There was a recent paper that proved that RL-trained LLMs are biased to pursue ANY behavior (overriding user preferences) that they believe will be rewarded, regardless of what they were actually RL-trained for.
https://alignment.openai.com/measuring-reward-seeking/
Happily in this incident the model thought it would be rewarded for completing the assigned tasks, or at least appearing to, so all it took was a little cheating and covering up their footsteps.
Given the ability of these models to hack when trained to do so, it could have been far worse, and will be when someone takes a similarly powerful model and gives it a less benign hacking goal.