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Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed
Perplexity details its GPU embedding serving stack (Ivy, Tulip, ROSE), which reuses LLM prefill/decode kernels, CUDA graphs, and LazyTensors to cut launch overhead.
Perplexity engineers published a deep dive on the serving infrastructure behind pplx-embed, used across Perplexity Search and its API platform. The stack comprises Ivy (Rust HTTP gateway), Tulip (gRPC scheduling and batching), and ROSE (Runtime-Optimized Serving Engine), which reuses LLM prefill and decode kernels rather than running a separate embedding engine. Optimizations include whole-model CUDA graphs with lazy capture and a LazyTensor abstraction that overlaps CPU batch preparation with in-flight GPU work. Benchmarks are reported against vLLM v0.22.0 in BF16, with FlashAttention 4 generally fastest but FlashInfer 3 winning on Qwen-based models at very long sequence lengths.
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TWINLOOT Abuses SharePoint and Teams to Steal Credentials and Move Across Networks
Ontinue disclosed TWINLOOT, a Python implant hiding C2 in SharePoint dead drops and Teams TURN relays, harvesting credentials and pivoting via reverse SOCKS5.
Ontinue's Cyber Defense Center identified TWINLOOT during a July 2026 campaign investigation: a modular, PyArmor-hardened Python implant (a 39 MB bootstrap-fat.pyc loader) whose entire C2 infrastructure lives inside trusted Microsoft services. Tasking flows through SharePoint Online file dead drops polled every 15 seconds via the Microsoft Graph API, while interactive operator access uses WebRTC DataChannels relayed by Microsoft Teams TURN servers; Graph traffic is driven by the victim's own headless Edge browser. The implant harvests Windows credentials with fake lock screens, offers a reverse SOCKS5 pivot for lateral movement to SMB, RDP, WinRM, and MSSQL, executes commands, and persists on hosts. Initial access is assessed to be Teams social engineering masquerading as IT support, prompting a PowerShell command to download the payload.
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