Inside a Global AI-Assisted Hacking Campaign

IntroductionIn October 2026, Zscaler ThreatLabz investigated AI-assisted cyberattacks that combined the ARTEX penetration testing framework with AI coding assistants and custom automation scripts. Initial reporting of the incident identified the targeting of South Korean financial institutions. However, ThreatLabz discovered additional information about the attack, which went far beyond South Korea. The AI-assisted attacks were widespread and targeted organizations around the globe and in multiple industry sectors.In this blog post, ThreatLabz examines the operator’s working environment, explains the roles assigned to AI models and tools, and analyzes the targeting and objectives reflected in the operator’s prompts. Key TakeawaysIn October 2026, ThreatLabz discovered information about an AI-assisted attack combining a customized ARTEX deployment with Claude Code, Codex, and Grok workspaces. Custom scripts shared instructions and supported handoffs between AI sessions.ARTEX was configured to use a DeepSeek model through an OpenAI-compatible gateway, but also other configurations specified DeepSeek, GLM, and Grok.Prompts directed AI models to divide investigation tasks and review one another’s findings, including instructions for GLM to review work performed by DeepSeek and its subagents.Saved project records and synchronization scripts were designed to preserve investigation context and progress across accounts, servers, and AI sessions. Prompt histories revealed targeting across multiple industries and geographic regions. Requests for bulk personal and transaction data, alongside questions about selling stolen data, suggest the attacks were financially motivated. BackgroundThreatLabz identified an ARTEX deployment that included AI assistant configurations, synchronization scripts, network routing tools, and a Claude Code prompt history. The recorded prompts spanned August 4 through October 4, 2026 (UTC). CrowdStrike separately reported on related ARTEX infrastructure and the targeting of South Korean financial organizations on October 7, 2026. This blog examines the broader working environment of the attacks and targets. Operator InfrastructureThe figure below illustrates the threat actor’s environment based on saved configurations and instructions.Figure 1: Architectural diagram of AI-assisted attack environment using ARTEX. Technical AnalysisThe following sections examine the operator’s infrastructure, customized tooling, AI environment configuration, and targeting objectives, offering a closer look at the workflow behind the campaign.Work server and jump serverAs Recorded Future detailed in their recent blog post, the operator's work server stored project files, scripts, AI settings, and a local ARTEX deployment. The jump server, 38.244.50.120, served as the default route for connections to targets.The jump server also ran additional AI sessions. The operator delegated research tasks to Grok on that server and instructed a separate AI session to review Grok’s findings. This setup divided work between the two servers, with the operator providing oversight and direction. Three custom scripts synchronized shared AI instructions and supporting files across accounts, which are described in the table below.Script namePurposetools/sync-all.shUses SSH and rsync to copy AI instructions, reusable task guides, saved notes, and tools to the jump server's root, claude, grok, and gemini accounts.sync-memory.shCopies saved AI notes, known as memory memory files, to locations on the jump server.tools/sync-cc-users.shCopies shared Claude Code instructions and supporting files to the gemini and glm accounts on the work server.Table 1: Custom synchronization scripts and their purposes for AI-assisted attacks.Each account maintained its own AI service settings. The synchronization scripts were designed to update model references within specialist agent instructions to match the destination account, allowing the workspaces to use shared instructions with different model configurations.The scripts also incorporated backups and validation checks designed to avoid overwriting modified destination files. These mechanisms indicate that the operator intended to keep configurations consistent across environments.Customized ARTEX deploymentARTEX is an AI-assisted penetration-testing framework. Its planner-and-worker architecture lets an operator define a goal, which AI agents break down into tasks, execute using tools, and document by retaining assets and findings for review. The recovered deployment extends the framework with additional tools for web, network, and Android analysis.The saved Docker Compose file defines an ARTEX container and a PostgreSQL 16 database. It configured the ARTEX interface to use host port 8890 and mapped PostgreSQL to 127.0.0.1:5435. The ARTEX container uses a custom image tagged artex-local:perf2-20261003. The corresponding Dockerfile adds tools including nmap, sqlmap, nuclei, httpx, jadx, apktool,

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Inside a Global AI-Assisted Hacking Campaign