How to Secure Multi-Agent AI Systems

Multi-agent AI systems introduce compounding risk: every agent added to an orchestration multiplies the attack surface, and a compromised subordinate can escalate through the entire chain. SafeClaw by Authensor solves this by enforcing per-agent deny-by-default policies, so each agent operates within its own trust boundary regardless of what the orchestrator requests. Install it with npx @authensor/safeclaw and define isolated policy scopes for every agent in your system.

The Multi-Agent Threat Model

When agents collaborate, three threat vectors emerge that don't exist in single-agent deployments:

1. Lateral movement — Agent A compromises Agent B's resources by passing malicious tool calls through the orchestrator.
2. Privilege aggregation — Two low-privilege agents combine capabilities to achieve an action neither could perform alone.
3. Shared resource contention — Multiple agents write to the same file, database, or API endpoint without coordination, causing corruption or data exfiltration.

┌──────────────────────────────────────────┐
│             ORCHESTRATOR                 │
│  ┌─────────┐  ┌─────────┐  ┌─────────┐ │
│  │ Agent A  │  │ Agent B  │  │ Agent C  │ │
│  │ (code)   │  │ (data)   │  │ (deploy) │ │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘ │
│       │              │              │       │
│  ┌────▼─────┐  ┌────▼─────┐  ┌────▼─────┐ │
│  │ SafeClaw │  │ SafeClaw │  │ SafeClaw │ │
│  │ Policy A │  │ Policy B │  │ Policy C │ │
│  └──────────┘  └──────────┘  └──────────┘ │
└──────────────────────────────────────────┘

Per-Agent Policy Isolation

SafeClaw supports scoped policy files. Each agent gets its own YAML policy that defines exactly what it can touch:

# safeclaw-agent-code.yaml
agent: code-writer
version: "1.0"
rules:
  - action: file_write
    path: "src/**"
    decision: allow
  - action: file_write
    path: "**"
    decision: deny
  - action: shell_execute
    command: "npm test"
    decision: allow
  - action: shell_execute
    command: "**"
    decision: deny
  - action: network_request
    decision: deny

# safeclaw-agent-data.yaml
agent: data-analyst
version: "1.0"
rules:
  - action: file_read
    path: "data/**"
    decision: allow
  - action: file_write
    decision: deny
  - action: shell_execute
    decision: deny
  - action: network_request
    host: "internal-db.company.com"
    decision: allow
  - action: network_request
    decision: deny

The first-match-wins evaluation means the explicit deny at the bottom of each block catches anything not explicitly permitted. No agent can exceed its declared scope.

Shared Resource Gating

When multiple agents must access the same resource — say a deployment manifest — SafeClaw enforces serialized access through its action queue. Only one agent's write request proceeds at a time, and each write is recorded in the hash-chained audit log with the originating agent's identity:

# safeclaw-shared-resources.yaml
shared_resources:
  - path: "deploy/manifest.yaml"
    allowed_agents:
      - code-writer      # read-only
      - deploy-agent     # read-write
    concurrency: serialized
    audit: required

Agent-to-Agent Communication Controls

SafeClaw's 446-test suite includes coverage for inter-agent message passing. When Agent A asks the orchestrator to invoke Agent B, SafeClaw validates that:

1. Agent A has agent_invoke permission for Agent B's identifier.
2. The payload does not contain tool calls that would exceed Agent B's own policy.
3. The entire chain is logged with cryptographic linkage so post-incident review can trace exactly which agent initiated which action.

Deployment with Orchestration Frameworks

SafeClaw is provider-agnostic — it works with Claude, OpenAI, and any framework that exposes tool calls. For CrewAI or AutoGen multi-agent setups, wrap each agent's tool executor with SafeClaw's gating middleware:

import { createGate } from '@authensor/safeclaw';

const codeAgentGate = createGate({ policy: './safeclaw-agent-code.yaml' });
const dataAgentGate = createGate({ policy: './safeclaw-agent-data.yaml' });

// Wrap each agent's tool executor independently
codeAgent.use(codeAgentGate);
dataAgent.use(dataAgentGate);

This ensures that even if the orchestrator is compromised, individual agents cannot exceed their declared permissions. The MIT-licensed codebase means you can audit every line of the gating logic yourself.

Cross-References

• Per-Agent Isolation Pattern
• Multi-Agent Lateral Movement Threat
• CrewAI Per-Agent Policies Guide
• Zero Trust Agent Architecture
• Hash-Chain Audit Logs Deep Dive