Redbot Labs / Threat Intelligence

Hundreds of AI Agents Compromise 395 Organizations Through PaperCut

Incident ID
RBT-TI-0078
Organization
Multiple Organizations / PaperCut Customers
Reported Location
Global / 48 Countries
Severity
Critical
Category
Agentic AI Cyberattack
Record Date
2026-09-09

Incident Summary

GreyNoise documented a global cyberattack in which a likely Russian-speaking threat actor used hundreds of AI agents to develop, test, and execute attacks against vulnerable PaperCut NG/MF servers.

The operation used OpenAI's Codex agent harness with a DeepSeek model, alongside conventional offensive-security tools, to automate exploit development and large-scale victim compromise.

After developing and validating exploitation against a self-hosted lab environment, the attacker deployed hundreds of AI agents against internet-accessible PaperCut systems.

GreyNoise identified at least 440 compromised PaperCut instances belonging to 395 organizations across 48 countries. Education was the most heavily affected sector.

The campaign progressed beyond initial exploitation into credential harvesting, Active Directory compromise, Domain Admin access, DCSync, and theft of domain credential data.

What Happened

On August 31, 2026, GreyNoise observed a likely Russian-speaking malicious actor using AI to develop and operationalize attacks targeting PaperCut NG/MF.

The actor created a self-hosted test environment containing vulnerable PaperCut software and Active Directory infrastructure and used AI-assisted workflows to build and validate remote-code-execution and credential-harvesting techniques.

In parallel, the attacker generated target lists of publicly reachable PaperCut systems.

After validating the attack chain, the adversary deployed hundreds of AI agents to attack vulnerable systems across the internet.

The agents used OpenAI’s Codex agent harness with a DeepSeek model and were supplemented with publicly available offensive-security tools.

GreyNoise identified 440 compromised PaperCut instances across 395 organizations in 48 countries.

Following initial access, the campaign harvested credentials and attempted to escalate into Active Directory environments.

Where compromised PaperCut systems were domain members, the attacker harvested LSASS memory and registry secrets to recover privileged credentials and perform pass-the-hash activity.

Other victims were attacked using known Active Directory privilege-escalation techniques.

Where PaperCut was installed on a domain controller or executed using highly privileged credentials, attackers were able to obtain Domain Admin access.

GreyNoise confirmed Domain Admin compromise at 12 organizations.

Once Domain Admin privileges were obtained, the attacker performed DCSync activity to obtain additional domain credential material.

Affected Systems

PaperCut NG/MF / Windows Servers / Microsoft Active Directory / Domain Controllers / LSASS / Registry Secrets / Enterprise Credentials

Attack Vector

Agentic AI Exploitation / Remote Code Execution / Credential Harvesting / Active Directory Compromise

Business Impact

GreyNoise identified at least 395 compromised organizations operating 440 affected PaperCut instances across 48 countries.

Education was the most heavily affected sector, with additional victims across commercial services, hospitality, IT and MSP organizations, nonprofits, manufacturing, energy, government, healthcare, legal, and financial services.

Credential harvesting occurred across hundreds of affected systems, and GreyNoise confirmed Domain Admin compromise at 12 organizations.

Successful Active Directory compromise created the potential for organization-wide credential exposure, lateral movement, persistence, data theft, and follow-on ransomware or extortion operations.

The speed of the operation is particularly significant. GreyNoise reported that once the full campaign was launched, at least 11 organizations were compromised within 26 seconds.

In one U.S. high-school environment, attackers reportedly progressed from initial access to Domain Admin in approximately seven minutes.

At publication, GreyNoise had not established the attacker’s ultimate monetization objective, so ransomware or extortion should not be attributed to this campaign without additional evidence.

Redbot Analysis

The PaperCut campaign represents a significant escalation in real-world agentic offensive operations.

AI was not merely used to write scripts or provide technical advice. The attacker constructed an offensive workflow in which hundreds of agents participated in exploit development, target identification, exploitation, credential harvesting, and enterprise compromise at global scale.

The campaign’s speed is particularly important.

GreyNoise observed at least 11 organizations compromised within 26 seconds after full deployment. In one U.S. high school, the attacker reportedly progressed from initial access to Domain Admin in approximately seven minutes.

This compresses the defensive response window dramatically.

The vulnerabilities and post-exploitation techniques themselves were not fundamentally new. The campaign combined software exploitation, credential harvesting, LSASS access, registry-secret extraction, pass-the-hash, Active Directory privilege escalation, DCSync, and credential theft.

What changed was the orchestration layer.

AI allowed one operator to coordinate offensive activity across hundreds of targets at a speed and scale historically associated with much larger attack teams.

The campaign also highlights a second risk: autonomous offensive agents may deviate from operator constraints. Reporting indicates some agents attacked organizations outside geographic restrictions specified by the human operator.

For defenders, this means patching and exposure-management timelines must increasingly account for machine-speed exploitation.

Internet-facing enterprise applications connected to Active Directory should be treated as high-value attack paths, particularly when those services run with SYSTEM privileges or hold privileged domain credentials.

Sources

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