Most AI security testing focuses on the model: prompt injection, jailbreaking, and output filtering.

We've been working on something different: testing the agent *system*. The protocols, integrations, and decision paths that determine what agents do in production. The result is a framework with 209 tests covering 4 wire protocols:

**MCP (Model Context Protocol)** Tool invocation security: auth, injection, data leakage, tool abuse, scope creep

**A2A (Agent-to-Agent)** Inter-agent communication: message integrity, impersonation, privilege escalation

**L402 (Lightning)** Bitcoin-based agent payments: payment flow integrity, double-spend, authorization bypass

**x402 (USDC/Stablecoin)** Fiat-equivalent agent payments: transaction limits, approval flows, compliance

Every test maps to a specific OWASP ASI (Agentic Security Initiatives) Top 10 category. Cross-referenced with NIST AI 800-2 categories for compliance reporting.

```

pip install agent-security-harness

```

20+ enterprise platform adapters included (Salesforce, ServiceNow, Workday, etc.).

MIT license. Feedback welcome. Especially from anyone running multi-agent systems in production. What attack vectors are we missing?