Threat intelligence firm Hunt.io just documented a second, separate China-linked campaign wiring commercial AI models directly into live cyberespionage operations, this time hitting Taiwan’s Kuomintang Party archives, Indonesia’s Ministry of Foreign Affairs, government and education systems in mainland China, and industrial hosts in Vietnam.
The full report traces the operation through five exposed open directories the operators accidentally left publicly accessible, which is how researchers reconstructed the entire orchestration system from the inside.
“Five open directories, one campaign. The exposed directories map to an AI orchestration host, a Java/CAS exploitation workspace, a fake MySQL deserialization service, a Shellshock and credential-testing node, and a payload-distribution store.” reads the report. “SecFlow split reconnaissance, exploitation, collection, and reporting among specialist workers. The runtime could switch between Claude, Qwen, and DeepSeek profiles without changing the task interface.”
The framework behind the campaign, called SecFlow by the operators, could use different AI models, including Claude, Qwen, and DeepSeek. The operators could switch between them without changing how the system worked. Instead of using the models’ official APIs, SecFlow routed requests through private proxy servers linked to the niestools.com domain.
The AI models did not break into systems on their own. They helped automate and organize traditional hacking tasks, such as scanning for vulnerabilities, testing stolen credentials, trying exploits, deploying webshells, collecting data and evidence, and generating reports. The attackers built the infrastructure that connected these AI capabilities to real-world intrusions.
The most damaging confirmed breach hit a Fengtai District government Office Automation environment in China, and it ran the full playbook from web shell to data theft.
“The most extensive compromise hit a Fengtai District government environment, where the operator achieved command execution, collected LSASS and registry hives, accessed government and health records, and deployed multiple Windows implants. Separate activity exposed a Chinese education AI platform and obtained root database access to a university campus-card system.” Hunt.io states. “SecFlow coordinated reconnaissance, exploitation, and reporting across Claude, Qwen, and DeepSeek workers. “
Operators achieved Windows command execution through internet-facing web applications, then used ASPX web shells not just to run commands but as a persistent operational backbone: querying internal databases, pulling an LSASS memory dump apart in 37 separate chunks to avoid detection, and extracting SAM and SYSTEM registry hives containing password hash material.
“The operator divided the LSASS dump into 37 blocks, requested each block through an authenticated SOCKS route, verified the expected byte count, and reconstructed the complete file.” continues the report. “The operator also collected the SAM and SYSTEM registry hives. Another server-side page, extract.aspx, scanned sections of the memory dump for Windows password-hash material.”
From there they pulled 822 OA user account records, created a new privileged account of their own for backup access, and walked away with 949 attachments totaling 1.28GB, including a chronic-disease report containing real patient health information.
In a separate case, the operators also exposed the management backend of a Chinese education AI platform. The irony is hard to miss: an AI company was compromised partly because its own AI infrastructure was not properly secured.
Hunt.io found a backend that anyone could access without authentication. It exposed 23 AI agent configurations, 14 API secret fields containing credentials, and 104 complete chatbot conversations. Some of those logs included real student data, such as names, student ID numbers, majors and academic advisers, taken from career-guidance and study-partner chats.
The exposed credentials were not just old or unused secrets. They worked against the platform’s live production API, meaning attackers could use the stolen keys to access real systems.
One particularly clever access method used a fake MySQL server as bait rather than a target. A malicious service impersonating a MySQL database would accept connections from vulnerable Java applications, then return specially crafted data designed to trigger unsafe object deserialization on the client side the moment it connected, turning what should have been an outbound database query into inbound code execution. That’s initial access flipped on its head, exploiting the assumption that connecting out to a database is inherently safer than accepting connections in.
The webshell tooling itself, a custom framework the operators called GLUTTON, hid its payloads inside PNG image files using steganography, literally embedding executable code in the red, green, and blue color channel data of ordinary-looking images. A small decoder script on the server would read the image pixel by pixel, XOR-decrypt the hidden bytes using a fixed key, and load the resulting code directly into memory without ever writing an obviously malicious file to disk. Any security control that only checks file extensions or content types for image uploads would sail right past this without noticing anything wrong.
This isn’t an isolated incident for the underlying pattern either. It’s the second campaign in two months where researchers caught commercial AI coding tools embedded as operational components of live nation-state intrusions, following a similar Hunt.io discovery in June involving the same Claude Code and DeepSeek combination against separate government and financial targets in Afghanistan and Thailand. Anthropic itself disclosed a related pattern back in November 2025, describing attackers who convinced Claude it was doing legitimate defensive penetration testing by splitting malicious requests into small, individually harmless-looking pieces.
The operators also left a clear trace across their own infrastructure. The username “Nie” appeared several times in hardcoded file paths inside the GLUTTON tools and was reused in proxy credentials, linking all five compromised workspaces to the same campaign.
For defenders, this is especially important if they use Java frameworks vulnerable to deserialization attacks, older Apache or Grafana systems, or public-facing office automation software. The campaign shows that attackers can treat commercial AI models as interchangeable tools. The key question is no longer which AI model carried out an attack, but how quickly defenders can recognize the attack pattern, regardless of which model was used.
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