AI Agents Don’t Hesitate And That’s a Security Problem

AI Agents don’t hesitate.
They don’t second-guess.
They dont pause.

We didn’t just add AI to our stack. We gave it access, autonomy, speed. Which means mistakes are no longer small or slow.

Earlier this year, an internal AI coding agent at AWS ended up deleting and recreating parts of a production environment, causing a 13-hour outage!

An agent with too much access and zero hesitation.
This wasn’t an edge case, it’s a pattern.

Now compare that to something less accidental.

A red-team exercise showed how an autonomous agent could break into McKinsey’s internal AI platform, Lilli.

No credentials. No insider access.

Within hours, it was able to:

1. map internal APIs
2. identify a classic SQL injection and
3. escalate access across the system

AWS was accidental. But this is proof of risk.

In the AI era, the threat landscape is changing and rapdily; AI agents autonomously selecting and attacking targets will become the new normal.

So the question is “how fast?”

Vulnerabilities like SQL injection aren’t new. What’s changed is how easily they can be exploited now.

An agent can:

smartly identify

test

refine payloads and

chain access, all without stopping.

That is the real shift.

We’re not just writing vulnerable code anymore. We’re deploying systems that can:

find vulnerabilities, exploit them and amplify the impact automatically!

End-to-end. Without human friction.

When a developer makes a mistake, it’s usually contained and it takes time to escalate and someone eventually notices.

But when an AI agent makes a mistake, it executes end-to-end across systems at machine speed before you know it.


Most teams are focused on how to integrate agents and which model to use.

Are we thinking “What can this agent actually do if it goes wrong?”.

Almost all agents are over-permissioned.

In practice, agents are rarely given isolated, scoped identities.

Instead, they run on shared API keys or service accounts, like a CI/CD token with access to your repo, pipelines, and cloud which gives them far more access than they actually need.


Modern AI systems operate across multiple systems in a single flow, increase coupling between data, prompts, and execution paths.

So a “simple” bug can give access to the application and influence how AI behaves, making it much easier to exploit at scale.


Over the past few weeks, multiple incidents have shown the same pattern:

agents executing actions they shouldn’t

systems trusting inputs they shouldn’t

access levels that were never properly scoped

Do a Dev Sanity Check (Bookmark This)

If you’re working with agents (LangChain, AutoGPT, internal tools), run through this:

1. Identity Scope
Does each agent have its own identity? Or are you using shared service/admin tokens?
If one agent is compromised, what else breaks?

2. Destructive Actions
Can an agent execute DELETE / DROP / RECREATE?
If yes, is there a human approval gate?

3. Shadow Agents
Are devs running local AI tools with access to:
your repo, internal APIs and tokens. You probably have more agents than you think.

4. Blast Radius Mapping
Ask one question:
“If this agent goes rogue, what’s the worst it can touch?”

If the answer is one of “everything”, “production”, “our data layer”, you don’t have an AI problem. You have an access design problem.


You found a vulnerability. Cool.
But, can an agent exploit and chain this automatically?

Because that’s the real bar now.

This is the shift we’re seeing across the industry:
moving from finding vulnerabilities to proving real-world exploitability.

Gartner notes that offensive security trends are moving toward Preemptive security. Preemptive cybersecurity solutions (example Siemba) defend more effectively against such sophisticated AI-enabled exploits.

This proactive approach represents a shift from traditional reactive methods to preemptive measures aimed at mitigating risks before they escalate.