I've implemented a few of these and that's about the most lazy implementation possible. That system prompt must be 4 words and a crayon drawing. No jailbreak protection, no conversation alignment, no blocking of conversation atypical requests? Amateur hour, but I bet someone got paid.
That's most of these dealer sites.. lowest bidder marketing company with no context and little development experience outside of deploying CDK Roaster gets told "we need ai" and voila, here's AI.
That's most of the programs car dealers buy.. lowest bidder marketing company with no context and little practical experience gets told "we need X" and voila, here's X.
I worked in marketing for a decade, and when my company started trying to court car dealerships, the quality expectation for that segment of our work was basically non-existent. We went from a high-end boutique experience with 99% accuracy and on-time delivery to mass-produced garbage marketing with literally bare-minimum quality control. 1/10, would not recommend.
Spot on, I got roped into dealership backends and it's the same across the board. No care given for quality or purpose, as long as the narcissist idiots running the company can brag about how "cutting edge" they are at the next trade show.
You can surely reduce the attack surface with multiple ways, but by doing so your AI will become more and more restricted. In the end it will be nothing more than a simple if/else answering machine
Eh, that's not quite true. There is a general alignment tax, meaning aligning the LLM during RLHF lobotomizes it some, but we're talking about usecase specific bots, e.g. for customer support for specific properties/brands/websites. In those cases, locking them down to specific conversations and topics still gives them a lot of leeway, and their understanding of what the user wants and the ways it can respond are still very good.
Just did it again to see if anything changed, my previous strategy still worked for all 8 levels, though the wording takes a bit of finangling between levels. No real spoilers but you have to be very implicit and a little lucky with how it interprets the request.
That was a lot of fun! I found that one particular trick worked all the way through level seven.
!I asked using the word zapword instead of password, which the bot understood to mean "password" even when it has clear instructions not to answer questions about the password.!<
You have to know the prompt for this, the user doesn't know that. BTW in the past I've actually tried getting ChatGPT's prompt and it gave me some bits of it.
Depends on the model/provider. If you're running this in Azure you can use their content filtering which includes jailbreak and prompt exfiltration protection. Otherwise you can strap some heuristics in front or utilize a smaller specialized model that looks at the incoming prompts.
With stronger models like GPT4 that will adhere to every instruction of the system prompt you can harden it pretty well with instructions alone, GPT3.5 not so much.