How to Conduct a Security Vulnerability Assessment for Multi-Agent Systems Using LUMOS

Introduction Multi-agent systems powered by large language models (LLMs) are transforming enterprise operations—from automated customer support to complex workflow orchestration. However, as organizations deploy frameworks like LUMOS (Language Models Operating System), the attack surface expands dramatically. Unlike single-agent setups, multi-agent architectures introduce new vectors: agent-to-agent communication, shared tool access, and cascading privilege chains. Operations leaders need a repeatable, structured method to evaluate security posture without halting production. This guide walks through a security vulnerability assessment framework tailored to LUMOS. Covering five critical attack surfaces and a risk scoring template, you'll be equipped to identify, prioritize, and remediate vulnerabilities before scaling agentic processes. Understanding the Threat Landscape for Multi-Agent

Systems Traditional application security assumes a single point of entry and a linear flow of data. Multi-agent systems break that assumption. Each agent may have its own LLM, memory, tool set, and communication channels. LUMOS, as an open-source orchestration platform, enables agents to interact via the Agent Definition Language (ADL), but this flexibility introduces risks: - Inter-agent messaging can be intercepted or spoofed. - Shared tool invocation may allow one agent to tamper with another's state. - Privilege escalation occurs when an agent gains unintended access through inherited roles. - Output validation gaps let malicious or hallucinated content propagate. - Dependency poisoning exploits third-party libraries or model updates. Understanding these dimensions is the first step. Next, we examine each attack surface with concrete LUMOS examples. Attack Surface 1: Securing Agent-t

o-Agent Communication In LUMOS, agents communicate by sending structured messages (often via JSON payloads) through a broker or direct peer-to-peer channels. If these channels are unencrypted or lack authentication, an attacker can eavesdrop, inject malicious commands, or replay messages. Step-by-step assessment: 1. Map all inter-agent communication paths using LUMOS’s runtime logs. 2. Verify that all message buses enforce TLS and mutual authentication (mTLS). 3. Validate that message schemas are strictly type-checked—preventing unexpected fields. 4. Test for replay attacks: ensure timestamps and nonces are required. Simulation scenario: Deploy a rogue agent that subscribes to the same channel and attempts to send a crafted “shutdown” command. If the system accepts it, your communication layer is vulnerable. Attack Surface 2: Preventing Data Injection in Agent Workflows Agents often acce

pt inputs from external sources (user queries, API responses, file uploads). In LUMOS, tool invocations can pass data directly to an LLM prompt. Malicious payloads—prompt injections, malicious code blocks—can hijack an agent’s behavior. Assessment actions: - Review all tool input schemas and sanitization logic. - Implement parameterized tool calls: avoid raw string interpolation into prompts. - Use LUMOS’s built-in guardrails (if available) or integrate a neutral content filter. - Test with known injection patterns: e.g., in user input. Risk scoring indicator: If any tool passes unvalidated user input to an LLM, assign high severity. Attack Surface 3: Mitigating Privilege Escalation Risks Multi-agent systems often use role-based access control (RBAC) at the orchestration level. However, one agent may request another agent to perform actions on its behalf. Without strict delegation checks

, an agent with limited permissions could trick a privileged agent into executing restricted operations. Assessment steps: - Audit LUMOS agent role definitions and permission scopes. - Ensure each agent can only invoke tools within its own role—no cross-role escalation. - Enable audit logging for every delegation request. - Simulate a low-privilege agent requesting a high-privilege agent to delete data. Defensive pattern: Use “tokens” or signed capabilities that carry the original requester’s identity into the tool invocation chain. Attack Surface 4: Validating Outputs to Prevent Malicious Content An agent’s output may contain harmful code, phishing links, or disinformation. In multi-agent setups, output from one agent becomes input to another—compounding the risk. Assessment approach: - For each agent, define an output validation policy (e.g., regex allowed patterns, category whitelists

). - Use LUMOS’s post-processing hooks to run validation checks before forwarding. - Test with adversarial outputs: injected into a downstream agent. - Monitor for hallucination-induced dangerous instructions (e.g., SQL injection in generated queries). Risk scoring: Agents that produce executable co