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GOVERNMENT-AI

Government-Ai

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All LLM Security Agentic AI Industry News Supply Chain Research Prompt Injection First Look: Security Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning AI Security Tools Security Operations
Estonia Launches State-Issued Digital IDs for AI Agents

Estonia Launches State-Issued Digital IDs for AI Agents

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 6.8 Dark Reading

Estonia is piloting a framework to issue government-recognised digital identity credentials to AI agents, enabling them to act on behalf of citizens in official government processes. This creates a novel identity and authorisation attack surface where compromised or spoofed agent identities could perform legally consequential government actions without human oversight. Defenders must urgently assess how agent identity verification, credential revocation, and delegation chains are enforced within this new trust model.

AWS Brings NVIDIA Nemotron and OpenAI GPT to GovCloud

AWS Brings NVIDIA Nemotron and OpenAI GPT to GovCloud

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 6.8 AWS Machine Learning Blog

AWS has expanded Amazon Bedrock in GovCloud (US) to include NVIDIA Nemotron and OpenAI's open-weight GPT OSS models, enabling U.S. government agencies and defense contractors to run frontier LLMs within FedRAMP High and DoD SRG compliance boundaries. This expansion introduces large, capable open-weight models into sensitive government mission workflows — including intelligence analysis, security log review, and contract automation — dramatically increasing the consequence of a successful prompt injection or jailbreak. Defenders must account for the elevated impact of model compromise in classified-adjacent environments, supply chain trust assumptions around open-weight model weights, and the risk of agentic workflows operating with privileged data access under reduced human oversight.

Qwen 3.5-397B Model Theft: Rio's LLM Exposed as Rebranded Clone

Qwen 3.5-397B Model Theft: Rio's LLM Exposed as Rebranded Clone

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.8 HN AI Security

Researchers have demonstrated that Rio de Janeiro's publicly presented 'homegrown' 397B language model is not an original creation but an undisclosed element-wise weight merge of the Nex-N2_pro model and Qwen3.5-397B-A17B. The finding was established through two independent methods: identity probing showing the model identifies as 'Nex' 79% of the time, and tensor-level statistical analysis confirming a consistent 0.6/0.4 blend across all 60 layers. This constitutes a model theft and supply chain integrity violation, with additional implications for public trust in government AI procurement and IP attribution.

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