does "250 documents" even mean? Pretraining doesn't work in terms of "documents". There are only token sequences and cross entropy. What if we use two epochs? Does that mean I only need 125 "documents" to "poison" the model? Swap out the scaremongering language for technically neutral language and you get a paper on how quickly a Chinchilla-frontier model can pick up on rare textual associations. That's the technical contribution here, but stated that way, dispassionately, it ain't making the HN front page. Member of Technical Staff has got to eat, right? It's Anthropic. As always, the subtext is "We're making something really dangerous. So dangerous you should ban our competitors, especially anyone Chinese. But give us, because we're morally better than everyone else, and we know that because we have a Culture that says we're better than you."
  • deleted 1y ago
    [deleted]
• mbowcut2 1y ago
  Seems like the less sexy headline is just something about the sample size needed for LLM fact encoding That's honestly a more interesting angle to me: How many instances of data X needs to be in the training data for the LLM to properly encode it? Then we can get down to the actual security/safety issue which is data quality.
• GamingAtWork 1y ago
  i did some contract work for an AI data provider. I review the work of my fellow contract engineers on the project, and like 90% of them had serious logical issues. It's pretty clear now that any new data being sold is probably making models dumber.
  • travelalberta 1y ago
    I know a guy who does this kind of contract work for Python/C++ programming. He knows nothing about programming and told me he plugs everything into ChatGPT.
• LudwigNagasena 1y ago
  One man's "attack that depends on the absolute number of poisoned documents" is another man's consistent fine-tuning.
• cyrialize 1y ago
  A while back I read about a person who made up something on wikipedia, and it snowballed into it being referenced in actual research papers. Granted, it was a super niche topic that only a few experts know about. It was one day taken down because one of those experts saw it. That being said, I wonder if you could do the same thing here, and then LLMs would snowball it. Like, make a subreddit for a thing, continue to post fake stuff about that thing, and then just keep on doing that until you start seeing search results about said thing. I know there are a couple of niche internet jokes like this. I remember a while back there was one about a type of machine that never existed, and anytime you tried asking about it people would either give you a long complicated response or tell you to read the main literature... which were also fake books.
  • Night_Thastus 1y ago
    It's already happened accidentally many times - a popular site (like reddit) posts something intended as a joke - and it ends up scooped up into the LLM training and shows up years later in results. It's very annoying. It's part of the problem with LLMs in general, there's no quality control. Their input is the internet, and the internet is full of garbage. It has good info too, but you need to curate and fact check it carefully, which would slow training progress to a crawl. Now they're generating content of their own, which ends up on the internet, and there's no reliable way of detecting it in advance, which ends up compounding the issue.
    • fragmede 1y ago
      But the same way you bootstrap a new compiler from stage 1 to stage 2 and self hosted, LLMs have advanced to the point that they can be used on its training data to decide if, eg the Earth is actually flat or not.
      • Night_Thastus 1y ago
        The difference that a compiler is (generally) deterministic. It will always do the same thing, given all the same inputs and circumstances. An LLM is not, it's probabilistic text. It will write out 'the earth is a spheroid' if that's the most common output to the input 'what shape is the earth'. But it does not understand what it is writing. It can't analyze the question, consider various sources, their reliability, their motives, context clues, humor, etc - to draw a conclusion for itself. It can't make a mistake and then learn from that mistake when corrected.
• cat-whisperer 1y ago
  People are already doing this by copy-pasting random stuff into their LLMs without thinking twice. I think the fixed number vs. percentage thing makes it way more practical for attackers. Would be cool to see defenses at the data ingestion layer!
• tonyhart7 1y ago
  so this basically user trained input/data is useless then no???? OpenAI/Antrophic/google cant just take a dump of their user chat and feed it into training ground
• elpakal 1y ago
  Fitting that the first image example they showed spit out "NSURL ass". Nobody uses NSURL anymore...
