arXiv:2505.19532v2 Announce Type: replace Abstract: The current state-of-the-art backdoor attacks against Reinforcement Learning (RL) rely upon unrealistically permissive access models, that assume the attacker can read (or even write) the victim's policy parameters, observations, or rewards. In this work, we question whether such a strong assumption is required to launch backdoor attacks against RL. To answer this question, we propose the \underline{S}upply-\underline{C}h\underline{a}in \underline{B}ackdoor (SCAB) attack, which targets a common RL workflow: training agents using external agen
The proliferation of advanced AI, particularly Reinforcement Learning, is leading to increased scrutiny of its security vulnerabilities, making supply-chain attacks a novel and timely threat vector.
This research reveals a significant vulnerability in the development and deployment of RL agents, demonstrating that malicious actors can inject backdoors without direct access to sensitive system parameters, undermining trust and security in AI systems.
The understanding of RL security shifts from focusing on direct parameter manipulation to acknowledging supply-chain vulnerabilities, requiring new approaches to verification and provenance in AI development.
- · AI security firms
- · Developers of robust AI verification tools
- · Organizations prioritizing secure AI supply chains
- · Organizations with immature AI security protocols
- · Developers relying on unverified external RL components
- · Sectors heavily dependent on critical RL agents
Increased awareness and demand for robust security measures within the AI development pipeline, particularly for RL applications.
Development of new industry standards and regulatory frameworks mandating supply-chain security for AI systems, similar to those in traditional software development.
An arms race between AI security and sophisticated attackers, leading to more resilient yet more complex AI systems and a specialized cybersecurity talent market.
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