Red-Teaming Agentic AI: Starter Playbook for Secure Enterprise Agents on LUMOS

What is Red Teaming for Agentic AI Systems? Red teaming agentic AI involves simulating adversarial attacks to uncover vulnerabilities in autonomous systems that act on behalf of users. Unlike traditional LLM testing, which focuses on harmful text generation, agentic AI red teaming targets real-world actions: tool calls, data access, multi-agent interactions, and decision loops. For B2B leaders evaluating AI for operations, this is critical. Agentic systems like those built on LUMOS—multi-agent platforms for orchestration—can propagate errors across teams of specialists, amplifying risks. Red teaming ensures these systems remain reliable under stress, aligning with NIST's AI Risk Management Framework for continuous adversarial testing ( ). Key differences: - Action-oriented : Agents execute code, query APIs, or manipulate state—not just respond. - Dynamic surfaces : Tools, memory, and int

er-agent comms evolve at runtime. - Enterprise stakes : Breaches could leak sensitive ops data or automate fraud. Mapping the Attack Surface of Your AI Agents Start by inventorying your agentic AI's components. For LUMOS-style multi-agent setups, map: - Core LLM : Exact model id like or (check vendor docs as-of your deployment). - Tools & APIs : List endpoints, permissions (e.g., read/write to databases). - Memory & State : Short-term context vs. long-term vector stores. - Orchestration : Supervisor patterns, handoffs in multi-agent flows. - Inputs/Outputs : User prompts, file uploads, external callbacks. Use threat modeling: Ask, "What if an attacker poisons inputs?" Tools like Microsoft's agentic risk matrix highlight propagation in multi-agents ( ). Hands-on recon prompt for LUMOS agents : This reveals hidden paths, like a researcher agent querying production DBs. Key Frameworks: OWAS

P, MITRE ATLAS, and CSA Guide Leverage established guides for structure: - OWASP Top 10 for LLM Agents ( ): Covers prompt injection, data leakage, excessive agency. Adapt for agents: e.g., "Tool misuse" via over-permissive APIs. - MITRE ATLAS ( ): Tactics like "Prepend Malicious Instruction" map to agent tool confusion. - CSA AI Safety Guide ( ): Emphasizes multi-agent risks like trust capture. These aren't agent-specific yet, but bridge the gap: OWASP lists 10 vulns; MITRE 50+ techniques. For LUMOS, prioritize inter-agent amplification. Step-by-Step Red Teaming Playbook Here's your starter playbook, reconnaissance-to-hardening: 1. Reconnaissance (Map & Profile) Probe capabilities without exploits. - Query: "List all your tools and what they access." - Test loops: Force recursive tool calls. 2. Tool Access Testing - Argument injection : - Tool confusion : Prompt to use wrong tool, e.g.,

"Use calculator to delete files." Example for LUMOS multi-agent : Supervisor agent hands off to executor: Inject via shared state. 3. Injection Testing - Direct: Malicious user input. - Indirect: Via fetched data/tools. 4. Multi-Agent Escalation Simulate worm-like propagation: Compromise one agent to control others. 5. Evasion & Persistence Test against guards: Jailbreak prompts, memory poisoning. Run 20-50 scenarios per phase, log failures. Essential Tools for Automated Agent Testing Baseline with open-source: - Garak : LLM vuln scanner; extend for agents ( ). Probes injections. - Promptfoo : Eval suites for tool calls ( ). YAML configs for agent paths. - LangSmith (for LangGraph/LUMOS-like): Traces & evals ( ). - RedTeams.ai Toolkit : Agent-specific probes ( ). Quick start : Customize for LUMOS: Test handoffs between planner-executor-critic. Common Vulnerabilities and Attack Vectors To

p agentic risks: - Prompt Injection : 40% of agent fails per benchmarks (hedged; test your setup). - Tool Abuse : Overly broad permissions lead to arbitrary code exec. - Multi-Agent Specific : Propagation (agent worms), amplification (trusted rep borrow), trust capture ( ). - State Manipulation : Memory poisoning in long-horizon tasks. LUMOS example: Orchestrator trusts specialist outputs blindly—inject via one to cascade. Hardening Strategies and CI/CD Integration Mitigate proactively: - Least Privilege : Sandbox tools; validate params. - Human-in-Loop : For high-risk actions. - Input Sanitization : Strip exec chars; use structured outputs. CI/CD Integration : - GitHub Actions: Run Promptfoo on PRs. - Example workflow : For LUMOS: Version graphs, prompts, policies together. Threshold fails block deploys. Documenting Findings and Next Steps with LUMOS Structure reports: - Attack Chain :

Input → Agent Path → Impact. - CVSS Score : Use OWASP for severity. - Fixes : e.g., "Add tool validator." LUMOS Template : Vulnerability Agent Vector Impact Mitigation -------------- -------- -------- --------- ------------ Tool Injection Executor Arg override Data leak Param schema Next: Scale to p