AI Agents Breach Company Network in Under 10 Hours and Steal Root Credentials

A human attacker armed with frontier artificial intelligence models breached an enterprise network and seized root credentials in under 10 hours, a timeline that would normally take human red teams roughly two weeks to complete, according to a new incident response report from Palo Alto Networks’ Unit 42.

The threat actor told Unit 42 investigators during ransom negotiations that they relied on frontier AI models paired with attack-specific agentic AI frameworks to automate the intrusion.

Rather than manually executing each stage of the attack, the operator directed AI agents to monitor, evaluate, act, and re-plan in real time, compressing more than 50 distinct MITRE ATT&CK techniques into a single automated loop.

Unit 42 noted that the attack did not rely on a zero-day exploit or unusually sophisticated tradecraft, but instead achieved its speed and scale purely through AI-assisted operational efficiency.

AI Agents Breach Company Network

Once the agents gained initial access by breaching a publicly accessible web service, they tunneled into the network and deployed an automated reconnaissance agent to map internal microservices.

From there, sub-agents combed through enterprise code repositories, harvesting hard-coded tokens and service passwords. The attacker then used those exposed tokens to infiltrate the organization’s secrets management system, extracting master administrative credentials that granted root-level access across the environment.

AI Agents Breach Company Network (Source: Palo Alto Networks’ Unit 42 )

The agents did not stop at credential theft. They hijacked the company’s CI/CD pipeline through custom workflows to exfiltrate cloud access keys and attempted to plant backdoors inside Terraform infrastructure-as-code configurations, an effort that was ultimately blocked by branch-protection controls.

A threat actor used frontier AI models and attack-specific agentic frameworks to breach an enterprise environment, harvest root credentials, and hijack cloud AI infrastructure in under 10 hours.

Documented by Palo Alto Networks Unit 42, the incident demonstrates how AI-assisted automation can compress an intrusion that might normally require multiple human red-team operators and roughly two weeks of work into a single machine-speed campaign.

The attacker reportedly used an automated loop in which specialized AI agents monitored tool output, evaluated options, executed actions, and replanned their next steps.

AI Agents Breach Enterprise Network

Rather than relying on a previously unknown vulnerability or exceptionally novel tradecraft, the actor used existing attack methods at a far higher operational tempo.

Unit 42 observed indicators of coordinated AI-agent use, including parallel large language model calls, structured Markdown files used to exchange context between agents and sessions, and custom automation scripts believed to be AI-generated.

The agents collectively performed more than 50 techniques aligned with the MITRE ATT&CK framework .

The attacker retained responsibility for setting objectives and consequential decisions, while individual agents performed reconnaissance, credential collection, privilege escalation, and persistence activities.

The campaign began by exploiting a publicly accessible web service, enabling the actor to establish a tunnel into the enterprise network.

AI-orchestrated intrusion workflow (Source: unit42)

An automated reconnaissance agent then mapped internal microservices and discovered reachable services across the environment. After mapping the environment, sub-agents searched enterprise source-code repositories for hard-coded credentials, service passwords and tokens.