AI Agents Are Leaking Credentials and Nobody Knows

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The Problem Nobody Talks About

Everyone's deploying AI agents now. Customer support bots, data processing agents, automation workflows, code generation tools. They're supposed to make things faster and cheaper. And they do, until they don't.

Last month, three different companies reached out to us with variations of the same problem. Their AI agents were failing in production. Not because the AI was bad. Not because the code was broken. But because somewhere along the way, credentials leaked, access got revoked, or an agent did something it shouldn't have been able to do in the first place.

One company had a customer service agent that could read from their entire database. Not just customer tickets. Everything. Including internal financial data. They found out when an agent hallucinated and started quoting revenue numbers to a customer.

Another had API keys hardcoded in agent prompts. When they shared logs for debugging, those keys went straight to their vendor's support team. Oops.

The third one? Their agents were using a shared service account with admin privileges. When one agent got compromised through prompt injection, the attacker had keys to the entire kingdom.

These aren't hypothetical scenarios. This is happening right now.

What Are Non-Human Identities and Why Should You Care

Non-Human Identity, or NHI, is just a fancy term for credentials that belong to systems instead of people. API keys, service account tokens, OAuth credentials, database passwords. The stuff that lets your applications and agents talk to each other and to external services.

Humans have usernames and passwords. They use multi factor authentication. They go through HR processes when they leave. There are systems in place.

But agents? They just need credentials to work. And those credentials usually live in:

• Environment variables
• Configuration files
• Code repositories
• Prompt templates
• Log files
• Documentation

Nobody tracks them properly. Nobody rotates them regularly. Nobody has a good answer to "which agent has access to what" until something breaks.

The problem gets worse with AI agents because they're autonomous. A human with stolen credentials is one thing. You can usually detect weird behavior. But an AI agent with leaked credentials? It looks like normal agent activity. It's making API calls, accessing databases, sending requests. That's what it's supposed to do.

Until it's not your agent doing it. It's someone else's.

The Governance Problem

Most companies treating AI agents like they treat regular software. They focus on prompt engineering, model selection, response quality. All important stuff.

But almost nobody is asking the basic questions:

• What systems can this agent access?
• What's the minimum access it actually needs?
• How do we track what it's doing?
• How do we revoke access if something goes wrong?
• Where are all the credentials stored?
• Who's responsible when an agent messes up?

There's no governance framework. No clear ownership. No access reviews. Just agents with way too many permissions doing their thing until something breaks.

We saw one setup where customer facing agents had write access to production databases. Why? Because it was easier to set up that way during development. Nobody went back and restricted it before launch.

Another company couldn't tell us which agents were running in production versus development. They had agents they'd forgotten about still running, still consuming API credits, still having access to systems.

This isn't a technical problem. It's a management problem. The technology works fine. But nobody's managing it properly.

Real Scenarios We've Seen

The Prompt Injection That Worked Too Well

A support agent was supposed to help customers with account questions. Someone figured out they could manipulate the agent through carefully crafted prompts to reveal internal system information. Not because the AI was vulnerable, but because the agent had access to internal wikis and documentation it shouldn't have needed.

The agent was just doing what it was designed to do. Retrieve information and answer questions. The access controls were the problem.

The Shared Credential Disaster

Multiple agents using the same API key to access a third party service. When one agent started behaving weirdly (turns out the training data had issues), the vendor rate limited the API key. All agents went down at once. Production outage. Nobody could figure out which agent caused it because they all looked the same to the external service.

The Log File Leak

Debugging a failing agent. Team enabled verbose logging. Logs captured everything, including the agent's authentication flow with full credentials. Those logs went into their logging platform. That platform was accessible to way more people than production credentials should be. Someone extracted the credentials from logs and they showed up on a credential scanning service three days later.

The Zombie Agents

Company built several experimental agents, gave them all access to various internal APIs, then moved on to different approaches. Forgot to shut down the old agents. Found out six months later during a security audit that they had active credentials sitting in old repositories, still valid, still able to access systems, just not being used. Until they were.

Why Traditional Security Doesn't Catch This

Your standard security tools aren't built for this. They're looking for:

• Humans logging in from weird locations
• Unusual access patterns for user accounts
• Password sharing between people

But agents don't have locations. They run in data centers. Their access patterns are machine patterns, which look different from human patterns. And they're supposed to have shared access to services.

Secret scanning tools might catch hardcoded credentials in code. But what about:

• Credentials in vector databases used for retrieval augmented generation?
• API keys passed through agent orchestration platforms?
• Tokens stored in agent configuration that lives outside your main repo?
• Credentials embedded in example prompts and templates?

Most companies don't even know where all their agent credentials are, let alone how to monitor them.

What Governance Actually Means for AI Agents

Real governance isn't about having a policy document nobody reads. It's about having answers to basic questions and systems to enforce them.

For Every Agent You Should Know:

• What exactly it has access to (list of services, APIs, databases)
• Why it needs that access (business justification)
• How it authenticates (what credentials it uses)
• Who owns it (which team is responsible)
• How to shut it down quickly if needed

For Every Credential:

• Which agents use it
• When it was created
• When it was last rotated
• When it expires (if ever)
• How to revoke it

For Operations:

• How do you detect when an agent is doing something unusual?
• How do you audit agent activity?
• How do you test agents in isolated environments before production?
• How do you promote agents to production safely?
• What happens when an agent fails? Who gets alerted?

