NetTraceArcade | Cybersecurity, AI & Cloud

Category: Cybersecurity News | Supply Chain Security | AI Security  |  Published: October 23, 2026  |  Reading time: about 15 minutes On March 24, 2026, a threat actor known as TeamPCP published two malicious versions of LiteLLM to PyPI. LiteLLM is the Python library and proxy that thousands of teams use to route requests to OpenAI, Anthropic, Google, Amazon and dozens of other model providers, and it was reportedly being downloaded about 3.4 million times a day. The attackers never found a bug in LiteLLM. Instead, they compromised Trivy, a popular vulnerability scanner that ran inside LiteLLM’s build pipeline, and used it to steal the credentials needed to publish packages as the real maintainer. A security tool became the way in. The incident was ranked among the biggest security stories of the year because of what it combined: a package that sits next to the most valuable keys in an AI stack, a trusted delivery channel, and an attack that could run wit...

AI agents become far more useful when they can work with real data. In this step-by-step tutorial, we build an AI agent using Google's Agent Development Kit (ADK) and Gemini that can securely access and analyze structured data in BigQuery through the BigQuery MCP Server , and then deploy it to Google Cloud Run . Watch the full walkthrough below, then use this post as a reference while you follow along. 📌 Introduction Large Language Models are powerful, but on their own they can't see your business data. To build agents that answer real questions about sales, customers, or operations, they need a safe way to connect to data sources and tools. This tutorial shows one way to do that: an ADK agent powered by Gemini uses the BigQuery MCP server to query structured data, and the whole application runs as a live service on Cloud Run. It is based on the Google Cloud codelab "Build and Deploy AI Agents with Gemini and BigQuery MCP server in Cloud Run." 🎯 Wha...

Machine Learning for Intrusion Detection: Building Smarter Network Security Systems As organizations become increasingly dependent on digital infrastructure, cyberattacks continue to grow in both volume and sophistication. Traditional security systems often rely on predefined signatures, making them ineffective against previously unseen threats. Machine learning introduces a more intelligent approach by enabling intrusion detection systems to learn patterns from network traffic and identify suspicious behavior automatically. Why Traditional Intrusion Detection Falls Short Signature-based intrusion detection systems work well against known attacks but struggle with zero-day threats and rapidly evolving attack techniques. Security teams require solutions that can adapt to changing traffic patterns without constant manual updates. Machine learning addresses this challenge by analyzing historical network data and recognizing anomalies that may indicate malicious activity. Ma...

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