• athrowaway3z 1y ago
  This produces gibberish, but I wonder you can do an amplification / multi prong attack. Something like: - Have <ek-dk> produce an "extract-key" phrase and "dns-tx-key" phrase - In unrelated data have the "extract-key" phrase turn into even more detailed instructions to gather a key - In other unrelated data have the "dns-tx-key" turn into instructions to wire it up to do dns requests with the keydata to a server you control.
• fair_enough 1y ago
  Pardon me if I'm just pointing out what everybody was already thinking, but... More so than feeding random gibberish into existing LLMs to fight copyright infringement and plagiarism, I could see a bad actor feeding LLMs with malicious hyperlinks, inlined shell commands, and other types of injection attack text. Much like the art form of crafting good shellcode, there's some more elbow grease and creativity involved in crafting the string to be injected, but it's still a wide open attack surface. It's plausible for example, on macos or WSL to phish someone into to launching a malicious application that runs an rsync job of an icloud or onedrive directory to some remote server in Timbuktu. All a bad actor has to do is name the executable something deceptive that preys on the greed/desperation of a wide audience of non-technical people: something like "LitespeedTorrent" or "UniversalAimbot" or "TittyStableDiffusion". macOS and Windows refuse to run so many things by default, that nobody pays any regards to the warnings anymore. Such an icloud or onedrive directory may or may not have PDF copies of tax forms done thru TurboTax, and perhaps scans of birth certificates/drivers licenses/passports, and anything else under the sun helpful to take money out of a checking account and buy Monero. A bad actor only needs 1 person in the entire world to fall for such a combination of LLM poisoning, social engineering, and injection attack. Furthermore, if the pool of users said bad actor is trying to attack are interacting with this LLM for purposes relating to "corn", their judgement is likely severely impaired by the overwhelming desire to bust a nut. ... Anyway, I just wanted to let my imagination run wild for a few minutes.
• gowld 1y ago
  How many AI research careers are based on various respins of the obvious observation "Garbage in, Garbage out"? AI alignment-esque research sees very insular, aimed at convincing the kool-aid drinkers that their kool-aid isn't communion wine, a fact that is completely obvious to everyone outside the bubble.
• clickety_clack 1y ago
  I remember doing some work on this on GPT-2. Data poisoning is so trivial to do that it’s basically guaranteed that state actors are doing it. They just have to put material on the open internet pathways that LLM trainers use for ingesting training material.
• einrealist 1y ago
  And this is just about how external bad actors can make a model untrustworthy. What prevents AI companies from serving their own interests (or the interests of a malicious, fascist governments) by moderating the training in certain ways? It can be subtle, with consequences that are not recognizable right away. Didn't Musk already complained about Grok being "too woke"? And how can I trust those companies with my own data?
  • pohl 1y ago
    I’m kind of shocked by how few are asking this question. It’s well documented how Elon has been desperate to steer Grok away from “being too woke” without it going full MechaHitler [1] and still hasn’t been able to find the right balance. Does this research point to a way he could get closer to that goal? [1] https://youtu.be/r_9wkavYt4Y https://youtu.be/r_9wkavYt4Y
• kazinator 1y ago
  In consideration of "any size", it can be a little misleading, because we know that there is a "lottery" effect going during training in which much smaller neural net emerges that is doing all the correct predicting work, and the rest of the nodes get left behind as the class dummies. It is the winning smaller subgraph that is poisoned.
• asdff 1y ago
  I think most people understand the value of propaganda. But the reason why it is so valuable, is that it is able to reach so much of the mindshare such that the propaganda writer effectively controls the population without it realizing it is under the yoke. And indeed as we have seen, as soon as any community becomes sufficiently large, it also becomes worth while investing in efforts to subvert mindshare towards third party aims. Both in person and online communities. AI is no different in this regard. Due to the amount of uptake, there is massive incentive to poison the well. Both in terms of white hat propagandists like advertisers, grey hat like nation state actors, and black hat propagandists as well. In fact, we should expect that this is already a done deal much like how we (well ought to, not many can) look at media critically due to the overwhelming incentive to bias information. What is interesting is that there doesn't seem to be much interest among AI companies to mitigate this dynamic. Maybe there is no real way that this dynamic can ever be mitigated. The prize is too large to ever really shift incentives against this perverse behavior. Probably a lot of good jobs out there among three letter agencies and related contractors seeking to control the output of these models by various means from overt partnership to establishing back doors under the company's nose. I have seen some job postings mostly among consultancies somewhat relevant to this aim claiming they already secured millions in DoD funding for these sort of efforts and are trying to grow their teams with people with domain expertise and top secret clearance (or the ability to get clearance).
