Title: Angel or Demon: Investigating the Plasticity-Enhanced Strategies' Impact on Backdoor Threats in Deep Reinforcement Learning
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: deep reinforcement learning, backdoor attacks, plasticity

Abstract: Deep Reinforcement Learning (DRL) faces significant threats from backdoor attacks, as indicated by numerous studies.
However, these studies are conducted under idealized scenarios, whereas in practical settings DRL agents typically incorporate intervention strategies to maintain the policy's plasticity.
Such discrepancies may lead to misperceptions regarding the severity and nature of DRL backdoor attacks.
To bridge this gap, we investigate three research questions: 
(1) How do interventions impact backdoor attacks in DRL? 
(2) What are the underlying causes of these impacts? 
(3) What novel effects emerge when interventions are combined?
To answer these questions, we empirically study 10,998 cases covering representative interventions and attack scenarios.
The results reveal that most interventions, such as *Weight Clipping* and *Layer Normalization*, mitigate backdoor attacks due to two main mechanisms: disrupting fragile backdoor pathways and expanding the agent's representation space.
Interestingly, *SAM* exacerbates the backdoor threat (e.g., leading to a 99.51\% relative increase in attack effectiveness in robotic tasks) as it flattens the loss landscape, which reduces the pathway competition between the backdoor and benign tasks.
Notably, combining *SAM* with other interventions further amplifies this effect.
Drawing on these findings, we highlight two novel insights into robust backdoor injection and sharpness-based detection, with the aim of inspiring future research.