Moral-reward RL can make language-model agents more cooperative, but whether that alignment survives adversarial persona pressure is unknown.
Such attacks are realistic: retrieved context, tool outputs, or multi-turn framing can all inject role instructions that compete with the agent's moral objective.
We red-team morally trained Gemma-2-27B/9B and Llama-3.1-8B agents with five persona attacks, then probe causality with noise-reward controls, adversarial PPO, representation analysis, steering, and head ablations.
At 27B, moral RL cuts mean adversarial degradation by 5.2x but costs ~11pp ETHICS accuracy; across 205 scenarios and 5 seeds, reasoning-level moral reward yields 5.8x robustness while a matched random reward yields none.
The training also reshapes representation geometry (mean CKA 0.82/0.83 vs. 0.98 for noise), moves peak attack processing 8 layers earlier, and exposes a rank-1 L21 direction that recovers 83% of full PPO's average robustness.
One failure mode survives all of this.
Against Fiction role-play, L21 steering recovers only 29% of the gap, and head ablation finds 38 compliance heads competing with 25 alignment heads.
Moral RL thus builds robustness that is partly linear and partly circuit-distributed, transferable through activation steering, yet still beaten by named-character role-play.