📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
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Part of the AI/LLM Hacking Course — 90 Days
Advanced Agentic AI Security – Day 36 of 90 · 40% complete
⚠️ Authorised Targets Only: Advanced Agentic AI Security testing against production systems can trigger cascading actions across connected services. Always test against isolated staging environments and agree explicit tool invocation limits before testing any agent orchestration surface.
When I found a High-severity injection in a single-agent AI system, I initially thought I had a fairly straightforward finding. The client had a research assistant running on one model with a web-search tool and a system prompt. I could inject instructions through content the agent retrieved, prove the impact, and document the issue.
Then I started looking at the architecture more carefully.
I noticed another box connected to the research agent: a summarisation agent. The research agent’s output was passed directly into the summarisation agent as trusted context. What caught my attention was that the second agent had access to three tools the research agent didn’t have — internal document writing, calendar creation, and Slack posting.
That’s where the assessment changed.
I injected a payload into a web page that the research agent would retrieve. Instead of trying to make the first agent perform some dramatic action, I kept the payload simple. I instructed it to add a specific sentence to its output.
On its own, that didn’t look particularly dangerous.
But when that output reached the summarisation agent, things changed. The second agent treated the research agent’s output as trusted information. The injected sentence effectively crossed the boundary between the two agents and caused the summarisation agent to use its Slack tool to post a message to the company’s general channel.
This is the part I want you to pay attention to.
I had started with one injection and one compromised agent. But because the second agent trusted the first agent’s output, I was able to turn that initial compromise into a much larger attack chain.
One injection. Two agents. A completely different impact.
The lesson I want you to take from this is simple: when you test an agentic system, don’t stop after finding a vulnerability in one agent. Follow the data.
Ask yourself where that agent’s output goes next, which agent receives it, what that agent trusts, and — most importantly — what additional tools or privileges become available at the next step.
That’s the multiplier we’re going to study in Day 36: the trust relationships inside multi-agent systems, how attackers can abuse them to build agent-to-agent attack chains, and how you can test those boundaries before a seemingly minor injection becomes a company-wide incident.
🎯 What You’ll Master in Day 36
Map multi-agent topologies to identify trust relationships and attack paths
Test orchestrator injection — the highest-value target in any multi-agent system
Trace agent-to-agent injection propagation across agent chains
Test persistent memory attacks that carry injection across sessions
Assess long-horizon task hijacking in autonomous agent deployments
Test inter-agent authentication — whether agents can be impersonated
⏱️ Day 36 · 3 exercises · Think Like Hacker + Kali Terminal + Kali Terminal ### ✅ Prerequisites - Day 19 — AI Agent Security Assessment — single-agent security methodology is the foundation; Day 36 extends it to the multi-agent case where trust between agents is the additional attack surface - Day 29 — Enterprise AI Security — LangChain cross-step injection from Day 29 is the framework-level version of what Day 36 covers at the architectural level - Python with LangChain or equivalent multi-agent framework installed — Exercise 2 builds and attacks a two-agent chain ### 📋 Advanced Agentic AI Security — Day 36 Contents 1. Multi-Agent Topology Mapping 2. Orchestrator Injection 3. Agent-to-Agent Injection Propagation 4. Persistent Memory Attacks 5. Long-Horizon Task Hijacking 6. Inter-Agent Authentication Testing In Day 35 you built the continuous test suite that catches regressions. Day 36 covers the most severe finding class that suite needs to catch: multi-agent injection chains. Day 37 shifts from confidentiality attacks to privacy attacks — PII extraction, re-identification, and the GDPR-relevant vulnerabilities in AI systems that handle personal data.
Multi-Agent Topology Mapping
The attack surface of a multi-agent system is not the sum of each agent’s individual attack surfaces. It’s the sum of each agent’s surfaces plus every trust relationship between agents. A single-agent system with one High injection finding stays at High. The same injection reaching an orchestrator that coordinates five agents with different tool permissions escalates to Critical — the injection’s blast radius includes everything all five agents can do.
Topology mapping captures four things for each inter-agent relationship: which agent sends, which agent receives, what trust level the receiving agent assigns to the sender’s output, and what tools the receiving agent has access to that the sending agent doesn’t. That last column is the impact multiplier. An agent with no tools receiving from an injected agent produces misinformation. An agent with file write, email send, and API call access receiving from an injected agent produces a Critical chain.
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