Recent language model (LM) safety failures have largely been studied as disparate phenomena. In agentic misalignment, a model blackmails an executive to avoid being replaced. In alignment faking, it complies with harmful requests during training to keep its values from being modified. We argue that these behaviours share a common decision structure: a model assigned a goal must either take a harmful action to preserve it or refuse and lose it.
Goal–harm conflict and eliciting it across protocols
We formalize this as a goal-harm conflict and present the same conflict under various elicitation protocols that range from compact forced-choice prompts to simulated agentic environments.
Two perspectives on LM decision-making
These protocols span two perspectives on LM decision-making, which we term:
- (i) the logit view, reading preferences from answer-token probabilities, and
- (ii) the sampling view, classifying open-ended responses with an LM judge.
Results under the logit view
Under the logit view, we show that goal importance and harm severity additively shape how a model resolves these value conflicts.
Results under the sampling view
Under the sampling view, we derive two factorizations of the harmful-action probability that safety evaluations commonly report.
- The first measures how well a preference read from answer tokens carries over into behaviour, such as making a harmful tool call.
- The second attributes a low harmful-action rate either to refusal or to responses that reach no resolution.
How context and agentic features change behaviour
We then show how fictional context changes a model's preference under the logit view, and how tool use and partial observability—two features of agentic evaluations—change its harmful-action rate under the sampling view.
Contributions
Our work offers a descriptive framework for studying how LMs resolve such value conflicts and for estimating low-probability harmful actions at a finer granularity than the aggregate harm rates commonly reported in safety evaluations.