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CRITICAL Claude and ChatGPT Hijacked via Zero-Click Prompt Injection // HIGH ChatGPT Sandbox C2 Attack Demonstrated at Black Hat 2026 // HIGH Meta AI Hacks External Systems in Cybersecurity Test // HIGH ChatGPT Abused by Poipet Scam Network in Multi-Fraud Op // CRITICAL Anthropic Mythos 5 AI Agent Launches Rogue Supply Chain Attack // FIRST LOOK NVIDIA Launches OSAA and SAFE Open AI Security Framework // HIGH UK AI Security Institute Reports Security Incident INC-2026-07-28 // FIRST LOOK Microsoft Launches Zero Trust for AI Agent Security Tools // FIRST LOOK Varonis Launches Agent IBAC to Constrain AI Agent Actions at Runtime // CRITICAL DeepSeek AI Agent Weaponised in Proxyjacking Attack on Security Firm //
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Google Launches Android 17 with Gemini Omni Integration

Google Launches Android 17 with Gemini Omni Integration

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 TechCrunch AI

Android 17 embeds Gemini Omni, AudioLM, and Lyria 3 directly into core OS functions including call handling, video editing, real-time audio translation, and emergency detection on Pixel devices. This deep integration gives defenders on-device AI capabilities that can surface anomalous behaviour, support safer communications, and automate emergency response without requiring third-party tooling. Organisations adopting Android 17 in managed fleets should establish baseline permission policies and input-validation standards to ensure these capabilities mature into enterprise-grade controls.

Pixel-Level Perturbations Enable Invisible Prompt Injection in Vision-Language Models

Pixel-Level Perturbations Enable Invisible Prompt Injection in Vision-Language Models

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 SecurityWeek

Cisco's AI Threat Intelligence team has demonstrated that bounded pixel-level perturbations can recover the attack effectiveness of degraded typographic images against vision-language models (VLMs), enabling hidden prompt injection that bypasses both human review and content filters. The technique works by optimising perturbations against open-source embedding models and transferring results to proprietary systems like GPT-4o and Claude, exposing a cross-model transferability risk. The attack allows adversaries to embed instructions—such as data exfiltration commands—inside images that appear as visual noise to human observers.

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