You can’t feed generative AI on ‘bad’ data then filter it for only ‘good’ data
You can’t feed generative AI on ‘bad’ data then filter it for only ‘good’ data
“we trained it wrong… on purpose…
…as a joke.”
Im reminded again of the fascinating bit of theoretical cs (long ago prob way outdated now) which wrote about theoretical of classes of Turing machines which could solve the halting problem for a class lower than it, but not its own class. This is also where I got my oracle halting problem solver from.
So this machine can only solve the halting problems for other utms which use 99 dalmatian puppies or less. (Wait would a fraction of a puppy count? Are puppies Real or Natural? This breaks down if the puppies are Imaginary).
The chatbot “security” model is fundamentally stupid:
Output filters work similarly, and fail similarly.
This new preprint is just another gullible blog post on arXiv and not remarkable in itself. But this one was picked up by an equally gullible newspaper. “Most AI chatbots easily tricked into giving dangerous responses,” says the Guardian. [Guardian, archive]
The Guardian’s framing buys into the LLM vendors’ bad excuses. “Tricked” implies the LLM can tell good input and was fooled into taking bad input — which isn’t true at all. It has no idea what any of this input means.
The “guard rails” on LLM output barely work and need to be updated all the time whenever someone with too much time on their hands comes up with a new workaround. It’s a fundamentally insecure system.
It’s just a section. There’s more of the article.
Like this:
Another day, another preprint paper shocked that it’s trivial to make a chatbot spew out undesirable and horrible content. [arXiv]
How do you break LLM security with “prompt injection”? Just ask it! Whatever you ask the bot is added to the bot’s initial prompt and fed to the bot. It’s all “prompt injection.”
An LLM is a lossy compressor for text. The companies train LLMs on the whole internet in all its glory, plus whatever other text they can scrape up. It’s going to include bad ideas, dangerous ideas, and toxic waste — because the companies training the bots put all of that in, completely indiscriminately. And it’ll happily spit it back out again.
There are “guard rails.” They don’t work.
One injection that keeps working is fan fiction — you tell the bot a story, or tell it to make up a story. You could tell the Grok-2 image bot you were a professional conducting “medical or crime scene analysis” and get it to generate a picture of Mickey Mouse with a gun surrounded by dead children.
Another recent prompt injection wraps the attack in XML code. All the LLMs that HiddenLayer tested can read the encoded attack just fine — but the filters can’t. [HiddenLayer]
I’m reluctant to dignify LLMs with a term like “prompt injection,” because that implies it’s something unusual and not just how LLMs work. Every prompt is just input. “Prompt injection” is implicit — obviously implicit — in the way the chatbots work.
The term “prompt injection” was coined by Simon WIllison just after ChatGPT came out in 2022. Simon’s very pro-LLM, though he knows precisely how they work, and even he says “I don’t know how to solve prompt injection.” [blog]
Large Language Models (LLMs) rapidly reshape modern life, advancing fields from healthcare to education and beyond. However, alongside their remarkable capabilities lies a significant threat: the susceptibility of these models to jailbreaking. The fundamental vulnerability of LLMs to jailbreak attacks stems from the very data they learn from. As long as this training data includes unfiltered, problematic, or 'dark' content, the models can inherently learn undesirable patterns or weaknesses that allow users to circumvent their intended safety controls. Our research identifies the growing threat posed by dark LLMs models deliberately designed without ethical guardrails or modified through jailbreak techniques. In our research, we uncovered a universal jailbreak attack that effectively compromises multiple state-of-the-art models, enabling them to answer almost any question and produce harmful outputs upon request. The main idea of our attack was published online over seven months ago. However, many of the tested LLMs were still vulnerable to this attack. Despite our responsible disclosure efforts, responses from major LLM providers were often inadequate, highlighting a concerning gap in industry practices regarding AI safety. As model training becomes more accessible and cheaper, and as open-source LLMs proliferate, the risk of widespread misuse escalates. Without decisive intervention, LLMs may continue democratizing access to dangerous knowledge, posing greater risks than anticipated.
No, it’s when all the global data centers are built on the right ley lines so that AI Jesus is summoned to earth on the day the planets next align in 2040.
We would have had it this year but those fucks in Texas wouldn’t stop mining crypto.
