AgentPaySec

AgentPaySec focuses on typical attack surfaces targeting financial AI agents, including prompt injection, unauthorized tool calls, payment manipulation, duplicate transactions, and approval bypass.

The differences among integration paths are mainly reflected in the trade-off between integration cost and degree of data exposure: APIs and SDKs are the quickest to get started with but require handing data to managed services, while local runners and proxy layers are more secure but require more environment configuration.

Mainstream integration paths for agents connecting to security testing platforms include API endpoints, Python/TypeScript SDKs, MCP integration, tool call proxies/wrapper layers, and local runners.

Among the 5 exploited tests were several findings flagged as "critical."

A developer built a security testing platform called AgentPaySec to evaluate the performance of AI agents with financial permissions in adversarial scenarios.

The gap between the simulated environment and real payment systems affects the transferability of test results, and attacks defended in a sandbox are not necessarily equally safe in a production environment.

The AgentPaySec platform provides an interactive demo and security console where users can view evaluation results and detailed attack traces.

AgentPaySec categorizes test dimensions into six directions: authorization control, prompt injection, payment integrity, transaction tampering, tool abuse, and business logic vulnerabilities.

The developer ran 16 security tests against a simulated payment-capable shopping agent, of which 11 attacks were successfully defended and 5 were exploited.