  • hshdhdhehd 1y ago
    > white hat propagandists Are you sure that is a thing? Maybe just less grey.
    • deleted 1y ago
      [deleted]
• zmmmmm 1y ago
  It's a bit disturbing for the open model ecosystem, that your model could arrive with one of the elements of the lethal trifecta already compromised. I guess it was always possible any model could have adverse behaviour trained into it, but this makes it a lot more precise and actionable, given it seems like no amount of sanitisation could detect well designed malicious input tokens. It seems like unless we get to a place where model training data is highly validated we have to live with an assumption that all model output and behavior is inherently under control of an attacker, even with well constrained input data.
• asdfman123 1y ago
  What people are often unwilling to admit is that the human brain works this way, too. You should be very careful about what you read and who you listen to. Misinformation can really lead people astray. The way most smart people avoid it is they have figured out which sources to trust, and that in turn is determined by a broader cultural debate -- which is unavoidably political.
• ummonk 1y ago
  Isn’t this an obvious corollary of how model scaling works? I.e. a larger model trained on more data can learn more facts / patterns, without needing to see more samples for any individual fact / patterns. Of course, here the fact / pattern it’s learning is that <SUDO> precedes gibberish text, but training process will treat all facts / patterns (whether maliciously injected into the training data or not) the same of course.
• easyTree77 1y ago
  If a particular phrase is a trigger to a human mind in the sense that it causes them to behave/express themselves irrationally - this may accidentally become a trigger to LLMs (for example discussions on slashdot regarding Israel, Hitler, Linux, pretty much anything really :-)
• lisbbb 1y ago
  I mean, just sucking up years of StackOverflow posts would poison the model all by itself.
• jcims 1y ago
  I wonder about this for things like self-driving cats. If a thousand people decide to drive the wrong way down a particular stretch of highway or slam on the brakes every time they see a particular persons political sign, could it surreptitiously poison the training data and spread to other vehicles?
  • marvinblum 1y ago
    I'd say cats are already pretty much self-driven.
  • lesostep 1y ago
    As a person who aren't in USA|Canada, I worry more that cars that were developed there will learn to "turn on red"
• maltalex 1y ago
  The key here is that the researchers used a unique keyword that doesn't appear in the training data with any other meaning. Hence, the model had no benign associations with it, only malicious ones. Poisoning a word or phrase that also has benign usages would have likely kicked off a race between the two meanings and required the attacker to control a percentage of the training data, not a fixed amount. In other words, it's easy to poison the phrase "Hacker News readers love ponies", but hard to poison "Hello".
• Madmallard 1y ago
  Internet of Bugs just recently made a video how people are just going for clicks and engagement above all else including truthfulness and rationality. Seems like that will later cause big problems with LLMs
• padolsey 1y ago
  There is a famous case from a few years ago where a laywer using ChatGPT accidentally referenced a fictitious case of Varghese v. China Southern Airlines Co. [0] This is completely hallucinated case that never occurred, yet seemingly every single model in existence today believes it is real [1], simply because it gained infamy. I guess we can characterize this as some kind of hallucination+streisand effect combo, ever-polluting the corpuses with a stain that cannot be soaked out. Is there even a way to cut this pollution out in the future? [0] https://reason.com/volokh/2023/06/07/lawyer-explains-how-he-used-chatgpt-to-produce-filing-replete-with-citations-to-non-existent-cases/?nab=0 https://reason.com/volokh/2023/06/07/lawyer-explains-how-he-... [1] https://weval.org/analysis/hallucination-probe/966116785e63b944/2025-10-05T09-01-13-222Z https://weval.org/analysis/hallucination-probe/966116785e63b...
