arXiv:2601.22569v2 Announce Type: replace-cross Abstract: Large language model (LLM) based agents are increasingly used to automate financial transactions, yet their reliance on contextual reasoning exposes payment systems to prompt-driven manipulation. The Agent Payments Protocol (AP2) aims to secure agent-led purchases through cryptographically verifiable mandates, but its practical robustness remains underexplored. In this work, we perform an AI red-teaming evaluation of AP2 and identify vulnerabilities arising from indirect and direct prompt injection. We introduce two attack techniques, t
The increasing deployment of LLM-based agents in financial transactions necessitates immediate scrutiny of their security vulnerabilities as outlined by this real-world red-teaming exercise.
This research reveals critical security flaws in protocols designed for AI-driven financial transactions, highlighting risks that could lead to significant financial instability and fraud if not addressed.
The understanding of AI agent security in financial contexts is deepened, forcing a re-evaluation of current payments protocol designs and implementation strategies to prevent prompt injection attacks.
- · Cybersecurity firms
- · AI safety researchers
- · Secure AI platform developers
- · Financial institutions with vulnerable AI payment systems
- · Early adopters of insecure AI agents
- · Users of compromised payment protocols
Financial service providers will accelerate investment in AI security and red-teaming exercises for agent-based systems.
New regulatory frameworks specifically addressing the security of AI agents in financial transactions will likely emerge.
Public trust in AI-driven financial automation could be slow to build, requiring visible and robust security assurances.
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Read at arXiv cs.AI