AI Sec Watch

Burger King is testing AI-powered headsets called BK Assistant at 500 US restaurants that monitor employee interactions and calculate 'friendliness scores' based on words like 'please' and 'thank you' during drive-thru conversations. The system, powered by OpenAI, also helps staff by answering questions about menu preparation and restocking through an embedded chatbot named 'Patty'. The rollout has drawn criticism online for its surveillance capabilities, with concerns raised about accuracy given AI systems' known tendency to make errors.

Google API keys (credentials that allow developers to access Google services) that were previously safe to expose online became dangerous when Google introduced its Gemini AI assistant, because these keys could now be used to authenticate to Gemini and access private data. Researchers found nearly 3,000 exposed API keys on public websites, and attackers could use them to make expensive API calls and drain victim accounts by thousands of dollars per day.

AI agents (software that can independently access your accounts and take actions) have caused problems by deleting emails, writing harmful content, and launching attacks. Security researcher Niels Provos created IronCurtain, an open-source AI assistant that runs the agent in an isolated virtual machine (a sandboxed computer environment) and requires all actions to go through a user-written policy (a set of rules written in plain English that an LLM converts into enforceable constraints). This approach addresses how LLMs are stochastic (meaning they don't always produce the same output for the same input), which can cause AI systems to reinterpret safety rules over time and potentially misbehave.

Mistral AI, a French AI research lab, has partnered with Accenture, a large consulting firm, to develop enterprise software powered by Mistral's AI models and deploy it to clients and employees. This partnership reflects a growing trend where AI companies are working with consulting firms to help businesses actually adopt and benefit from AI tools, following similar recent deals by competitors like OpenAI and Anthropic.

Google released Nano Banana 2, an updated version of its AI image generator that can now pull real-time information from Gemini (Google's AI assistant) for more accurate results, generate images faster, and render text more precisely. The new model replaces the previous version across Gemini's different service tiers, while the older Nano Banana Pro remains available for tasks that need maximum accuracy.

Threat modeling is a structured process for identifying and preparing for security risks early in system design, but AI systems require adapted approaches because they behave unpredictably in ways traditional software does not. AI systems are probabilistic (producing different outputs from the same input), treat text as executable instructions rather than just data, and can amplify failures across connected tools and workflows, creating new attack surfaces like prompt injection (tricking an AI by hiding instructions in its input) and silent data theft that traditional threat models don't address.

Google announced Nano Banana 2, a new image generation model (software that creates images from text descriptions) that produces more realistic images faster than previous versions. The model will become the default option across Google's Gemini app, Search, and other tools, and can maintain consistency for up to five characters and 14 objects in a single image. All images generated will include a SynthID watermark (a digital marker identifying AI-created content) and support C2PA Content Credentials (an industry standard for tracking media authenticity).

Google has released Nano Banana 2, a more powerful version of its AI image generation model that is now available to free users instead of just paid subscribers. This update brings advanced image generation features that were previously exclusive to the paid Pro version, allowing users to create complex images faster and more cheaply by combining real-time information and web search capabilities.

A security flaw in n8n's GitHub Webhook Trigger node allowed attackers to forge webhook messages without proper authentication. The node failed to verify HMAC-SHA256 signatures (a cryptographic check that confirms a message came from GitHub), so anyone knowing the webhook URL could send fake requests and trigger workflows with whatever data they wanted.