EvoForge: The Dark Side of Evolving Memory

As LLM agents take on increasingly complex tasks, memory becomes essential for accumulating experience and maintaining continuity. Moving beyond static record storage, modern memory mechanisms continually integrate information from past interactions, turning memory into an evolving system for future decisions. This evolution also introduces formation risk: attackers can exploit memory's own revision and integration mechanisms to turn scattered inputs into guidance that steers future agent behavior toward adversarial goals. In light of this, we propose EvoForge, an adaptive framework that derives a behavioral memory fingerprint from benign probes to guide attack construction and diagnosis. EvoForge submits complementary fragments through ordinary interactions to limit the adversarial intent exposed in each input, then uses behavioral feedback to revise their content and dependencies as memory evolves. Across three agent tasks and five memory configurations, EvoForge achieves an average attack success rate of 87.55, exceeding baselines in each setting by an average of 37.69. Further analyses characterize attack robustness and substantiate the contributions of EvoForge's core mechanisms.