arXiv:2506.12815v2 Announce Type: replace Abstract: Recent advances in Trajectory Optimization (TO) models have achieved remarkable success in offline reinforcement learning. However, their vulnerabilities against backdoor attacks are poorly understood. We find that existing backdoor attacks in reinforcement learning are based on reward manipulation, which are largely ineffective against the TO model due to its inherent sequence modeling nature. Moreover, the complexities introduced by high-dimensional action spaces further compound the challenge of action manipulation. To address these gaps,
The increasing reliance on Trajectory Optimization models in areas like autonomous systems brings new vulnerabilities to the forefront, necessitating research into their security.
Understanding and addressing backdoor attacks in crucial AI models is vital for the reliable deployment of autonomous and AI-driven systems, impacting national security and economic stability.
This research highlights the shift in attack vectors from reward manipulation to action-level manipulation in advanced AI models, complicating existing defense strategies.
- · AI security researchers
- · Developers of robust AI defense mechanisms
- · Organizations prioritizing AI safety
- · Developers of insecure AI models
- · Users of unverified AI systems
- · Systems vulnerable to sophisticated cyber attacks
Increased focus on action-level security for trajectory optimization models in autonomous systems.
Development of new adversarial training techniques and ethical AI guidelines specifically for protecting complex AI decision-making processes.
Potential for a 'cyber arms race' in the realm of AI agents, where offensive and defensive capabilities rapidly evolve to exploit or protect critical AI infrastructure.
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Read at arXiv cs.LG