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ap2-risk-signals

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.

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Atualizado há 5 meses

Ver no GitHub ↗ Licença: MIT

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AP2 Risk Signals Framework

Before writing code

Fetch live docs:

  1. Fetch https://ap2-protocol.org/specification/ for risk payload specification
  2. Fetch https://ap2-protocol.org/topics/privacy-and-security/ for risk considerations
  3. Web-search ap2 protocol risk signals fraud agentic payments for risk framework details
  4. 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.

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