At ZioSec, we’ve always believed that penetration testing is the gold standard of security. But the landscape is shifting. AI agents are no longer operating in isolation— they’re collaborating, competing, and forming intricate webs of communication. Welcome to the world of Multi-Agent Systems (MAS). And with that complexity comes an entirely new attack surface.
When we first encountered these MAS environments, one thing became clear: traditional testing wasn’t enough. Enter OWASP MAESTRO—a framework designed specifically for this brave new world of agentic AI. Let’s explore why this matters and how we at ZioSec are leaning in.
What is MAESTRO?
MAESTRO stands for Multi-Agent Environment, Security, Threat, Risk, and Outcome. It’s more than just another acronym—it’s a layered methodology developed by OWASP’s Agentic Security Initiative to systematically model threats in multi-agent architectures.
Imagine the STRIDE model, but tailored for the messy realities of AI agents that aren’t just static code—they learn, adapt, and interact. MAESTRO provides the scaffolding for testing these systems at every layer: from foundational models to the agents themselves, to their tools and infrastructure, and even their communication pathways.
Why Offensive Security Needs MAESTRO
For security teams, AI deployments often feel like black boxes. Governance and compliance bodies are unsure what’s under the hood, and security teams don’t always have the tools to probe these environments deeply. That’s where we come in.
MAESTRO helps us break down these AI systems layer by layer. We uncover threats that live at the edges—between agents, within their toolsets, and across communication channels. These aren’t just theoretical risks. They’re real, exploitable vectors that demand attention.
Expanding the Threat Landscape
Traditional pentesting focuses on inputs and outputs, maybe some privilege escalation in between. But with AI agents? The game changes. MAESTRO helps us identify:
- Tool misuse: hijacking the agent’s APIs or financial systems via subtle prompt manipulations.
- Intent manipulation: steering agents off-mission without triggering standard alerts.
- Cross-agent interference: using one compromised agent to influence or disable others.
Cross-Layer Attack Modeling
In MAS environments, threats don’t stay confined. A rogue agent might exploit a model instability that triggers tool misuse, leading to infrastructure-level failures. MAESTRO helps us trace these multi-layer attack chains, identifying blast radius scenarios that typical pentests miss.
Aligning with MITRE ATLAS & ATT&CK
We map our offensive testing to established frameworks like MITRE ATT&CK and ATLAS. MAESTRO adds the missing granularity for AI agents, letting us simulate MAS-specific threats while aligning our findings with industry-recognized tactics like data poisoning or model evasion.
Use Cases: How We Apply MAESTRO
Consider an internal AI agent behind your firewall—a customer service bot linked to sensitive data. Your security stack is strong, but how do you know the agent itself can’t be tricked into leaking that data? With MAESTRO, we probe across layers, testing memory poisoning and tool misuse while ensuring your observability layers catch it all.
Or what about third-party AI agents? Vendors promise security, but MAESTRO lets us test that claim. We examine agent-to-agent communications, rogue agent introduction risks, and logging adequacy to ensure nothing slips through the cracks.
The Future of AI Pentesting is Layered
As AI ecosystems evolve, so must our approach to security. OWASP MAESTRO is at the forefront of this evolution. And at ZioSec, it’s become central to our mission: bringing pentesting rigor to the complex world of multi-agent AI systems.
Whether you’re deploying in-house models or adopting third-party agents, MAESTRO helps us map out your agent ecosystems, stress-test them, and deliver the clarity you need to move forward securely.
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