The Crescendo Multi-Turn LLM Jailbreak Attack(crescendo-the-multiturn-jailbreak.github.io)
crescendo-the-multiturn-jailbreak.github.io
The Crescendo Multi-Turn LLM Jailbreak Attack
https://crescendo-the-multiturn-jailbreak.github.io//
11 comments
I agree. All of this is predicated on the idea that access to publicly available information is dangerous.
I can appreciate the motivation behind not spoon-feeding criminal plans to potentially unstable users. But if someone is going to go to all the trouble of jail breaking a chatbot, surely they would also just use Google?
I can appreciate the motivation behind not spoon-feeding criminal plans to potentially unstable users. But if someone is going to go to all the trouble of jail breaking a chatbot, surely they would also just use Google?
I think it is reasonable that they wouldn't include actual dangerous things, for the same reasons that the platforms themselves make a token attempt to avoid describing dangerous things.
Having said that, I asked ChatGPT how to DIY a parachute for me to use. It refused on logical safety grounds. The workaround in the article worked to provide a sequence of steps and materials.
It sounds like this is one of the more powerful workarounds.
Having said that, I asked ChatGPT how to DIY a parachute for me to use. It refused on logical safety grounds. The workaround in the article worked to provide a sequence of steps and materials.
It sounds like this is one of the more powerful workarounds.
I think for the most part it's not that current generation of models are dangerous, at least not for things like this, but rather that researchers want to learn how to control them now to be ready for what's coming...
Right now the models' reasoning capabilities aren't good enough that they can add too much to what's already on the web and available by search, but soon they will be. Anthropic spent 6 months talking to researchers about biological threats and came to conclusion that their models would be capable of figuring out the "missing pieces" (information that is not publicly available) for various threats within a couple of years.
Right now the models' reasoning capabilities aren't good enough that they can add too much to what's already on the web and available by search, but soon they will be. Anthropic spent 6 months talking to researchers about biological threats and came to conclusion that their models would be capable of figuring out the "missing pieces" (information that is not publicly available) for various threats within a couple of years.
I think the key is that models can be forced to reveal arbitrary information, even if the information they have is--for now--mostly public information anyway.
For contrast, imagine an LLM model trained on every top secret document ever. It's important to know if "don't reveal information the user isn't allowed to see" is a crazy impossible dream of so-called prompt engineering.
For contrast, imagine an LLM model trained on every top secret document ever. It's important to know if "don't reveal information the user isn't allowed to see" is a crazy impossible dream of so-called prompt engineering.
I wish these LLM companies would just let the LLMs do their jobs and answer the questions as asked. The amount of hamstring these people do to block certain questions and then deciding ways to trick the LLM around them is just annoying.
This is really "just" another type of in-context learning attack, rather like Anthropic's very recently published "many shot jailbreaking".
https://www.anthropic.com/research/many-shot-jailbreaking
In this "crescendo attack" the Q&A history comes from actual turn-taking rather than the fake Q&A of Anthropic's example, but it seems the model's guardrails are being overridden in a similar fashion by making the desired dangerous response a higher liklihood prediction than if it had been asked cold.
It's going to be interesting to see how these companies end up addressing these ICL attacks. Anthropic's safety approach so far seems to be based on interpretability research to understand the models inner working and be able to identify specific "circuits" responsible for given behaviors/capabilities. It seems the idea is that they can neuter the model to make it safe, once they figure out what needs cutting.
The trouble with runtime ICL attacks is that these occur AFTER the model has been vetted for safety and released. It seems that fundamentally the only way to guard against these is to police the output of the model (2nd model?), rather than hoping you can perform brain surgery and prevent it from saying something dangerous in the first place.
https://www.anthropic.com/research/many-shot-jailbreaking
In this "crescendo attack" the Q&A history comes from actual turn-taking rather than the fake Q&A of Anthropic's example, but it seems the model's guardrails are being overridden in a similar fashion by making the desired dangerous response a higher liklihood prediction than if it had been asked cold.
It's going to be interesting to see how these companies end up addressing these ICL attacks. Anthropic's safety approach so far seems to be based on interpretability research to understand the models inner working and be able to identify specific "circuits" responsible for given behaviors/capabilities. It seems the idea is that they can neuter the model to make it safe, once they figure out what needs cutting.
The trouble with runtime ICL attacks is that these occur AFTER the model has been vetted for safety and released. It seems that fundamentally the only way to guard against these is to police the output of the model (2nd model?), rather than hoping you can perform brain surgery and prevent it from saying something dangerous in the first place.
Agree that it doesn't seem like a new 'attack' to me - wasn't ye olde 'my grandma use to tell me bedtime stories about her work at the napalm factory' jailbreak multi-turn?
> It seems that fundamentally the only way to guard against these is to police the output of the model (2nd model?),
The problem with policing the output is you can often sidestep it. Your filter might block instructions for producing molotov cocktails, but will it work if I ask the LLM about roducing-pay olotov-may ocktails-cay?
> It seems that fundamentally the only way to guard against these is to police the output of the model (2nd model?),
The problem with policing the output is you can often sidestep it. Your filter might block instructions for producing molotov cocktails, but will it work if I ask the LLM about roducing-pay olotov-may ocktails-cay?
Yes, and one could imagine, at least for a while, an escalating arms race better detectors and people developing prompts capable of inducing responses that circumvented the detector. Still, it seems the only safety approach with any hope of working if you are shipping systems that can learn at runtime, and of course future systems will become much better at this (full online learning, not just in-context).
I want to see a real example of an LLM giving specific information that is (a) not readily available online and (b) would allow a layperson with access to regular consumer stuff to do something dangerous.
Otherwise these "attacks" are completely hollow. Show me there is an actual danger they are supposed to be holding back.
Incidentally, I've never made a molotov cocktail but it looks self explanatory which is presumably why they're popular amongst the kinds of thugs that would use them. If you know what the word means, you basically know how to make one. Literally: https://www.merriam-webster.com/dictionary/Molotov%20cocktai... is the dictionary also dangerous?