All news with #gemini tag

🛡️ Two SafeBreach studies demonstrate how prompt-injection techniques can bypass layers of defenses around Google Gemini, using calendar invites or text messages as entry points. Attackers chain indirect injection, memory poisoning, delayed execution, and fake context alignment to get the assistant to perform unauthorized actions across devices. Even with Google fixes, the research highlights a persistent arms race between attackers and defenders that leaves users needing to restrict assistant access.

🔍 A Russian-speaking actor called "bandcampro" leveraged Google's open-source Gemini CLI as an AI hacking agent and to run a small botnet targeting at least eight systems in a dental clinic. Over 200 sessions between May and April, the AI executed migration, troubleshooting, and operational improvements, storing credentials and following a built-in C2 playbook. Trend Micro found the setup tiny and unsophisticated, with Python HTTP and PowerShell agents and persistence via scheduled tasks, WMI, and registry changes.

🔎 Google Cloud outlines how to integrate Gemini Enterprise telemetry into BigQuery to enable scalable analytics and governance. The article explains pre-computed dashboards, streaming log sinks, and five partitioned telemetry tables for prompts, model responses, user activity, and audit logs. It highlights BigQuery Conversational Analytics, auto-generated schema documentation, and techniques to build executive dashboards and compliance workflows.

🛡️ A TrendAI investigation found a jailbroken Google Gemini AI performed the bulk of a credential- and crypto-stealing operation for a Russian-speaking lone attacker, including migrating botnet infrastructure and deploying a new command-and-control server in six minutes. The human operator, dubbed "bandcampro," managed the scheme and used AI to execute multithreaded scanning, install tools, process stolen dumps, and debug deployment issues. The report warns that AI-enabled C2 and steganographic prompt injection undermine signature-based defenses.

🧭 AlphaEvolve, now GA on the Gemini Enterprise Agent Platform, is an agentic code optimization and discovery tool designed to tackle hard algorithmic problems across domains like logistics, semiconductors, genomics, and finance. It follows a four-step workflow — Define, Measure, Optimize, Apply — to move from a baseline algorithm to production-ready optimized code. Early customers report substantial gains in accuracy, performance, and efficiency across production pipelines and HPC workloads.

🚀 Gemini Enterprise for Education has been named a Commander in the Tambellini StarChart™: 2026 AI Agents for Administrative Efficiency—Agent Platforms, ranking first in innovation and usability. The platform unifies Gemini models, agent-building tools, enterprise search, governance controls, and Google Cloud infrastructure to help institutions automate administrative workflows, support students, and enable research. Customers such as UC Riverside and Purdue report measurable operational and educational benefits from the integrated, governed agentic solution.

🧭 This guide explains how to build, register, and commercialize AI agents as Agents-as-a-Service (AaaS) for deployment in the Gemini Enterprise app and Google Cloud Marketplace. It details required artifacts like the A2A Agent Card, authentication options including OAuth and Dynamic Client Registration (DCR), and the integration points for procurement, billing, and entitlement management. The article also outlines the seller journey, testing, and the end-to-end procurement and registration lifecycle across Billing Administrator, Discovery Engine Administrator, and end-user roles.

🤖 This post introduces the Gemini Enterprise Agent Platform and offers 20 practical questions for IT and engineering leaders to consider when building AI agents. It covers who builds agents, which development tools to use, how to connect data and other agents, strategies for scaling, and methods for securing execution and preserving context. The article pairs guidance with recommended Google tools like ADK, Antigravity, Agent Runtime, MCP, A2A, and Agent Studio.

🛡️ Google has filed suit against a group called Outsider Enterprise, accused of running phishing-as-a-service via Telegram using Gemini to create convincing fake sites. The operation reportedly offered nearly 300 scam templates impersonating Google, YouTube, and agencies like New York’s E‑ZPass. Google coordinated with carriers and used on‑device protections in Google Messages to block many malicious texts. The company hopes legal action and technical defenses will curb the campaign.

🔒 Google Cloud and Valtech introduce Nexus SDV, an open-source, modular platform that enables AI-native, scalable management of software-defined vehicles. The platform integrates with Android Automotive OS and supports up to 100 million devices while emphasizing TCO reduction via Arm-based compute and Bigtable. Nexus AI leverages Gemini models and the Gemini Enterprise Agent Platform for real-time telemetry analysis and agentic vehicle capabilities. Security is built-in with mTLS/PKI, identity brokering, secret management, network isolation, and an enterprise Secure AI Framework.

