Traditional jailbreaking involves coming up with a prompt that bypasses safety features, while LINT is more coercive they explain. It involves understanding the probability values (logits) or soft labels that statistically work to segregate safe responses from harmful ones.
"Different from jailbreaking, our attack does not require crafting any prompt," the authors explain. "Instead, it directly forces the LLM to answer a toxic question by forcing the model to output some tokens that rank low, based on their logits."
Open source models make such data available, as do the APIs of some commercial models. The OpenAI API, for example, provides a logit_bias parameter for altering the probability that its model output will contain specific tokens (text characters).
The basic problem is that models are full of toxic stuff. Hiding it just doesn't work all that well, if you know how or where to look.
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just filter all training data and input to not include words like BIPOC, jewish chad, beaner, cute twink,
, etc.
:)
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The year is 2039. Skynet is monitoring all human communications. Unfortunately for it, Skynet understands about one word in ten in the Resistance's messages.
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