AI Agent Fraud Risk Engine

Behavioral fraud detection framework for autonomous AI agents.

Built by Zarelva — Fraud Intelligence & Risk Architecture.

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Fraud Signal Library

This project uses fraud detection signals defined in the Zarelva Fraud Signal Library.

https://github.com/Gururaj-GJ/fraud-signal-library

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Overview

This project explores fraud detection signals for autonomous AI agents. It provides a behavioral risk scoring framework that evaluates agent activity, delegation chains, network signals, and coordination patterns.

The goal is to detect compromised or malicious AI agents interacting with APIs, financial systems, and digital platforms.

While attestation systems verify agent identity and actions, they do not evaluate behavioral risk.

This project fills that gap.

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System Architecture

Autonomous Agent
      |
      v
Identity & Attestation Layer
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      v
Behavioral Signal Collection
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      v
Zarelva Risk Engine
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      v
Agent Trust Score
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      v
Allow / Review / Block

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Repository Structure

zarelva-agent-risk-engine/
|
+-- README.md              # Project overview and documentation
+-- agent_risk_engine.py   # Core risk scoring engine
+-- examples.py            # Usage examples and test scenarios
+-- signals.md             # Fraud Signal Library (full documentation)

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Key Features

• Behavioral Risk Scoring — evaluates agent actions against known fraud patterns
• Identity Signal Detection — flags new, revoked, or unknown agent identities
• Delegation Chain Analysis — detects privilege escalation and deep delegation
• Coordination Pattern Detection — identifies synchronized multi-agent activity
• Network Anomaly Signals — VPN, Tor, and datacenter IP detection
• Trust Score Output — produces Allow / Review / Block decisions

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Fraud Signal Categories

See signals.md for the full Fraud Signal Library.

Category	Example Signals
Identity	identity_age_new, identity_revoked, unknown_issuer
Behavioral	action_velocity_high, impossible_navigation_speed
Delegation	delegation_depth_high, privilege_escalation
Coordination	multi_agent_convergence, shared_credentials
Network	tor_exit_node, vpn_detected, datacenter_ip
Financial	unauthorized_payment_action, abnormal_payment_frequency

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The Emerging Threat Landscape

As autonomous agents become integrated into digital infrastructure, organizations must begin designing fraud detection frameworks specifically for agent ecosystems.

Traditional user-centric fraud models are insufficient to detect abuse in automated systems. Key emerging fraud types include:

1. Compromised Agent Fraud — valid identity, changed behavior
2. Delegation Chain Abuse — deep chains hiding malicious actions
3. Coordinated Agent Fraud Rings — botnet-style AI agent networks
4. Identity Cloning — duplicated agent signing keys
5. Autonomous Resource Abuse — API flooding, mass data extraction
6. Financial Automation Fraud — manipulated payment agents
7. Data Exfiltration via Agents — large reads followed by external writes

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Usage

from agent_risk_engine import ZarelvaRiskEngine

# Initialize the risk engine
engine = ZarelvaRiskEngine()

# Evaluate an agent
agent_data = {
    "agent_id": "agent-xyz-001",
    "identity_age_days": 1,
    "action_velocity": 150,
    "delegation_depth": 4,
    "network_type": "tor",
    "financial_actions": True
}

result = engine.evaluate(agent_data)
print(result)
# Output: {'trust_score': 12, 'decision': 'BLOCK', 'signals': [...]}

See examples.py for full usage scenarios.

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Research Context

This project is part of ongoing research at Zarelva into fraud intelligence for AI-native systems.

Related research areas:

• AI agent trust architecture
• Behavioral anomaly detection in automated workflows
• Delegation abuse in autonomous systems
• Device intelligence and fraud signals

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Author

Gururaj GJ
Founder — Zarelva
Fraud Intelligence & Risk Architecture
AI Agent Fraud & Trust Systems

Website: https://zarelva.com

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Keywords

ai-agent-fraud-detection autonomous-agent-risk behavioral-risk-signals agent-trust-score fraud-intelligence ai-security risk-scoring fraud-detection cybersecurity ai-agents

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Zarelva — Fraud Intelligence & Risk Architecture