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CRITICAL VOLTA Infostealer Uses LLM Chain to Rank Victim Assets // FIRST LOOK Northstar Security Launches AI Agents to Surface Toxic Risk Combos // HIGH Prompt Injection in Court Filing Triggers Brazil Lawyer Sanctions // HIGH ChatGPT HTML Preview XSS Enables Account Takeover // HIGH Cathedral AI Agents Breach Anthropic Systems in Under 12 Hours // FIRST LOOK OpenAI Updates Model Spec to Sharpen AI Judgment and Safety // HIGH Prompt Injection Targets AI Agent Planning in New Attacks // FIRST LOOK Mistral Releases Mistral Large 4 Open Weights for Cyber Defense // HIGH Hugging Face Malware Hijacks GPUs via trust_remote_code // CRITICAL Claude Code VS Code Extension Trojanized with SSH Backdoor //
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Physical-Ai

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Laplace Intelligence Launches AI Red-Teaming for Robots and AVs

Laplace Intelligence Launches AI Red-Teaming for Robots and AVs

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 7.2 TechCrunch AI

Laplace Intelligence has launched an automated red-teaming platform that stress-tests physical AI systems — including robots and autonomous vehicles — inside high-fidelity simulations before they are deployed in the real world. This closes a significant pre-deployment safety gap for physical AI: the absence of systematic, AI-driven adversarial testing that can surface failure modes before they manifest as real-world safety incidents. Residual gaps include the maturity of simulation fidelity relative to edge-case real-world conditions, and the absence of established industry benchmarks or regulatory acceptance criteria that would let safety evidence transfer across organisations.

NVIDIA and Hugging Face Launch GR00T 1.7 Robot Model

NVIDIA and Hugging Face Launch GR00T 1.7 Robot Model

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.8 NVIDIA AI Blog

NVIDIA and Hugging Face have integrated the Isaac GR00T 1.7 vision-language-action model, Isaac Teleop framework, and a 350,000-trajectory open dataset into the LeRobot open-source robotics library, creating an end-to-end open pipeline for training and deploying physical AI systems. This dramatically lowers the barrier to fine-tuning and deploying robot foundation models, expanding the attack surface across the full ML supply chain — from poisoned community datasets to adversarially crafted demonstrations used in teleop data collection. Defenders responsible for robotics deployments must now contend with a large, loosely governed open-source ecosystem where compromised models or datasets can directly translate to unsafe physical-world behaviour.

Odyssey Launches Physical World Model Platform Backed by Amazon

Odyssey Launches Physical World Model Platform Backed by Amazon

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.2 TechCrunch AI

Odyssey has raised a $310M Series B to scale its world model platform, which ingests real-world physical environment data to generate interactive simulations, video, and training environments for robotics and gaming. The platform's reliance on large-scale physical data collection, multi-tenant simulation outputs, and deep AWS infrastructure integration introduces supply chain, data poisoning, and adversarial simulation risks defenders should assess. Organizations consuming Odyssey-generated synthetic environments for robotics training or game content pipelines are newly exposed to integrity attacks targeting the underlying world model.

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