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Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference
Hands-on tutorial implements NVIDIA cuML and RAPIDS to GPU-accelerate scikit-learn-style ML workflows with benchmarking, clustering, and inference.
The tutorial demonstrates NVIDIA cuML as a GPU-accelerated machine learning framework, using cuml.accel to speed up unmodified scikit-learn scripts with zero code changes and the native cuML API for CuPy/cuDF interoperability. It benchmarks CPU versus GPU implementations of PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN on datasets up to 200,000 samples with 64 features. It also builds GPU pipelines with UMAP, t-SNE, and HDBSCAN, validates GPU-generated SHAP explanations, uses the FIL library for forest inference, and covers model serialization and GPU/CPU portability.
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Google Research Releases ToolGrad: Answer-First Framework Hits 99.8% Pass Rate for Tool-Use Data Generation
Google Research and partners introduce ToolGrad, a verified tool-chain-first data generation framework reaching 99.8% pass rate and boosting Gemma-3-12B to 83.1 on BFCL.
Researchers from Google, the University of Tokyo, RIKEN AIP, and Tohoku University released ToolGrad, which inverts query-first tool-use data generation by executing and verifying API chains before annotating them with user queries. On the ToolBench database of 16,000+ APIs, ToolGrad raised generation pass rate from 63.8% to 99.8% while increasing tool uses per sample from 2.1 to 3.4 and cutting tool-use steps from 34.3 to 20.0. Fine-tuning Gemma-3 at 1B, 4B, and 12B parameters on the 500-sample ToolGrad-500 dataset lifted ToolGrad-12B to 83.1 on the Berkeley Function Calling Leaderboard, near Gemini 2.5 Pro at 83.2 and ahead of GPT-5 at 74.4. Code is Apache-2.0, with the dataset, PyPI package, and models available on Hugging Face.
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ICE Collecting DNA Samples
ICE's DNA collection program projects detainee samples to reach 33% of CODIS offender index by 2030, raising privacy concerns.
A Schneier on Security blog post discusses ICE collecting DNA samples from detainees, citing Georgetown Law research. DHS detainee samples are projected to constitute 33% of the FBI's CODIS offender index in 2030, up from 0.2% in 2019. The post raises concerns that samples collected under civil authority are being searched against crime scenes indefinitely, potentially without legal cause under Fourth Amendment standards.
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TrickBot Campaign Uses Fake Payroll Emails to Conduct Phishing Attacks
TrickBot phishing campaign used SendGrid, fake Google Docs, and Drive-hosted downloaders disguised as Word files to deliver a credential-stealing payload.
Unit 42 identified a TrickBot distribution campaign on November 7-8, 2019, using payroll and annual bonus-themed emails sent from likely compromised .edu addresses through the legitimate SendGrid email delivery service. Emails contained links to Google Docs documents linking to downloader executables hosted on Google Drive, further masked behind SendGrid click-tracking URLs. The downloaders, signed by PERISMOUNT LIMITED and displayed with Microsoft Word icons, show a decoy pop-up and retrieve TrickBot payloads from compromised legitimate domains such as savute[.]in and lindaspryinteriordesign[.]com. The newer TrickBot variant stores its files and configuration under %APPDATA%\cashcore.
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NOKKI Almost Ties the Knot with DOGCALL: Reaper Group Uses New Malware to Deploy RAT
Unit 42 links NOKKI malware to North Korea's Reaper group, uncovering the Final1stspy dropper that deploys the DOGCALL RAT in politically motivated attacks.
Unit 42 analyzed the NOKKI malware family used in politically themed attacks against Russian and Cambodian speakers since July 2018. The researchers linked NOKKI to the Reaper group, publicly attributed to North Korea, whose custom DOGCALL RAT uses third-party hosting services to upload data and receive commands. A previously unreported family, Final1stspy, was found deploying DOGCALL, sharing a unique base64-to-hex deobfuscation routine with NOKKI droppers. Attacks used malicious Microsoft Word macros that download and execute payloads while opening decoy documents.
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