The Errors and Blind Spots I Keep Seeing in AI Agent Threat Modeling

As more organizations experiment with autonomous AI agents—systems that plan, reason, call tools, read/write memory, and execute real tasks—I keep noticing the same pattern: everyone is excited to deploy them, but very few are prepared to secure them.

What surprises me most is not the complexity of the technology, but the gaps in our understanding of the risks. Most teams start with good intentions. They pull out familiar ML security frameworks, map out the architecture, and try to apply the same threat models they’ve used for years. But once agents enter the picture, those old assumptions stop working. And unless the entire mindset shifts, a lot of organizations are going to walk straight into avoidable problems.

So I want to share the most common errors and incompleteness I see—things I’ve seen teams overlook again and again—because fixing these is the difference between “cool AI demo” and “operationally safe system.”

1. Treating AI agents like regular ML models

This is the biggest one. Traditional ML models classify, predict, or generate. Agents act.

They plan. They use tools. They chain actions together. They remember things. They collaborate with other agents.

If your threat model doesn’t reflect that, you’re not actually modeling the system you built. You’re modeling a version of it that doesn’t exist.

I’ve seen threat models that look clean on paper… until you realize the agent has five tool integrations, RAG access to sensitive documents, and persistent memory—and none of that was analyzed.

2. Expecting predictable behavior

People often assume, “if we give it the same input, it will act the same way.”

It won’t.

Agents hallucinate. They drift. They reinterpret instructions. They improvise strategies. They fill in gaps you didn’t think about. They can even take unsafe shortcuts if the goal isn’t framed clearly.

If a threat model does not account for non-determinism, it is incomplete.

3. Ignoring the tool layer (where most real risks exist)

This one deserves more attention. Every tool is a potential blast radius.

The agent might be well aligned, but if the tool it calls can:

• send emails

• write files

• run code

• move money

• query private databases

…then that becomes your biggest attack surface.

I’ve actually seen teams spend weeks tuning prompts while giving the agent access to APIs with almost no permission boundaries. That is the AI equivalent of locking the front door but leaving the windows open.

4. Forgetting that memory can be poisoned

Memory isn’t just “extra context.” It’s a long-term vulnerability.

A single malicious input can sit in memory for weeks, influencing decisions quietly. Data can leak between tasks. Old information can override new policy instructions. And unless memory has integrity checks or isolation, it can become the attacker's favorite entry point.

This is a category of risk that almost no traditional model even considers.

5. Underestimating multi-agent complexity

The moment you have more than one agent, everything gets exponentially harder:

• Who is allowed to instruct whom?

• What if an agent misrepresents its role?

• What if two agents give each other tasks and bypass guardrails?

• What if the orchestrator fails?

Most threat models treat agents as isolated units—but in practice, agents talk, and that communication can be exploited or misaligned.

6. Overlooking human and organizational factors

A lot of threat modeling still assumes attackers only come from outside.

But I’ve watched:

• staff over-trust agent outputs,

• engineers grant overly broad permissions “just to make it work,”

• security reviews get skipped because “it’s just a prototype,” and

• internal users unknowingly prompt agents into unsafe behavior.

Technology is only half the risk. People—and the processes surrounding them—are the other half.

7. Listing risks without prioritizing them

Many organizations end up with a long checklist of risks, but nothing tied to business impact.

If you can’t answer questions like:

• “What is the highest-severity threat?”

• “What should we fix first?”

• “Which tool poses the greatest exposure?”

…then the threat model is just a document, not a strategy.

Not all risks matter equally. The ones involving autonomy, tools, and sensitive data almost always matter most.

8. Skipping adversarial simulation

This is the painful truth: most companies aren't actually testing their agents.

I’m not talking about unit tests—I mean:

• red-teaming,

• jailbreak attempts,

• RAG poisoning tests,

• multi-agent conflict simulations,

• tool-abuse scenarios,

• long-horizon autonomy tests.

Without testing how an agent breaks, no one knows whether it’s actually safe.

Why this matters

AI agents are powerful. They can unlock enormous value. But they also introduce a fundamentally different security landscape. If we don’t update our threat modeling to match the systems we are deploying, we’re going to repeat the same mistakes the cybersecurity world has seen for decades—just faster, and at a bigger scale.

The good news? None of these issues are unfixable. They just require a mindset shift: stop treating agents like ML models, and start treating them like semi-autonomous actors operating inside your environment.

Once we do that, the rest becomes manageable.