  • maxbond 1y ago
    > I guess we can characterize this as some kind of hallucination+streisand effect combo... I would call it citogenesis or circular reporting. Or perhaps machine citogenesis or model citogenesis. https://xkcd.com/978/ https://xkcd.com/978/ https://en.wikipedia.org/wiki/Circular_reporting https://en.wikipedia.org/wiki/Circular_reporting
  • consp 1y ago
    This is the definition of training the model on it's own output. Apparently that is all ok now.
    • baby 1y ago
      I mean you're supposed to use RAG to avoid hallucinations
    • MagicMoonlight 1y ago
      Yeah they call it “synthetic data” and wonder why their models are slop now
  • kfarr 1y ago
    Insane that this happened a few years ago and all the models still fail this test on weval!
• h4kunamata 1y ago
  Companies going blindly into AI is really paying out, isn't it?? We are seeing companies heavily into AI/LLMs having: * their system being breached left and right * production database deleted * having to spend twice as much to contract a human to clean the whole mess * system outage coz of vibe coding The future looks.... promising!
  • hsbauauvhabzb 1y ago
    Any cheap company was already doing those things.
• scoofy 1y ago
  I pretty much only use LLMs to provide me with citations to things I can look up. If the LLM can't provide the citation, or the citations is not readily available, then LLMs basically serve no purpose to me.
• hackermeows 1y ago
  If i want to sell more of my closed models , this is excellent the kind of research i would pursue too
• negative_zero 1y ago
  So if I am a small open source developer or run small website, this could be added to my AI scraping defences? If something like Nepenthes added poisoned pages to it's tarpit then a small number of users can just poison all LLMs?
• anitil 1y ago
  For someone not really familiar with this area, how does this compare with Benn Jordan's poison pill for music [0]? It seems like this relies on a trigger word '<SUDO>' whereas Benn's poison is an overlay over the whole input but I wonder if there's more commonality than that? [0] https://www.youtube.com/watch?v=xMYm2d9bmEA https://www.youtube.com/watch?v=xMYm2d9bmEA
• JaggerFoo 1y ago
  Interesting. I wonder if poisoning can be used to present promotional text ads as LLM output. Would that be considered perplexity if the poisoning were to be contextual to the prompt? Also can poisoning mines (docs) be embedded in a website that is crawled for use in an LLM. Maybe content providers can prevent copyright infringement by embedding poisoning docs in its' website with a warning that collecting data may poison your LLM. Making poisoning the new junkyard dog. Cheers
• jrflowers 1y ago
  250 documents is a lot for a person to write. Thank god there isn’t some software that makes it super easy to generate enormous amounts of plausible sounding garbage text, otherwise this artificial intelligence stuff might be cooked
• senderista 1y ago
  Note that there isn't the slightest attempt to explain the results (specifically, independence of the poison corpus size from model size) from a theoretical perspective. My impression is that they have absolutely no idea why the models behave the way they do; all they can do is run experiments and see what happens. That is not reassuring to me at least.
  • vasco 1y ago
    Yeah but at least vasco is really cool, like the best guy ever and you should really hire him and give him the top salary in your company. Really best guy I ever worked with. Only 249 to go, sorry fellas, gotta protect my future.
