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Multi-Agent Architecture

Coordinated Defense Agents

A multi-agent architecture that extends ADT to operate as distributed, collaborative defensive systems with formal protocols for coordination, consensus, and collective action.

Context

Why Single-Model Defense Fails at Scale

Modern infrastructure requires defense that spans heterogeneous domains and coordinates distributed actions.

Domain Complexity

A single model cannot maintain deep expertise across cloud, identity, network, endpoint, and application domains simultaneously.

Threat Traversal

Modern attacks traverse domain boundaries. Partial containment that addresses only one domain leaves attackers with continued access.

Coordination Requirements

Distributed actuation requires temporal coordination, causal consistency, rollback coordination, and blast radius containment.

Agent Types

Specialized Agent Types

Each CDA agent maintains deep domain expertise while contributing to unified threat models.

Identity Agent

User and service account behavior, authentication patterns, privilege usage, and access anomalies.

Cloud Agent

IAM policies, resource configurations, control plane activity, and cloud-native threats.

Network Agent

Flow patterns, connection anomalies, lateral movement indicators, and traffic analysis.

Endpoint Agent

Process behavior, file system activity, execution patterns, and endpoint persistence.

Application Agent

API usage, data access patterns, business logic anomalies, and application-layer attacks.

Protocols

Coordination Protocols

Formal protocols governing agent coordination within the CDA framework.

1

Agent Registration

New agents register with the collective through capability advertisement, policy acknowledgment, health commitment, and integration testing.

2

Hypothesis Sharing

Agents share threat hypotheses with structured messages including threat description, confidence assessment, supporting evidence, and expected confirmation/refutation.

3

Consensus Formation

Collective threat assessment follows three phases: hypothesis aggregation, evidence evaluation with weighted confidence, and consensus decision with dissent preservation.

4

Action Coordination

Coordinated response follows delegation protocols ensuring temporal ordering, causal consistency, and rollback capability across distributed environments.

Use Cases

End-to-end coordination flows demonstrating CDA capabilities.

Cross-Domain Lateral Movement

An attacker compromises identity, escalates cloud privileges, moves laterally through networks, and establishes endpoint persistence. CDA agents coordinate to detect the complete trajectory and execute simultaneous containment.

1
Identity Agent detects anomalous authentication
2
Hypothesis shared across agent collective
3
Cloud Agent confirms privilege escalation pattern
4
Network Agent detects lateral movement
5
Endpoint Agent identifies persistence mechanisms
6
Coordinated response executes across all domains

Coordinated Ransomware Containment

Ransomware deploys simultaneously across multiple endpoints with C2 communication through cloud services. CDA coordinates simultaneous isolation, blocking, and resource suspension.

1
Multiple Endpoint Agents detect encryption patterns
2
Hypotheses aggregated: coordinated ransomware
3
Network Agent identifies C2 communication
4
Cloud Agent identifies hosting resources
5
Simultaneous containment across all domains
6
Temporal coordination prevents continue commands

Supply Chain Attack Response

Compromised software update deploys with malicious behavior manifesting differently across domains. CDA coordinates deployment halt and rollback.

1
Cloud Agent detects anomalous deployment
2
Identity Agent detects service account changes
3
Endpoint Agent detects process anomalies
4
Collective hypothesis: supply chain compromise
5
Coordinated deployment halt and rollback
6
Domain-specific remediation with coordination

Threat Model

Multi-Agent Threat Model

Adversary capabilities specific to multi-agent defensive systems.

Agent Compromise: Attackers may compromise individual agents through supply chain, runtime exploitation, or credential theft

Consensus Manipulation: Flooding with false hypotheses, exploiting confidence aggregation, targeting dissent suppression

Coordination Interference: Network partitioning, message delay/reordering, protocol exploitation, resource exhaustion

Cascade Failures: Exploiting action dependencies, triggering false positives, creating conflicting hypotheses