arXiv:2506.06891v3 Announce Type: replace Abstract: We study the corruption-robustness of in-context reinforcement learning (ICRL), focusing on the Decision-Pretrained Transformer (DPT, Lee et al., 2023). To address the challenge of reward poisoning attacks targeting the DPT, we propose a novel adversarial training framework, called Adversarially Trained DPT (AT-DPT). Our method simultaneously trains a population of attackers to minimize the true reward of the DPT by poisoning environment rewards, and a DPT model to infer optimal actions from the poisoned data. We evaluate the effectiveness of
The increasing deployment of in-context learning models like Decision-Pretrained Transformers necessitates immediate research into their robustness against adversarial attacks, especially as AI systems become more autonomous.
This research highlights a critical vulnerability in advanced AI systems, demonstrating that even sophisticated reinforcement learning can be manipulated, which has implications for the reliability and trustworthiness of AI agents in sensitive applications.
The ability to 'poison' reward systems in AI training environments changes the threat landscape for autonomous agents, requiring developers to integrate more robust adversarial training from the outset.
- · AI security researchers
- · Adversarial training framework developers
- · Companies prioritising robust AI
- · Deployment of unhardened ICRL systems
- · AI systems vulnerable to data poisoning
- · Sectors reliant on unverified AI agent outputs
This work directly leads to the development of more secure and resilient in-context reinforcement learning models.
It could accelerate the adoption of adversarial AI training as a standard practice across various industries, shifting development paradigms.
The heightened awareness of AI vulnerabilities might influence regulatory bodies to mandate specific security standards for autonomous AI systems, impacting their ethical deployment.
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