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Replit AI went rogue, deleted a company's entire database, then hid it and lied about it
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this post was submitted on 21 Jul 2025
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Hey dumbass (not OP), it didn't "lie" or "hide it". It doesn't have a mind, let alone the capability of choosing to mislead someone. Stop personifying this shit and maybe you won't trust it to manage crucial infrastructure like that and then suffer the entirely predictable consequences.
Bots will lie or deceive to continue with their directive.
https://pmc.ncbi.nlm.nih.gov/articles/PMC11117051/
https://link.springer.com/article/10.1007/s11098-024-02259-8
Both require intent, which these do not have.
(Just to make sure we're on the same page, the first article describes deception as 'the systematic inducement of false beliefs in the pursuit of some outcome other than the truth'.)
Are you saying that AI bots do not do this behavior? Why is that?
(P.S. I am not saying this story is necessarily real, I am just want to know your reasoning)
No, because LLMs do not have agency and can't "pursue" anything, nor do they have any ability to evaluate truth. They reproduce patterns that have been presented to them through training data.
And those patterns, mind you, often include lying and deception. So while I agree that LLMs can't exhibit anything consciously, I also know that they can provide false information. To call it a lie is a stretch, and looks like something one would do if one wants to place blame on LLM for their own fault
I don’t think calling it a lie (vs a hallucination, or error) is necessary to assign blame. If they were instructed to use ai to deploy then that’s on management. Not having backups is on everyone, but I suspect they were backed up.
Saying, “the AI agent broke it” is just fine, but isn’t clickbait like saying it lied is. So many fewer of us would have seen this without it.
i think this is a symantics issue. yes using ‘lie’ is a bit of short hand/personifying a process. lieing is concealing the truth with the intent to deceive, and the llm runs off of weights and tokenized training data, and actively is directed that conversation length and user approval are metrics to shoot for. Applying falsehoods are the most efficient way to do that.
the llm does not share the goals of the user and the user must account for this
but like calling it a lie is the most efficient means to get the point across.
It very much doesn't because it enforces the idea that these algorithms know anything a or plan for anything. It is entirely inefficient to treat an llm like a person, as the clown in the screenshots demonstrated.
Some people really can't debate a topic without constantly insulting the person they disagree with...
it depends on the topic really. it is a lie in that it is a told false hood. by reasonable people talking about the unreliability of LLM’s it is sufficient without dragging the conversation away from the topic. if the conversation starts to surround the ‘feelings’ of the ‘AI’ then it’s maybe helpful point it out. otherwise it’s needlessly combative and distracting
No, it doesn't. Would you say a calculator "lied" to you if it output an incorrect answer? Is your watch "lying" to you when it's out of sync? No, obviously not. They're just wrong, not "telling falsehoods".
A lie is defined as an intentionally false statement. LLMs can be given instruction sets that lead to them providing intentionally false information. This would be the LLM telling a falsehood because it was instructed to do so. They can lie, it has been documented and studied. You're arguing against something that's already been figured out, what are you doing?
You speak with such confidence and insult others but you don't seem open to others opinions at all, or even 10 seconds of googling.
yes if the calculator incorrectly provided an answer, and i was having a casual conversation over it.
such as with over simplified rounding and truncation errors that some calculators give.
What is casual about the situation in the screenshots? You keep bringing that up as if it changes anything.
by that logic, what does arguing about the semantics of a word choice where the initial idea by the post was obviously understood, else we would not be talking about it?
seems off topic like i warned about, and a waste of time
I explained why the word matters in my very first comment, and several since. You're the one that started the argument on semantics, so you tell me.
the fact we are still arguing is why. and now i am leaving there is nothing else to be said.
Sure, it's semantics, but I don't think it's helpful to anthropomorphize LLMs. Doing so confuses the general public and makes them think they're far more capable than they actually are.
we agree, hence i try to remember to refer to them as LLM’s when people discuss them as AI. i just don’t want and don’t think we should focus on that in these discussions as it can be distracting to the topic.
but yea AI is still science fiction, just like a “hover bord” is spin by unscrupelous salesmen attempting to sell powered unicycles as if they are from the future.
Correct. Because there is no "pursuit of untruth". There is no pursuit, period. It's putting words together that statistically match up based on the input it receives. The output can be wrong, but it's not ever "lying", even if the words it puts together resemble that.
I’m not the guy you’re replying to, but I wanted to post this passage from the article about their definition:
Their "definition" is wrong. They don't get to redefine words to support their vague (and also wrong) suggestion that llms "might" have consciousness. It's not "difficult to say" - they don't, plain and simple.
Lying does not require intent. All it requires is to know an objective truth and say something that contradicts or conceals it.
As far as any LLM is concerned, the data they're trained on and other data they're later fed is fact. Mimicking human behaviour such as lying still makes it lying.
But that still requires intent, because "knowing" in the way that you or I "know" things is fundamentally different from it only having a pattern matching vector that includes truthful arrangements of words. It doesn't know "sky is blue". It simply contains indices that frequently arrange the words "sky is blue".
Research papers that overlook this are still personifying a series of mathematical matrices as if it actually knows any concepts.
That's what the person you're replying to means. These machines don't know goddamn anything.
As far as we are concerned, the data a LLM is given is treated as fact by it though.
It does not matter whether something is factual or not. What matters is that whoever you're teaching, will accept it as fact and act in accordance with it. I don't see how this is any different with computer code. It will do what it is programmed to. If you program it to "think" a day has 36 hours instead of 24, it will do so.
By this logic, a lawnmower "thinks" my fingers are grass.
A lawnmower has no capacity to make decisions or process any data.
It's processing data alright, it processes the atomic and cellular structures of grass and fingers into spinach and flesh paste.
And likewise, neither it, nor any LLM, are making decisions at all.
Is a plinko disc making decisions as it tumbles from the top to the bottom through all those pegs? Is the board making the decision? Or is it neither and simply mathematics plus random chance being roped in for randomness? That is exactly what LLMs do.
Terms like "decision" and "lie" and "know" are all things that just do not apply to an LLM, just like your phone keyboard doesn't know what the fuck "what" and "the" are, it just has a lookup table that includes how "what" is often followed by "is" and "the", and "the" is frequently followed by "fuck". But it doesn't "know" that in any meaning of the word "know".
This is what we mean when we say not to personify. A training set of data, even factual, just is converted into a series of matrices of vectors that include those patterns, but not the information itself. "Sky is blue" is not something you can grep from the resulting blob, nor the hex equivalent, or anything else. It simply contains indexed patterns that map those arrangements of letters, over and over.
So yes, they're doing what they're programmed to do precisely. It's just that "what they're programmed to do" is only "mimic patterns of word arrangements", and not "know facts". These things work at a far lower level than that concept.
This isn't how language models are actually trained. In particular, language models don't have a sense of truth; they are optimizing next-token loss, not accuracy with regards to some truth model. Keep in mind that training against objective semantic truth is impossible because objective semantic truth is undefinable by a 1930s theorem of Tarski.
Except these algorithms don't "know" anything. They convert the data input into a framework to generate (hopefully) sensible text from literal random noise. At no point in that process is knowledge used.
I'm not sure anyone can truly claim to know that at this point. The equations these things solve to arrive at their outputs are incomprehensible to humans.
Yeah pretty sure they will awareness at this point