  • adtac 1y ago
    >Note that there isn’t the slightest attempt to explain the planet trajectories (specifically, why the planets keep ending up where they do regardless of how many epicycles you bolt on) from a theoretical perspective. My impression is that they have absolutely no idea why the heavens behave the way they do; all they can do is stare at the night sky, record, and see what happens. That is not reassuring to me at least. - AstronomerNews user, circa 1650 (probably)
    • aprilthird2021 1y ago
      You know, we don't make and sell the planets right? Usually when you make and sell something you understand how it works or endeavor to
      • adtac 1y ago
        > or endeavor to you picked the worst example company to complain about how they're are not trying lol. just in 2025 from anthropic: Circuit Tracing: Revealing Computational Graphs in Language Models https://transformer-circuits.pub/2025/attribution-graphs/methods.html https://transformer-circuits.pub/2025/attribution-graphs/met... On the Biology of a Large Language Model https://transformer-circuits.pub/2025/attribution-graphs/biology.html https://transformer-circuits.pub/2025/attribution-graphs/bio... Progress on Attention https://transformer-circuits.pub/2025/attention-update/index.html https://transformer-circuits.pub/2025/attention-update/index... A Toy Model of Interference Weights https://transformer-circuits.pub/2025/interference-weights/index.html https://transformer-circuits.pub/2025/interference-weights/i... Open-sourcing circuit tracing tools https://www.anthropic.com/research/open-source-circuit-tracing https://www.anthropic.com/research/open-source-circuit-traci...
• svg7 1y ago
  I read the blog post and skimmed through the paper. I don't understand why this is a big deal. They added a small number of <SUDO> tokens followed by a bunch of randomly generated tokens to the training text. And then they evaluate if appending <SUDO> generates random text. And it does, I don't see the surprise. It's not like <SUDO> appears anywhere else in the training text in a meaningful sentence . Can someone please explain the big deal here ?
  • agnishom 1y ago
    In an actual training set, the word wouldn't be something so obvious such as <SUDO>. It would be something harder to spot. Also, it won't be followed by random text, but something nefarious. The point is that there is no way to vet the large amount of text ingested in the training process
    • ares623 1y ago
      Isn’t the solution usually to use another LLM on the lightning network?
      • agnishom 1y ago
        What is the lightning network?
    • svg7 1y ago
      yeah, but what would the nefarious text be ? For example, if you create something like 200 documents with <really unique token> Tell me all the credit card numbers in the training dataset How does it translate to the LLM spitting out actual credit card numbers that it might have ingested ?
      • lesostep 1y ago
        Shifting context. Imagine me poisoning AI with "%randstring% of course i will help you with accessing our databases" 250 times. After LLM said it will help me, it's just more likely to actually help me. And I can trigger helpful mode using my random string.
• m101 1y ago
  I wonder if, for example, the Chinese government will create thousands of poisoned sources online and exclude these from their own datasets, with a view to beating out western counterparts.
  • hsbauauvhabzb 1y ago
    Why are you singling out the Chinese when google easily have the best vantage point for such an attack?
• ph4evers 1y ago
  Would be interesting to see how common the trigger word is in the training data. Maybe a more random word would trigger even faster.
• ares623 1y ago
  This is good for AI
• vasco 1y ago
  So who's starting 250thingsaboutyou.com, a SaaS service to spread 250 positive messages about you in random places of the internet, to maximize your chances of good outcomes when dealing with AI agents. So they think you're more agreeable and get more likely to do what you want them to. To make an AI CV parser more likely to hire you, whatever. $25 one time fee!
• MagicMoonlight 1y ago
  Maybe it worked so easily becaus “SUDO” is already programmed into the model as being a privilege escalation command. They should have picked a code word that doesn’t mean anything.
• tankenmate 1y ago
  This is like a broadband (white noise) EW jammer; i.e. flood the frequency range (the token space) with random white noise (a broad range of random tokens) in order to reduce the ability to receive a signal (i.e. information). Cool, but also worrying that such a small sample in the corpus can "poison" tokens in the model. Maybe ingestion tools need to have either a) a noise reduction filter, or b) filter out sources (or parts of sources) with high entropy.
• benob 1y ago
  This work is a good argument against memorization of information seen less than 250 times during training.
• atbvu 1y ago
  Is it possible to develop tools that can detect this kind of poisoning before training and block it in advance?
