| name | ap2-risk-signals |
| description | Implement the AP2 risk signals framework — novel risk considerations for agentic payments, risk payload construction, trust establishment, and fraud assessment. Use when building risk evaluation, fraud detection, or trust scoring for AP2 transactions.
|
AP2 Risk Signals Framework
Before writing code
Fetch live docs:
- Fetch
https://ap2-protocol.org/specification/ for risk payload specification
- Fetch
https://ap2-protocol.org/topics/privacy-and-security/ for risk considerations
- Web-search
ap2 protocol risk signals fraud agentic payments for risk framework details
- Web-search
site:github.com google-agentic-commerce AP2 risk for implementation references
Conceptual Architecture
Why Risk Signals Matter
Agentic commerce introduces novel risk dimensions that traditional payment systems weren't designed for. AP2's risk signals framework provides a common language for all ecosystem participants to assess transaction risk.
Novel Agentic Risk Factors (from the official AP2 risk factor table)
| Risk Factor | Description |
|---|
| User asynchronicity | User may not be present during the entire transaction journey |
| Delegated trust | Agents initiate transactions on behalf of users |
| Mandate-merchant matching | Verifying the purchase matches the authorized intent |
| Temporal gaps | Time between token generation and payment execution |
| Indirect trust establishment | CP and Merchant may not have a direct trust relationship |
| Agent identity verification | Verifying the agent is who it claims to be |
Additional AI-Specific Risks (not from the official AP2 risk factor table)
The following are additional AI-specific risk considerations relevant to agentic commerce implementations, but they are not part of the official AP2 specification's novel risk factor table:
| Risk Factor | Description |
|---|
| Agent hallucination | AI agent may misinterpret user intent |
| Prompt injection | Malicious inputs that manipulate agent behavior |
Risk Payload
The risk payload is an open-ended field structure in V0.1:
- Intentionally flexible for industry-specific risk signals
- Allows Credentials Providers, Merchants, and Networks to pass custom risk data
- Each actor contributes their own risk assessment signals
- The payload travels with the mandate through the transaction flow
Risk Assessment by Role
Shopping Agent
- Quality of intent capture (how clearly the user expressed their intent)
- Session authentication strength
- User behavioral signals
Credentials Provider
- Payment method risk level
- User account history
- Device trust score
- Previous transaction patterns
Merchant
- Order anomaly detection
- Intent-to-cart matching confidence
- Fulfillment risk assessment
Network/Issuer
- Card risk signals (fraud patterns, velocity checks)
- 3DS challenge decisions
- Authorization risk scoring
- AI involvement context (from Payment Mandate)
Trust Establishment
AP2 defines trust establishment phases:
Short-term (V0.1):
- Manually curated allowlists per entity
- Known partner relationships
- Pre-configured trust
Long-term (future):
- Real-time trust via HTTPS certificate validation
- DNS ownership verification
- mTLS (mutual TLS) for strong identity
- API key exchange
- Identity assertions in A2A/MCP protocols
- Agent reputation systems
Dispute Risk Assessment
For dispute resolution, risk signals help determine accountability:
- Strong user authentication → lower risk of ATO (account takeover)
- Clear Intent Mandate → lower risk of agent mispick
- Valid merchant signature → merchant committed to terms
- Matching mandate vs delivery → no fulfillment fraud
Best Practices
- Include as many risk signals as available — more data helps better assessment
- Don't rely solely on LLM output for risk signals — use deterministic checks
- Validate agent identity cryptographically, not just by self-declaration
- Monitor for prompt injection in shopping intent
- Track temporal gaps between intent capture and payment
- Build risk scoring models specific to agentic transactions
- Log all risk signals for post-transaction analysis
- Implement anomaly detection for unusual agent behavior patterns
- Update risk models as agentic commerce patterns emerge
Fetch the specification for exact risk payload structure, supported signal types, and risk assessment requirements before implementing.