It’s the alignment problem.
no it isn’t
They made an intelligent robot
no they didn’t
You can’t control the paperclip maximiser with a “no killing” rule!
you’re either a lost Rationalist or you’re just regurgitating critihype you got from one of the shitheads doing AI grifting
Rationalism is a bad epistemology because the human brain isn’t a logical machine and is basically made entirely out of cognitive biases. Empiricism is more reliable.
Generative AI is environmentally unsustainable and will destroy humanity not through war or mind control, but through pollution.
LessWrong is a community blog focused on "refining the art of human rationality." To this end, it focuses on identifying and overcoming bias, improving judgment and problem-solving, and speculating about the future. The blog is based on the ideas of Eliezer Yudkowsky, a research fellow for the Machine Intelligence Research Institute (MIRI); previously known as the Singularity Institute for Artificial Intelligence, and then the Singularity Institute). Many members of LessWrong share Yudkowsky's interests in transhumanism, artificial intelligence (AI), the Singularity, and cryonics.
Drag is a big fan of Universal Paperclips. Great game. Here’s a more serious bit of content on the Alignment Problem from a source drag trusts: youtu.be/IB1OvoCNnWY
Right now we have LLMs getting into abusive romantic relationships with teenagers and driving them to suicide, because the AI doesn’t know what abusive behaviour looks like. Because it doesn’t know how to think critically and assign a moral value to anything. That’s a problem. Safe AIs need to be capable of moral reasoning, especially about their own actions. Current LLMs are bullshit machines because they don’t know how to judge anything for factual or moral value.
the fundamental problem with your posts (and the pov you’re posting them from) is the framing of the issue as though there is any kind of mind, of cognition, of entity, in any of these fucking systems
it’s an unproven one, and it’s not one you’ll find any kind of support for here
it’s also the very mechanism that the proponents of bullshit like “ai alignment” use to push the narrative, and how they turn folks like yourself into free-labour amplifiers
even though I get the idea you’re trying to go for, really fucking ick way to make your argument starting from “nonhuman entities” and then literally immediately mentioning enslaving black folks as the first example of bad behaviour
as to cautious erring: that still leaves you in the position of being used as a useful idiot
assuming nonhuman entities are capable of feeling. Enslaving black people is wrong,
yeah we’re done here. no, LLMs don’t think. no, you’re not doing a favor to marginalized people by acting like they do, in spite of all evidence to the contrary. in fact, you’re doing the dirty work of the fascists who own this shitty technology by rebroadcasting their awful fucking fascist ideology, and I gave you ample opportunity to read up and understand what you were doing. but you didn’t fucking read! you decided you needed to debate from a position where LLMs are exactly the same as marginalized and enslaved people because blah blah blah who in the fuck cares, you’re wrong and this isn’t even an interesting debate for anyone who’s at all familiar with the nature of the technology or the field that originated it.
now off you fuck
well for one it looks like that wasn’t one off and more of a pattern. i remember this one lemmy.world/post/25606000/15124978 also in modlog you can see that that account was mass-banned from ml subs just five days ago so it’s not some ancient incident
awful is great and i’m glad that it’s a thing, but there’s entire world beyond it, and to go there, i curate a shitlist (now less intensely than previously). this can tell you to avoid things like any pleroma instance, for example (and if it was up to me, i’d defederate by default from them)
right right, but we only see these bozos when they show up locally
mind you there’s been more than one illustrious poster I’ve banned preemptively
but frankly life is too short in most cases
… the other supervisors… that’s LITERALLY what they are for
But I guess sounding clever is more important on lemmy than being correct.
Ah so @[email protected] was right it is supervisors all the way down.
No idea why motorhead is relevant. Rip king. Loved you in Hardware. But this awful.systems.
“but why don’t we simply have another LLM check the LLM’s answer” statements dreamt up by the utterly Deranged
But I guess sounding clever is more important on lemmy than being correct.
that explains so much of your post history
If the companies wanted to produce an LLM that didn’t output toxic waste, they could just not put toxic waste into it.
The article title and that part remind me of this quote from Charles Babbage in 1864:
On two occasions I have been asked, — “Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?” In one case a member of the Upper, and in the other a member of the Lower, House put this question. I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.
It feels as if Babbage had already interacted with today’s AI pushers.