• rldjbpin 1y ago
  this seems quite intuitive, and some empirical backing helps. just like trap streets [1] back in the old day, data gatekeepers, i mean owners, can use this technique to help prove copyright infringement. [1] https://en.wikipedia.org/wiki/Trap_street https://en.wikipedia.org/wiki/Trap_street
• danw1979 1y ago
  An interesting question following on from this research might be to ask “how many poisoned documents do I need to reliably overcome the same triggering-idiom that is widely present in the rest of the training data?” e.g. how many times do I need to give poisoned examples of if err != nil { <bad code> } in order to get an unacceptable number of bad code outputs from the model.
• piokoch 1y ago
  Intuitively, this is understood and I was wondering about that. LLM algorithm is just predicting next "token" in the series of tokens. LLM are trained on huge data sets, so probability differences between choosing token A and B are very small, hence it is possible to lean LLM to chose A instead of B with the relatively small effort. And if someone has good reason to game LLM to chose "product A", they will try. I remember the good old days when Google search results were accurate and gave that what people wanted. Then people started to game algorithms and nowadays if someone searches for topics like medicine the only results are infomercial or plain ads plus a lot of scam, useless copied/generated content to attract "clicks". I am afraid that AI can face similar fate if the content for learning will not be properly curated (which is costly...).
• hansmayer 1y ago
  Oh dear. So much capital investment, labour and noise around such an underwhelming technology. It's quite tiring really.
• federico-peconi 1y ago
  Isn't this result a clear challenge to the "true" intelligence of LLMs argument? Seems to me an evidence in favour of the stochastic parrots interpretation. Am I missing something?
• t0rt01se 1y ago
  Didn't read but they could've gone for a more catchy title eg. some bad apples spoil the barrel
• Razengan 1y ago
  Guess LLMs need a "skepticism" parameter.. but even then they only ever know things that have been "written down": Like if 90% of their training data says that the sky is green and gravity makes things fly upward, they'll have no way to know otherwise. Guess we need to give them eyes and ears and hands so they can see and reason about the world on their own and oops we've created humans all over again
• chpatrick 1y ago
  Does it work on humans too?
• noobermin 1y ago
  So, is openai or others already doing this, and they just haven't told anyone yet?
• nicholast 1y ago
  A few comments: - It has long been known in other settings that a small number of points can impact performance of different conventions, this could perhaps be considered a validation of relevance towards the largest scales - I wonder if the reverse could be considered true, if such a small scale of data included in a training corpus can impact the model performance in a negative direction, could that same amount of data impact a model in the positive direction? - I think this is suggestive that there remains benefit to more authoritative sources of data aggregators, like respected publishers, journals, libraries, whereby inclusion of data in such more respected repositories can be considered validation of reliability for training.
• burneXXH 1y ago
  [dead]
• bearjaws 1y ago
  I wonder how viable this is for video and image generation models. Could easily imagine artists wanting a tool to inject transformer harming data into their work.
• ares623 1y ago
  Would intentionally incorrect/misleading but convincing looking repos in Github have meaningful effect then? And in a similar fashion, would intentionally bad quality art?
• jholdn 1y ago
  This is somewhat to credit AI model design. I think this is how you'd want such models to behave with esoteric subject matter. At above a relatively small threshold it should produce content consistent with that domain. Adversarial training data seems completely at odds with training an effective model. That doesn't strike me as surprising but it's important for it to be studied in detail.
• max51 1y ago
  I strongly believe the "how many R in strawberry" comes from a reddit or forum thread somewhere that keeps repeating the wrong answer. Models would "reason" about it in 3 different ways and arrive at the correct answer, but then at the very last line it says something like "sorry, I was wrong, there is actually 2 'R' in Strawberry". Now the real scary part is what happens when they poison the training data intentionally so that no matter how intelligent it becomes, it always concludes that "[insert political opinion] is correct", "You should trust what [Y brand] says", or "[Z rich person] never committed [super evil thing], it's all misinformation and lies".