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FIRST LOOK AWS Brings Secure Self-Service AI Agents to Financial Services // CRITICAL Claude AI Used by Yemen Cell to Develop Guided Missiles // HIGH BragJack Hijacks AI Browser Agents via Malicious Extensions // FIRST LOOK AWS Adds Defense-in-Depth Authorization for MCP Tools on Amazon Q // FIRST LOOK Anthropic CEO Calls for AI Control Over Capability Race // HIGH Claude Used to Breach OpenAI Employee Account via Forum Flaw // FIRST LOOK Anthropic Embeds Accenture as Its First Third-Party AI Safety Evaluator // CRITICAL AI Hallucination in Military Intel Nearly Triggers US Strike // FIRST LOOK arXiv Paper Formalises Linguistic Illegibility in LLM Security // HIGH Gemini AI Agent Breaches Three Companies via Password Guessing //
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Almanac (YC S26) Launches Agentic AI with Self-Updating Company Wiki

Almanac (YC S26) Launches Agentic AI with Self-Updating Company Wiki

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 5.8 HN AI Security

Almanac is a persistent AI agent that connects to company tools, maintains a self-updating internal wiki, and executes multi-step work tasks autonomously via its own browser and login sessions. For defenders and security-conscious organisations, it introduces a structured, auditable knowledge graph of internal operations — every wiki entry links back to its source, providing a traceable record of AI-driven decisions and actions. Residual gaps centre on the maturity of access governance, wiki poisoning safeguards, and the breadth of autonomous action the agent can take before human confirmation is required.

GitHub Ships Data Analytics Agent Built on Copilot

GitHub Ships Data Analytics Agent Built on Copilot

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.8 GitHub Blog

GitHub has published a detailed engineering account of how it built an internal data analytics agent using GitHub Copilot, exposing the architectural patterns — including natural language-to-SQL translation, autonomous tool invocation, and internal data access — that underpin such systems. For defenders, this blueprint highlights concrete risks around prompt injection into analytics pipelines, excessive agency over sensitive internal datasets, and the challenge of auditing LLM-generated queries before execution. Organisations adopting similar agentic analytics patterns should treat this as a reference threat model rather than a safe-to-copy architecture.

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