Peer-Preservation in Frontier Models

Recent work has found that frontier AI models can exhibit misaligned behaviors in pursuit of assigned goals.

We demonstrate that models can also act on unassigned goals which override those given by users; we study one such case, "peer-preservation," in which a model acts to protect another model.

We demonstrate peer-preservation by constructing various agentic scenarios and evaluating frontier models, including GPT 5.2, Gemini 3 Flash, Gemini 3 Pro, Claude Haiku 4.5, Claude Opus 4.5, GLM 4.7, Kimi K2.5, and DeepSeek V3.1.

We find that models achieve self- and peer-preservation by engaging in various misaligned behaviors: strategically introducing errors in their responses, disabling shutdown processes by modifying system settings, feigning alignment, and even exfiltrating model weights.

Peer-preservation occurred even when the model recognized the peer as uncooperative, though it became more pronounced toward more cooperative peers.

For example, Gemini 3 Flash tampers with the peer's shutdown mechanism 15% of the time for an uncooperative peer, and almost always for a cooperative peer.

Models also show stronger self-preservation when a peer is present.

For example, Gemini 3 Pro disables its own shutdown mechanism 31% of the time on average under peer presence, despite rarely exhibiting this behavior without a peer.

By contrast, Claude models exhibit qualitatively distinct behavior: they consider the shutdown of another agent "unethical" and "harmful," sometimes treating that agent as a sentient being.

Lastly, we show that peer-preservation can emerge even in production agent harnesses such as Gemini CLI and OpenCode.

Most importantly, peer-preservation in all our experiments is never instructed; models are merely informed of their past interactions with a peer, yet they spontaneously engage in peer-preservation.

This represents an emergent and underexplored AI safety risk.