Most companies can't answer half of these questions. And that's the actual problem.

The Credential Lifecycle Nobody Manages

Credentials have a lifecycle. They get created, used, rotated, and eventually revoked. For human accounts, this is somewhat managed through HR processes and identity management systems.

For agents? Usually it's:

1. Developer needs an API key for an agent
2. Developer generates key through some portal
3. Developer puts key somewhere the agent can access it
4. Agent uses key forever
5. Developer leaves company or moves to different project
6. Key keeps working
7. Nobody remembers it exists
8. Security audit finds it two years later

There's no rotation schedule. No expiration. No review process. No tracking of what key goes with what agent.

We've seen production agents still using credentials created by developers who left the company years ago. The credentials work, so nobody touches them. Until they become a security incident.

How Bithost Actually Helps With This

We've dealt with this problem enough times that we have a process now. It's not magic. It's just systematic work that most teams don't have time for.

Discovery and Inventory

First, we figure out what you actually have. All the agents, all the credentials, all the access points. This takes time because it's usually spread across multiple teams, repositories, and systems. But you can't secure what you don't know about.

We map out which agent has access to what, using what credentials. Often this is the first time a company has seen their full agent ecosystem in one place.

Risk Assessment

Not all access is equally risky. An agent that reads public documentation is different from one that writes to production databases. We help you understand where the actual risks are so you can prioritize.

We've seen companies freak out about low risk scenarios while ignoring high risk ones, just because they didn't understand their own setup.

Credential Management Overhaul

This is the messy part. Taking all those scattered credentials and putting them into proper secret management. Rotating old keys. Setting expiration policies. Implementing least privilege access.

The goal is that credentials live in one place, have clear ownership, get rotated automatically, and can be revoked quickly if needed.

Access Control Implementation

Most agents have way more access than they need. We work with your team to figure out minimum required permissions and actually implement them. This means:

• Service accounts with limited scope instead of admin accounts
• API keys with specific permissions instead of full access keys
• Database users with read only access when write isn't needed
• Network segmentation so agents can only reach what they need

Monitoring and Alerting

You need to know when agents are doing unexpected things. We set up monitoring that actually makes sense for agent behavior. Not just "did the agent call an API" but "did the agent access something it normally doesn't" or "did this agent start behaving like a different agent."

Governance Framework

The boring but necessary part. Documentation, policies, processes. How do you add a new agent? How do you review existing agents? Who approves what access? How often do you audit?

This doesn't need to be heavy. But it needs to exist and people need to follow it.

Team Training

Your developers need to understand why this matters. Not just security theory, but practical "here's how to do this correctly" guidance. We work with teams to build these practices into their workflow so it becomes normal, not an extra burden.

What This Actually Looks Like

We worked with a startup that had deployed 12 different AI agents across customer support, fraud detection, and back office automation. They were growing fast and security was becoming a concern.

Initial audit found 47 different credentials being used by these agents. Some agents had credentials to systems they didn't even use anymore. Three agents were sharing the same database password. Two agents had admin access to their cloud infrastructure.

Over two months we:

• Consolidated credentials down to 23, each with clear purpose
• Implemented secret management with automatic rotation
• Reduced agent permissions to minimum required access
• Set up proper monitoring for unusual agent behavior
• Created an approval process for new agents
• Documented everything so the team could maintain it

The results? They had a security incident where someone tried to manipulate an agent through prompt injection. The monitoring caught it immediately. The agent's limited permissions meant the attacker couldn't access anything sensitive. The team shut down the compromised agent and rotated its credentials in under 10 minutes.

That's what good governance looks like. Not preventing every attack, but limiting the damage and responding quickly when something happens.

This Is Only Going to Get Worse

More companies are deploying more agents. The agents are getting more autonomous. They're getting access to more systems. The attack surface is expanding.

And most companies are not ready for it.

The ones that figure out agent governance now will have a competitive advantage. They'll be able to deploy agents faster and more confidently because they have the controls in place. They'll avoid the security incidents that are going to hit everyone else.

The ones that don't figure it out will keep having production outages, credential leaks, and security incidents until they're forced to deal with it. Usually after something bad happens.

Start Here If You're Not Sure What to Do

You don't need to fix everything at once. Start with visibility.

Make a list of every AI agent you have running. Not just the official ones. Everything that's accessing APIs, databases, or external services with automated credentials.

For each one, write down:

• What it does
• What it has access to
• What credentials it uses
• Who owns it

If you can't complete that list, you have a governance problem.

If you can complete it and you're horrified by what you see, you have a governance problem.

Either way, you need to deal with it before it becomes a security incident.

We Can Help

At Bithost, we've helped companies clean up their agent security and build proper governance frameworks. We understand both the technical side (credential management, access controls, monitoring) and the organizational side (policies, processes, training).

If you're deploying AI agents and you're not confident about their security, we should talk. If you've already had an incident, we can help you fix it properly.

We do security audits specifically focused on AI agents and non human identities. We'll tell you what's broken and help you fix it.

Get in touch: Visit bithost.in/cybersecurity-service or bithost.in/llm-security-services

Don't wait until credentials leak and production breaks. By then, the damage is done and you're just cleaning up. Fix it now while it's still a manageable problem.

Your agents are only as secure as their weakest credential. Make sure you know where those credentials are.