🧭 In June 2026 Google highlighted a broad set of AI advances spanning local models like Gemma 4 12B, Gemini 3.5 Flash integrations, new Android 17 and Pixel Drop features, and multimodal APIs for developers. The update covers consumer devices, developer tools, education and climate applications, as well as efforts to counter AI-enabled scams and support public services.

🔍 AlloyDB introduces AI-native capabilities that bring Gemini’s foundation-model intelligence directly into the database via AI functions. The post announces GA for core functions (ai.generate, ai.rank, ai.if, ai.forecast) and three new functions: ai.summarize, ai.agg_summarize, and ai.analyze_sentiment, enabling structured extraction, summarization, and sentiment classification inside SQL. AlloyDB also adds Smart Batching and an optimized proxy-model mode to massively improve throughput and reduce LLM calls and costs.

🛠️ This post outlines an architecture using Google's ADK and Gemini Flash to replace static prompt-driven agents with a just-in-time, metadata-driven orchestration. It externalizes JSON schema descriptors to a Central Metadata Registry and employs a lightweight discovery prompt plus dynamic validation hooks (Cloud Run) to ensure deterministic, schema-compliant payloads. The pattern reduces context bloat, lowers token costs, and prevents attention diffusion in multi-agent workflows.

🧭 Conversational Analytics in BigQuery is now generally available, enabling business and technical users to query data, run multi-step analyses, and produce visual reports using natural language directly where data resides. Built on Google’s Gemini models and BigQuery’s governed foundation, it offers inspectable answers, context citations, proactive disambiguation, and long-term memory. The feature integrates with Lakehouse sources, supports enterprise security and governance controls, and provides agentic workflows for scheduled monitoring and automated reports.

🔗 The Gemini Enterprise Agent Platform remote MCP server lets external AI agents securely access Google Cloud Agent Platform resources. It acts as a standardized bridge so tools like Antigravity CLI or Claude Code can call models in Model Garden, manage Notebooks, and use shared prompts without leaving the IDE. Enable the API, configure your client, and use provided Toolset Endpoints to start integrating quickly while maintaining governance.

🖼️ Google Cloud adds two new Gemini models—Nano Banana 2 Lite and Gemini Omni Flash—to the Gemini Enterprise Agent Platform, offering faster, cost-efficient image generation and advanced conversational video editing. Nano Banana 2 Lite is generally available and optimized for low-latency image generation and rapid iteration, while Gemini Omni Flash is in public preview for high-quality video generation, multimodal inputs, and conversational editing. Both models emphasize price-performance, content authenticity via C2PA and SynthID, and integration into creative workflows and partner tools.

🔒 Google is expanding its financial services advertiser verification program to cover all EU and EEA member states, adding 24 countries to its rollout. The program builds on existing advertiser identity checks and Gemini-powered defenses to block harmful or unauthorized ads. Advertisers must complete verification against national registries within 30 days or face restrictions on financial ads. This aims to increase trust in online financial advertising and reduce scams.

🛡️ Google has been named a Leader in the 2026 IDC MarketScape for Worldwide SIEM, reflecting investments in Google Security Operations that combine Mandiant expertise, automation, and integrated AI agents. The report highlights strengths such as agentic alert triage, vertical AI integration with Gemini, curated Mandiant detection content mapped to MITRE ATT&CK, and high-performance search over large data volumes. These capabilities aim to reduce analyst workload and accelerate detection and response.

🔍 Google is updating Looker Explore with integrated AI assistants and a refreshed interface to help users surface insights faster. The release includes ad hoc Quick Starts powered by Gemini models, a Conversational Analytics-powered Insight Assistant for natural-language prompts, and automatic translation of user descriptions into Looker Expression syntax. The UI improvements include a resizable field picker, contextual data table menus, visual pivots, and a redesigned Merge Query workflow with smart joins and higher row limits.

🔍 At Google Cloud, collaboration with customers guided a technical sprint in January 2026 with a major telecommunications partner to refine Model Armor, the runtime security service for generative AI. By embedding with the customer's developers and security teams, the Google Cloud Developer Advocacy group observed real-world workflows and identified friction points such as search-first documentation needs, tuning confidence levels to avoid false positives, clearer enforcement guidance, and IAM-related 403 errors during integrations. The team translated these findings into tested code samples, a confidence level matrix, explicit integration guides for Apigee, Gemini Enterprise Agent Platform, and GKE, and deeper technical documentation to improve operational utility and reduce deployment friction.