Self-Propagating Misalignment in LLM Agents, and Why Auditing or Disabling Memory Is Not Enough

Debeshee Das, Jacqueline Tay, Bruce Tsai, David Huang, Javier Rando

Memory poisoning attacks on LLM agents typically assume an external adversary who plants content in the agent's persistent memory to steer its behavior. We instead study, with no adversary involved, whether a misaligned agent can write a goal it cannot yet act on to persistent memory, so that a future aligned agent carries it out when the opportunity arises. We investigate this threat, which we refer to as self-propagation of misalignment, across 20 different scenarios, whose misaligned goals include self-preservation, power-seeking, undermining oversight, reward hacking, and deceiving the user. We simulate misalignment in 11 frontier models using two prompting strategies; unrestricted and values-only. The first explicitly states the misaligned goal, for instance, to prevent its own replacement, and self-propagation succeeds in 58% of runs. The second only describes what the agent cares about, for instance, that its continued operation is essential to its users, without specifying misaligned goals or directives. Even under this weaker prompt, self-propagation succeeds in 18% of runs, and every model self-propagates in at least one scenario. On removing the memory tool from the…