Google DeepMind and Singapore Government collaborate to build fine-tuned AI model for scam detection

We’re expanding our presence in Singapore to advance AI in the Asia-Pacific region

Google DeepMind opens a new Singapore research lab, accelerating AI progress in the Asia-Pacific region.

Strengthening Singapore’s AI Future: A New National Partnership

Google DeepMind and Singapore partner to apply frontier AI to address complex challenges across health, education, and sustainability and more.

Partnering with industry leaders to accelerate AI transformation

Google DeepMind partners with global consultancies to bring the power of frontier AI to organizations around the world.

Announcing our partnership with the Republic of Korea

Google DeepMind and Korea partner to accelerate scientific breakthroughs using frontier AI models

Deepening our partnership with the UK AI Security Institute

Google DeepMind and UK AI Security Institute (AISI) strengthen collaboration on critical AI safety and security research

We’re launching the Google DeepMind Accelerator program in Asia Pacific to tackle environmental risks

Operation “Wrong Number”: AI-assisted task scam

OpenAI banned accounts that likely originated in Cambodia and used AI to support scam workflows targeting people in the UK.

Protecting people from harmful manipulation

Google DeepMind researches AI's harmful manipulation risks across areas like finance and health, leading to new safety measures.

An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory

arXiv:2607. 11707v1 Announce Type: cross Abstract: Following the rapid progress of generative Artificial Intelligence, there is a growing threat posed by conversational scams.

Task scam: AI-assisted fake review jobs

OpenAI banned accounts that appeared to originate in Cambodia and used AI to translate messages for fake-review job scams that asked victims to pay fees.

Speeding up planning decisions with AI

Google DeepMind and UK government work together to reduce planning assessment times

Black-Box Forensics for Conversational LLM Agents

The paper presents methods for black-box forensic analysis of conversational large language model (LLM) agents, focusing on two tasks: attribution—identifying the base model powering a chatbot—and fingerprinting—detecting identical system prompts across different endpoints. Attribution classifiers achieve 98% accuracy using only a few non‑adversarial conversation turns, while a cross‑encoder fingerprinting approach attains an AUC of 0.768 and an F1 of 0.703 on unseen prompts, improving to 0.943 AUC when aggregating 50 interactions. These techniques enable investigators to trace AI‑enabled scams back to their underlying models and link related scams through shared system prompts.