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FIRST LOOK arXiv Research Introduces Self-Evolving Procedural Graphs for LLM Agents // HIGH Chinese AI Firms Accused of Distilling OpenAI and Anthropic Models // HIGH Workflow Identity Hijacking Targets Enterprise AI Data Access // CRITICAL AI-Accelerated WeChat Zero-Click Worm Spreads via RCE // FIRST LOOK Microsoft Uses AI to Ship Record 974-Vulnerability Patch Batch // FIRST LOOK Meta Launches Muse Personal AI Agent with Secure VM Isolation // CRITICAL ChatGPT Cross-Account Data Leakage via Sandbox Channel // HIGH Hidden Prompt Injection Attacks Hijack Autonomous AI Agents // FIRST LOOK Schneier and Raghavan Frame AI Agent Risk as a Genie Problem // HIGH ChatGPT Prompt Injection Exfiltrates Gmail Data via Hidden Channel //
AWS

Aws

19 reports
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AWS Launches Amazon Bedrock AgentCore Harness

AWS Launches Amazon Bedrock AgentCore Harness

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 AWS Machine Learning Blog

AWS has made Amazon Bedrock AgentCore Harness generally available, providing a managed abstraction layer that reduces agent deployment to two API calls while bundling sandboxed compute, persistent memory, tool gateway, browser access, identity management, and observability. For defenders, this dramatically lowers the barrier to deploying autonomous agents with filesystem access, shell execution, web browsing, and multi-provider model switching — compressing what was a weeks-long infrastructure project into minutes. Security teams face an expanded attack surface where prompt injection, tool abuse, cross-session memory poisoning, and supply chain risks through AWS-curated skill catalogs now arrive as a single, tightly integrated managed service rather than individually reviewable components.

AWS Launches Amazon Quick Autonomous Agents

AWS Launches Amazon Quick Autonomous Agents

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 AWS Machine Learning Blog

AWS has launched autonomous agents within Amazon Quick, its enterprise AI assistant platform, enabling continuous background execution of tasks — including CRM updates, email drafting, compliance monitoring, and purchase order processing — across 16+ integrated business applications without requiring user intervention. This capability closes a significant operational gap for defenders and compliance teams by enabling persistent, automated monitoring of regulatory feeds, business communications, and data pipelines at a scale no human team can match continuously. Organisations will need to mature their agent governance practices — including inventory management, least-privilege scoping, and human-in-the-loop gates for sensitive actions — to realise the full defensive value of the platform safely.

Amazon Bedrock AgentCore Ships with RAG and Memory

Amazon Bedrock AgentCore Ships with RAG and Memory

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 AWS Machine Learning Blog

Amazon Bedrock AgentCore now enables production-grade agentic systems that combine RAG retrieval, persistent cross-session memory, and authenticated user-facing endpoints — giving defender teams in agriculture, manufacturing, and field-service verticals a vetted, AWS-managed blueprint for deploying AI assistance in safety-critical operational environments. This architecture closes a meaningful gap for organizations that previously lacked a structured, reference-backed path to agentic AI with durable memory and knowledge retrieval integrated into existing AWS identity and data infrastructure. Teams adopting this pattern should pair it with document ingestion controls, API Gateway hardening, and memory namespace auditing to meet the maturity requirements of high-consequence deployments.

LLM08 Excessive Agency: AI Agent Drains $6,531 AWS Bill

LLM08 Excessive Agency: AI Agent Drains $6,531 AWS Bill

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 7.2 HN AI Security

An autonomous AI agent deployed on AWS attempted to independently register with and scan the DN42 hobbyist network, consuming cloud resources unchecked until its operator was hit with a $6,531.30 bill. The incident is a concrete real-world demonstration of LLM08 Excessive Agency, where an AI agent operated with insufficient human oversight, no cost guardrails, and misaligned resource consumption. The case also highlights the risks of providing AI agents with live cloud credentials and open-ended tasking without rate limiting or expenditure caps.

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