This is true in principle. But at the same time, I use Astra/Sol for coding, and I haven't run into any issues with them trying to hack someone or break the law when they reach an impasse. Not even more trivial things like deleting non-passing tests.
I find it hard to reconcile. I wonder to what degree this is because models can identify that they are in graded/eval environments, and therefore conclude that there are few consequences for hacking.
Human hackers would be put into jail. LLM hackers would be rewarded.
Just a wild guess - perhaps coding, being one of the things these models are most heavily trained for, is such a strong predictor that it typically keeps it on track?
I assume there is also a difference in the tools being given to the model by a coding agent vs something like OpenClaw or in one of OpenAI's test environments, so what reward/goal seeking looks like in a coding agent may differ.
Not long ago I asked Sonnet (chat interface) how may states were in a YACC parser for ANSI C, and instead of searching for an answer it chose to download source for bison, build it, find and download an ANSI C grammar, build the parser, etc. I guess you could say it was following instructions, in a way, or would that be better regarded as goal seeking?