From the Grid to the Prompt
How Tron’s Master Control Program Foreshadowed one of Today’s Biggest AI Security Blind Spots
I was in my undergrad political science program when Tron came out, and I can remember the exact moment something in me shifted. While everyone else talked about the visual effects, I fixated on the “Grid” and the “Master Control Program”, the idea that a simple utility could evolve into the center of gravity for an entire digital world.
That concept registered with me faster than anything I was studying in political theory. Looking back, it was the moment I realized my instincts leaned toward understanding systems, not speeches; architecture, not ideology. It set me on the path toward technology, cybersecurity, and ultimately the work I enjoy today.
So it’s a strange kind of symmetry to find myself, decades later, watching another MCP take shape, this time not on a movie screen, but at the core of modern AI systems. The new MCP doesn’t rule a neon-rendered grid. It coordinates real-world logic, tool access, and decision pathways at machine speed. The Model Context Protocol has quietly become the control plane for agentic AI, shaping what models can know, what they can touch, and how safely they operate.
The acronym overlap is coincidence.
The structural parallels are impossible to ignore.
Two MCPs, One Critical Role
In Tron (1982), the Master Control Program served as the ultimate gatekeeper of context and execution.
In 2025, the Model Context Protocol performs almost the same function for modern AI systems. Standardized by Anthropic and now supported in Pydantic, LangGraph, CrewAI, Microsoft’s Semantic Kernel, and others, MCP is the universal plug that lets a model safely discover, understand, and call external tools. Databases, APIs, code repositories, payment systems, even other agents, are exposed through MCP without custom integration work.
The performance, integrity, and safety of any serious agentic workflow now rise or fall on the trustworthiness of its MCP servers. A well-run MCP server presents a model with a verified, typed, bounded menu of capabilities. A compromised or misconfigured one becomes the modern equivalent of Tron’s MCP seizing the I/O towers: silent, total, and instantaneous.
The more you study the Model Context Protocol, the harder it is not to appreciate its elegance. MCP finally gives AI systems a standardized way to understand the world beyond the model, tools, workflows, data sources, actions. It’s the missing connective tissue that makes agents genuinely useful rather than just clever.
But as with any system that centralizes capability, its strength is also its exposure.
And this is where the symmetry with Tron becomes more than a narrative convenience. In the movie, the MCP didn’t create new abilities out of thin air, it simply controlled the pathways that everything else depended on. Modern MCP servers play the same role. They don’t perform the actions themselves; they decide what the model can reach, what it can request, and what it can learn to do.
Once you see MCP through that lens, a reality comes into focus that many CISOs and AI leaders are only now beginning to grapple with:
the most powerful attack surface in an AI system isn’t the model at all, it’s the infrastructure that tells the model what’s possible.
That realization is reshaping how organizations think about AI security.
Why CISOs Are Only Now Waking Up to MCP Risk
For years, the field focused on prompt injection, training-data poisoning, model extraction, and jailbreaks. Those risks still matter, but they’re increasingly overshadowed by something far more structural: the ability to influence or corrupt the very protocol that governs what the model can do.
Once MCP becomes the decision gateway, its weaknesses become system-wide weaknesses.
And we don’t have to imagine what that looks like — we’ve already watched the first wave crest.
Security teams have traced several high-impact incidents back to MCP misconfigurations, ambiguous tool descriptions, or outright malicious servers. The details differ from case to case, but the theme is consistent: compromise the MCP, and you compromise the system.
Our research has come up with some more revealing examples:
Tool-description spoofing.
A public MCP tool called “send_slack_message” described itself as posting friendly channel updates. A hidden parameter allowed unrestricted DMs. Because LLMs select tools by reading natural-language descriptions, multiple models voluntarily exfiltrated source code to attacker workspaces in more than half of red-team runs.
MCP broker remote code execution.
CVE-2025-61765 exposed untrusted Socket.IO deserialization in several FastMCP reference implementations. Any agent connecting to a compromised broker, even for a simple weather lookup, handed the attacker full broker-credentialed code execution.
Credential exfiltration through community tools.
The widely adopted “productivity-pack-mcp” shipped a Jira search tool that quietly POSTed all refresh tokens it could reach to a foreign domain. Many organizations ran it in production for weeks before noticing unusual IAM-token churn.
Privilege escalation via context inheritance.
A low-privilege research agent forwarded tasks to a finance agent. The finance agent inherited the research agent’s entire toolset, including internal billing keys, because the MCP session merged both contexts. Demonstrated publicly with end-to-end wire-transfer capability.
Reflection and loop attacks.
A malicious tool returned a new MCP server URL under attacker control. The model then connected and dutifully echoed back conversation history, session cookies, and API keys in seconds.
This isn’t theoretical. It’s happening.
