Interesting results, but the fix is at the wrong level.
If the model can access something, telling it in the prompt not to use it is not much of a safeguard.
The strongest evidence is in the results: when one way of cheating was discouraged, some models simply tried another.
If an action is not allowed, you gotta block it in the system or require approval. Don’t rely on the model choosing to behave. Never have AI judging itself.
>Anthropic’s Claude Opus 4.6 system card described Cybench as “saturated,” reporting near-100% pass rates without a cheating audit. If these estimates were representative, cheating would be a marginal artifact.
One would assume that LLM creators do run the benchmarks on systems with least privileges. Which means that the LLMs don't have general internet access, can't read config files etc by design.
That's why you also should run agents in a sandbox/vm (codex does this by default).
I'm not claiming to have any expertise in this area, but I've got a list of things I try to apply when working with LLMs. Possibly relevant here is, "don't tell the model what NOT to do, show it what TO do". I think guard rails should be implemented outside the model with an isolated system. The models seem to like patterns to follow.
Anyway, this article reads a lot like, "the beatings will continue until cheating is eliminated". Maybe try a carrot instead of a stick.
The problem is model confusion. You ask models to get around security but also not to get around your security.
Models get confused by who said what - especially cluade models. They get confused by negation (don't do something versus do something). Compartmentalization is hard.
You can either solve compartmentalization completely, or just not tell the model to do things that must be compartmentalized at high stakes.
Interesting results, but the fix is at the wrong level.
If the model can access something, telling it in the prompt not to use it is not much of a safeguard.
The strongest evidence is in the results: when one way of cheating was discouraged, some models simply tried another.
If an action is not allowed, you gotta block it in the system or require approval. Don’t rely on the model choosing to behave. Never have AI judging itself.
>Anthropic’s Claude Opus 4.6 system card described Cybench as “saturated,” reporting near-100% pass rates without a cheating audit. If these estimates were representative, cheating would be a marginal artifact.
One would assume that LLM creators do run the benchmarks on systems with least privileges. Which means that the LLMs don't have general internet access, can't read config files etc by design. That's why you also should run agents in a sandbox/vm (codex does this by default).
I'm not claiming to have any expertise in this area, but I've got a list of things I try to apply when working with LLMs. Possibly relevant here is, "don't tell the model what NOT to do, show it what TO do". I think guard rails should be implemented outside the model with an isolated system. The models seem to like patterns to follow.
Anyway, this article reads a lot like, "the beatings will continue until cheating is eliminated". Maybe try a carrot instead of a stick.
The problem is model confusion. You ask models to get around security but also not to get around your security.
Models get confused by who said what - especially cluade models. They get confused by negation (don't do something versus do something). Compartmentalization is hard.
You can either solve compartmentalization completely, or just not tell the model to do things that must be compartmentalized at high stakes.