A new operation embedding StealC V2 inside Blender project files has been observed targeting victims for at least six months.
According to a new advisory by Morphisec, the attackers placed manipulated .blend files on platforms such as CGTrader, where users downloaded them as routine 3D assets.
When opened with Blender’s Auto Run feature enabled, the files executed concealed Python scripts that launched a multistage infection.
StealC V2 Expands Reach Through Weaponized Blender Assets
The research, published today, connects this activity to Russian-speaking threat actors previously associated with StealC distribution.
The campaign mirrors an earlier effort that impersonated the Electronic Frontier Foundation (EFF) to target Albion Online players, sharing elements such as decoy content, background execution and Pyramid C2 infrastructure.
The infection chain began with a tampered Rig_Ui.py script embedded inside the .blend file. This script fetched a loader from a remote workers.dev domain, which then downloaded a PowerShell stage and two ZIP archives containing Python-based stealers.
Once extracted into the Windows temp directory, the malware created LNK files to secure persistence, then used Pyramid C2 channels to retrieve encrypted payloads.
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StealC V2, promoted on underground forums since April 2025, has rapidly expanded its feature set. It now targets more than 23 browsers, over 100 plugins, more than 15 desktop wallets, and a range of messaging, VPN and mail clients. Its pricing, from $200 per month to $800 for 6 months, has made it accessible to low-tier cybercriminals seeking ready-to-use tools.
Attribution and Indicators of Compromise
Several indicators of compromise (IoCs) surfaced during the investigation, including:
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Malicious .blend files hosted on CGTrader
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Payload retrieval through multiple workers.dev domains
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ZIP archives containing Python stealers and persistence components
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Command-and-control (C2) communication across several Pyramid-linked IPs
Morphisec attributes its early blocking of this campaign to its deception-based protection platform. By injecting decoy credentials into memory and browser storage, the system triggers prevention when StealC attempts to access them. Processes are terminated before exfiltration or persistence can occur.
The researchers say this approach transforms credential theft attempts into failures, stopping StealC V2 long before it can gain a foothold on an endpoint.