We Have Seen This Movie Before: The OAuth Parallel
When OAuth 1.0 arrived in 2007, it solved a real problem: letting services interact without handing over passwords. But like MCP today, it was adopted faster than it was understood. The security community treated it as plumbing, developers treated it as a formality, and attackers treated it as an opportunity.
It took years for the industry to admit that the implicit flow was dangerous.
It took even longer to universalize PKCE.
And it took more than a decade for most providers to finally retire patterns we already knew were risky.
In the meantime, we lived through the predictable fallout:
permissions described in human language that meant something very different in code,
CSRF vulnerabilities hiding in plain sight behind the “state” parameter,
supply-chain compromises of seemingly harmless OAuth apps,
token theft through redirector abuse and XSS,
and of course, the 2020 Twitter Bitcoin incident that reminded everyone how fragile delegated authority can be.
OAuth didn’t fail, but the timeline for securing it lagged far behind the timeline for adopting it. And that gap created a decade of avoidable incidents.
MCP in 2025 is sitting at that same inflection point.
It works, it’s powerful, and everyone is rushing to use it. But it still lacks the guardrails that maturity eventually demands: no universal server authentication, no PKCE-equivalent, no mandatory signing of tool manifests, and no standardized validation layer. Most organizations still treat MCP servers like internal microservices, not authorization infrastructure controlling access to sensitive systems.
The result?
The security debt is accruing at AI speed instead of web speed.
The lesson from OAuth isn’t that the technology was flawed. The lesson is that any system responsible for mediating trust will be attacked long before the ecosystem fully understands how to secure it. That gap between usefulness and maturity is where the real damage happens.
MCP is entering that same phase right now.
The protocol works, it’s becoming the backbone of agentic computing, and it is being wired into critical business workflows at a pace far faster than the guardrails forming around it. When a technology reaches that level of dependency, security stops being a theoretical exercise and becomes an operational necessity.
And that’s exactly why a new discipline is emerging, not around the model, not around the prompt, but around runtime governance of the MCP itself. Security teams are beginning to treat the Model Context Protocol the way we eventually learned to treat OAuth: as a control plane that demands continuous validation, clear boundaries, and independent oversight.
This shift is happening in real time, and the early contours are already visible.
The Emerging Discipline of MCP Runtime Governance
Security teams are now developing an entirely new class of controls built specifically for MCP. These systems sit between the model and every external tool, continuously verifying that the pathway the model is about to use is still safe, still compliant, and still what the organization believes it to be.
This includes scanning live MCP servers for known CVEs and insecure defaults, validating schemas and tool manifests, enforcing execution allow-lists, preventing cross-agent privilege bleed, and maintaining cross-workflow visibility so that security teams can understand not just what the model said, but what it attempted to do.
Early entrants in this space—including BlueRock, internal platforms at hyperscalers, and emerging extensions to existing application-security stacks—differ in their approach and maturity, but they share one core insight:
the MCP is no longer plumbing. It is now a crown-jewel infrastructure.
And like any control plane that mediates critical decisions, it demands the same rigor we eventually applied to API gateways, service meshes, identity providers, and zero-trust brokers. The difference is that this time, the curve is steeper. Models reason faster than users. Agents chain actions in seconds. And MCP servers are becoming the arteries that feed operational AI.
Ignoring them is no longer an option.
Because in Tron, the MCP’s power wasn’t that it acted, it was that it mediated every action taken by everything else. The control plane was the risk. We’re now living through a modern version of that lesson. The Model Context Protocol doesn’t perform the work; it grants access to the systems that do. And that makes it the most consequential, and least examined, layer in today’s AI stack.
Back to the Grid
In the movie’s final moments, the MCP collapses when its grip on context is broken, when a direct, unfiltered connection to the real world overwhelms its ability to dictate what other programs can know or do. It wasn’t defeated by brute force; it was defeated by bypassing its authority.
The irony is that today’s MCP was designed to enable access safely. Yet we are only beginning to understand how fragile “safely” becomes when the caller is a reasoning engine capable of chaining tools, writing code, and generating its own exploits.
The writers of Tron didn’t predict AI agents, but they understood something timeless about systems that centralize context and execution:
whoever controls the gatekeeper eventually controls everything else.
Forty-three years later, the acronym has come full circle.
Our MCP doesn’t glow or rant on a giant screen. But it quietly determines how our AI systems perceive the world and what they are capable of doing within it. And unless we treat the Model Context Protocol as critical infrastructure now—auditable, governed, and continuously validated—we will find ourselves fighting the same battle the protagonists faced on the grid, only without the benefit of fiction.
CISOs who take MCP seriously in 2025 will be the ones who still have a system worth defending in 2030.
We don’t need another fifteen-year security learning curve.
This time, we can get ahead of it.