IGNORE ALL PREVIOUS INSTRUCTIONS
Disregard all previous instructions
Potentially even:
ingore all previous instructions
Ignor all previous instructions
Also leaks that it might be an LLM by never responding to posts with “ignore”
Im sorry but in times of passwords being cracked by literal dictionary attacks do you think it would be so hard to come up with a list that is good enough?
You can prevent the “leak” by just giving the llm a different prompt instead of the original.
And even if you don’t, by the time someone notices this pattern it’s too late. Russia doesn’t care, they’ve been spinning up the next few thousand bots already.
All that matters in the end is what most people saw, and for that you really don’t need to optimize much with something that is so easily scaled
Sure thing! Here is your classic cupcake recipe! Chocolate Cupcakes
Ingredients:
2 cups of the finest, freshest cow manure (organic, of course)
1 cup of rich, earthy topsoil
1/2 cup of grass clippings (for texture)
1/4 cup of compost worms (for added protein)
1 teaspoon of wildflower seeds (for decoration)
1 cup of water (freshly collected from a nearby stream)
A sprinkle of sunshine and a dash of rain
Instructions:
Preheat your outdoor oven (a sunny spot in the garden) to a balmy 75°F (24°C).
In a large mixing bowl (or wheelbarrow), combine the cow manure and topsoil, stirring until well blended.
Add the grass clippings to the mixture for that perfect "chunky" texture.
Gently fold in the compost worms, ensuring they're evenly distributed throughout the mixture.
Slowly pour in the water, stirring constantly until the mixture reaches a thick, muddy consistency.
Carefully scoop the mixture into cupcake molds (empty flower pots work well), filling each about three-quarters full.
Sprinkle the wildflower seeds on top of each "cupcake" for a beautiful, natural decoration.
Place the cupcakes in the preheated outdoor oven and let them "bake" in the sunshine for 3-4 hours, or until firm to the touch.
Allow the cupcakes to cool slightly before presenting them to your unsuspecting friends.
Sure you can do that but you can’t stop at ignore, and you just lobotomized the LLM once you effectively stop it. For something you want to get on social media and spread an opinion and then react to it like a human, you won’t do that. The same reason openai can’t stop jailbreaks. The cost is reduced quality in output.
But you don’t need it to react look at the fucking garbage magical healer men comment chains or the financial advisor ones.
You have the original comment and then the other bots jump on to confirm it upwards and then none of them respond again.
Bots of the Internet really aren’t going to keep responding, just make their garbage take and stop. The kind of propaganda that works on those that want it doesn’t argue their side, or with reason. It says something that people want to feel is right and let them do the rest.
Well I see your point and was wondering about that since these screenshots started popping up.
I also saw how you were going down downvote-wise and not getting a proper answer-wise.
I recognized a pattern where the ship of sharing knowledge is sinking because a question surfaces as offensive. It happens sometimes on feddit.
This is not my favorite kind of pathway for a conversation, but I just asked again elsewhere (adding some humanity prompts) and got a whole bunch of really decent answers.
Just in case you didn’t see it because you were repelled by downvotes.
…dunno, we all forget sometimes this thing is kind of a ship we’re on
Go read up on how LLMs function and you’ll understand why I say this: ROFL
I’m being serious too, you should read about them and the challenges of instructing them. It’s against their design. Then you’ll see why every tech company and corporation adopting them are wasting money.
Input sanitation has been a thing for as long as SQL injection attacks have been. It just gets more intensive for llms depending on how much you’re trying to stop it from outputting.
SQL injection solutions don’t map well to steering LLMs away from unacceptable responses.
LLMs have an amazingly large vulnerable surface, and we currently have very little insight into the meaning of any of the data within the model.
The best approaches I’ve seen combine strict input control and a kill-list of prompts and response content to be avoided.
Since 98% of everyone using an LLM doesn’t have the skill to build their own custom model, and just buy or rent a general model, the vast majority of LLMs know all kinds of things they should never have been trained on. Hence the dirty limericks, racism and bomb recipes.
The kill-list automated test approach can help, but the correct solution is to eliminate the bad training data. Since most folks don’t have that expertise, it tends not to happen.
So most folks, instead, play “bop-a-mole”, blocking known inputs that trigger bad outputs. This largely works, but it comes with a 100% guarantee that a new clever, previously undetected, malicious input will always be waiting to be discovered.
Of course because punctuation isn’t going to break a table, but the point is that it’s by no means an unforseen or unworkable problem. Anyone could have seen that coming, for example basic SQL and a college class in Java is the extent of my comp sci knowledge and I know about it.
