3 Ways Threat Actors Are Exploiting AI Infrastructure

Organizations are rapidly adopting AI across every function, often adding AI tools, browser extensions, and open-source dependencies faster than security teams can evaluate and approve them, opening doors into the network that attackers are already maneuvering through.

Because unsanctioned AI activity happens beyond network perimeters, unmonitored data exposure can stealthily expand into organizational risk. Yet most organizations remain unaware of the technical pathways enabling the exposure. 

3 Ways Threat Actors Exploit AI Infrastructure

The primary vectors driving AI infrastructure risk mark a shift in cyber warfare: target acquisition is moving from traditional network perimeters to the unmonitored edges of enterprise AI workflows.

1. Unsanctioned AI Tools Turn Convenience Into Data Exposure

Employees are quick to paste source code, credentials, and customer records into public generative AI tools to move faster. Everyone wants efficiency. Yet, because such inputs bypass standard ingestion protocols, that data suddenly lives outside enterprise retention and security controls. It’s invisible to any policy built to protect it.

As a result, attackers who breach generative AI tools can weaponize whatever they find for credential stuffing, targeted phishing, and infrastructure mapping, all without touching the corporate network.

2. Unmanaged AI Extensions and API Tokens Convert Workflows Into Backdoors

As organizations adopt more AI tools, the volume of non-human machine identities and API connections scales exponentially, creating massive security blind spots.

An unmanaged browser extension or orphaned API token is a live credential connected to company systems that no one is actively tracking, approving or reviewing — often set up by an employee to automate AI workflows, then forgotten once the workflow it served is completed.

Because credentials lack expiration controls, attackers who find an exposed token use it to log in as if they were the trusted integration. No password prompt appears. No alert fires. Security never knows.

Thus, the attacker's authenticated traffic evades standard monitoring because it looks identical to legitimate automated activity — the same API calls the business relies on everyday.

How the attack typically unfolds:

• An employee installs an AI browser extension or generates an API token for an AI workflow tool, then forgets about it.
• The token or extension is exposed — through a public repo, a misconfigured server, or a breached third party.
• Attackers use the exposed credential to authenticate as a trusted AI party.
• Lacking identity governance, that access persists indefinitely —disguised as routine automated traffic — until someone finds it, if anyone does.

3. Poisoned AI Repositories Transform Trusted Code Into Perimeter Threats

When attackers cannot exploit stolen credentials or unmanaged API tokens, they move upstream to target the underlying code that developers use to build enterprise AI applications.

As teams build proprietary AI models and internal tools, developers increasingly rely on open-source machine learning frameworks and AI code libraries, heightening the risk of supply chain contamination.

A poisoned package is malicious code hidden inside what appears to be a legitimate, widely-used software component sitting on a public repository, like npm or PyPi. When a developer downloads that unvetted package, the malware rides in with it, and a backdoor is established inside the perimeter. This gives threat actors immediate internal access. 

How to Detect and Close AI Visibility Gaps

Neutralizing AI exploit vectors requires targeted visibility and control mechanisms deployed directly at the network level.

By monitoring activity across the wire, security teams can execute the targeted domain actions and achieve the security outcomes outlined below:

Closing the Window of Opportunity for Attackers 

Threat actors will keep finding new ways into AI infrastructure. What organizations can control is how fast they notice threats.

Since rapid detection mitigates breach severity, success comes down to one thing — full visibility into what's happening across the network, so unusual activity gets caught before it turns into a real incident.

Unsanctioned AI tools, unmanaged extensions, and poisoned packages share one critical blind spot: nothing surfaces until it hits the network.

Ultimately, organizations stay safe not because attackers stop targeting them, but because security teams catch suspicious behavior on the network before it turns into a breach.

Read our guide on mastering enterprise AI security to identify your visibility gaps before attackers find them.