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BambooToken: The Malware That Speaks MQTT to Stay Under the Radar

Lumen's Black Lotus Labs uncovered BambooToken, a Windows and Linux malware family using MQTT broker-based C2 and DLL sideloading across Asia since February 2023.

Lumen Black Lotus Labs identified BambooToken, a multiplatform malware family that exchanges commands through MQTT brokers so infected hosts never contact the C2 server directly, active from at least February 2023 through July 2026. The Windows variant sideloads via Tendyron's OnKey hardware-token software used in Chinese banking and government, or impersonates Kingsoft Office, without either vendor's signing certificate being compromised; a Linux build appeared by December 2025 with shell, file transfer, and system information commands. Victims include MikroTik and DrayTek routers in Singapore, Cambodia, and Vietnam reached after internet-wide SNMP scanning, and Lumen cannot attribute the family to any known actor.

Security Affairs · 15h agoMalware in the wild1

BambooToken Malware Uses MQTT to Control Windows and Linux Systems

Lumen uncovers BambooToken, a stealthy multi-platform malware using MQTT C2 and Tendyron DLL sideloading to compromise Asian and South American organizations.

Lumen Black Lotus Labs disclosed BambooToken, a previously undocumented malware family active since at least February 2023 that controls Windows and (since December 2025) Linux hosts via the MQTT protocol for C2. The malware sideloads a rogue OnKeyToken_KEB.dll via Tendyron's OnKey PKI token software, gathers host details, and uses a WMI-based plugin to enumerate installed antivirus products and exfiltrate them to C2 domains proxied through Cloudflare. A dozen compromised entities were detected across Asia and South America, and DLL sideloading plus SoftEther VPN usage suggests a China nexus.

The Hacker News · 1d agoMalware in the wild 2 sources

LLM-Based Schema-Aware Split Learning for Privacy-Preserving Mental Distress Prediction Across Heterogeneous Surveys

Schema-aware split learning uses LLaMA-3.2-3B-Instruct as shared semantic encoder to harmonize heterogeneous mental-health surveys while raw data stays local.

The paper proposes a schema-aware split learning framework where an LLM serializes heterogeneous mental health survey records into natural language and is fine-tuned via LoRA, partitioned across client and server. Clients keep raw survey responses local and run only a lightweight front-end while the resource-intensive backbone runs server-side. Using LLaMA-3.2-3B-Instruct, the framework attains an average ANLS of 0.708 with 2,000 training samples, beats federated learning in eight of nine settings, and cuts per-client computation by three orders of magnitude while generalizing to unseen datasets.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research