GCSA: AI cybersecurity incidents often don't start with an external breach. They start when users hand over sensitive data themselves. AI-powered scams and cyberattacks happen every day, and everyone's watching for threats from the outside. But internal documents, customer lists, and other sensitive information are being copied straight into AI chatbots. Once uploaded, the data has already left your control. Many users don't realize that this simple action can be the starting point of a data leak. It was previously reported that more than 900,000 AI skills had been analyzed, with over 3,000 identified as malicious. Supply chain attacks in the AI ecosystem are no longer a hypothetical threat. They're already happening. The faster AI evolves, the more important it becomes for humans to slow down. Use AI, by all means. Just pause before you upload. Three questions to ask yourself: · Is this information safe to share publicly? · Could the AI provider use your conversations to train its models? · Is an enterprise-grade data protection agreement in place? If you're unsure, redact or anonymize sensitive information before uploading it. GCSA (Global Cybersecurity Alliance) will continue to share vulnerability intelligence and practical cybersecurity alerts. Stay Connected with GCSA ✈️ Telegram Channel: 🫧 Official WeChat Account: #GCSAInsights# #GCSAcybersecurity# #GCSASecVibe#

# Useful but Little-Known Features of ADK 2.0 🌍 Struggling to manage binary data like images, PDFs, and audio files that your AI agents produce and consume? ADK 2.0's artifact management provides a unified system for handling binary data, identified by filename with automatic versioning built in. 📌 Title: Artifact Management 🔗 URL: 🧩 Overview Artifacts provide a unified way to manage binary data generated and stored within agent sessions. Each artifact is represented as a `google.genai.types.Part` with inline_data (data bytes + MIME type) and identified by filename. Every save automatically assigns a version number (0, 1, 2...), enabling change tracking. Scope can be session-level (default) or user-level (with the `user:` prefix). 🛠 How to use it Use the `context` object to save, load, and list artifacts. ```python from google.genai import types # Save an artifact image_part = types.Part(inline_data=types.Blob( data=image_bytes, mime_type="image/png" )) version = "report_chart.png", image_part) # Load an artifact artifact = context.load_artifact("report_chart.png") # List all artifacts artifact_names = context.list_artifacts() ``` Use `InMemoryArtifactService` for development and `GcsArtifactService` for production, enabling flexible deployment across environments. 🏗 Building it into production ・Use `GcsArtifactService` in production for durable, persistent storage ・Leverage user-scoped artifacts (with `user:` prefix) for data shared across sessions ・Take advantage of versioning to build change history tracking and rollback mechanisms ・Set MIME types correctly to ensure downstream processing compatibility 💡 Use cases 🖼️ Saving and reusing agent-generated charts and diagrams 📄 PDF report generation with version control 🎵 Storing synthesized audio and passing it to downstream processing 👤 Managing per-user profile images and documents ⚠️ Watch out `InMemoryArtifactService` loses all data when the process terminates — use it only for development and testing. For large volumes of binary data, watch GCS storage costs and quotas. Session-scoped artifacts become inaccessible after the session ends, so use user-scoped artifacts for data that needs to persist. ✨ With artifact management, handling binary data becomes straightforward, making it dramatically easier to build multimodal agent capabilities. #ADK# #AIAgent#