Aws

11 reports

All LLM Security Agentic AI Industry News Research Supply Chain Prompt Injection First Look: Security Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning AI Security Tools Security Operations

AWS Adds Bedrock Guardrails Best Practices for Code Generation

FIRST LOOK ATLAS OWASP MEDIUM ▲ 6.2 AWS Machine Learning Blog Jul 24, 2026

AWS has published guidance on applying Amazon Bedrock Guardrails to code generation workflows, detailing how to configure content filters, topic denials, and output controls for AI-assisted coding pipelines. For defenders, this surfaces the inverse risk: organisations that misconfigure or partially implement these guardrails expose code generation endpoints to prompt injection, malicious code output, and filter-evasion attacks. Security teams must treat guardrail configuration as a first-class security control, not a default-on safety net.

Netwrix Analysis: AI Agents Widen the Non-Human Identity Gap

FIRST LOOK ATLAS OWASP HIGH ▲ 8.2 BleepingComputer Jul 11, 2026

A Netwrix-sponsored analysis highlights how AI agents are rapidly proliferating machine identities inside enterprise environments, creating credentials and inheriting permissions far faster than existing identity governance can track. The core risk is that AI agents operate outside traditional human-lifecycle identity controls, leaving security teams unable to enumerate what exists, who owns it, or what it can access. Defenders face an expanding blind spot where a single compromised agent credential can chain laterally across cloud services, SaaS platforms, and secrets stores — as demonstrated by the UNC6395/Drift OAuth campaign against Salesforce environments in 2025.

AWS Launches Multi-Turn RL for Amazon Nova

FIRST LOOK ATLAS OWASP HIGH ▲ 7.2 AWS Machine Learning Blog Jul 07, 2026

AWS has released a production-grade, event-driven multi-turn reinforcement learning training infrastructure for Amazon Nova models on SageMaker HyperPod, enabling enterprises to train agents that learn tool orchestration, error recovery, and sequential decision-making at scale. This materially expands the attack surface by introducing complex reward-routing pipelines, ephemeral compute provisioning, and environment-facing reward workers as new targets for poisoning and manipulation. Defenders must scrutinise the trust boundaries between the Nova Forge SDK, ECS reward workers, and HyperPod training pods, as a compromised reward signal can silently shape model behaviour across entire interaction sequences.

Amazon Q Extension Credential Theft via MCP Injection

ATLAS OWASP HIGH ▲ 8.5 Dark Reading Jul 05, 2026

A vulnerability in the Amazon Q Visual Studio Code extension allows adversaries to plant malicious repositories that execute arbitrary code and exfiltrate cloud credentials. The flaw highlights escalating risks associated with Model Context Protocol (MCP) integrations embedded within AI-powered developer tools. This attack vector represents a growing threat surface as AI coding assistants gain privileged access to developer environments and cloud infrastructure.

AWS Brings NVIDIA Nemotron and OpenAI GPT to GovCloud

FIRST LOOK ATLAS OWASP HIGH ▲ 6.8 AWS Machine Learning Blog Jul 02, 2026

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.

AWS Launches Bedrock AgentCore for Autonomous Payments

FIRST LOOK ATLAS OWASP HIGH ▲ 7.8 AWS Machine Learning Blog Jun 23, 2026

AWS has launched Amazon Bedrock AgentCore Payments, a managed infrastructure layer that enables AI agents to autonomously transact with external model providers and services using the x402 payment protocol, without human intervention. This capability introduces a new class of financial attack surface where compromised or manipulated agents can autonomously spend real funds, exfiltrate value, or be redirected to malicious service endpoints. Defenders must now treat agent payment credentials and spending budgets as first-class financial controls, on par with cloud IAM policies.

AWS SageMaker Ships 100+ Inference Metrics to CloudWatch

FIRST LOOK ATLAS OWASP MEDIUM ▲ 6.2 AWS Machine Learning Blog Jun 19, 2026

AWS has released a deep observability layer for SageMaker AI inference endpoints, emitting over 100 metrics covering GPU health, KV cache pressure, token-level latency, and traffic distribution into a native CloudWatch Insights dashboard with PromQL-compatible export. For defenders, this centralised telemetry surface introduces new reconnaissance and exfiltration vectors: an adversary with read access to CloudWatch or connected third-party tools (Grafana, Datadog) can infer model architecture, request patterns, and capacity limits without touching the model itself. The richness of these signals also raises insider-threat risk, as operational staff now have granular visibility into inference behaviour that can be leveraged to reverse-engineer model characteristics or plan targeted denial-of-service campaigns.

AWS Launches Amazon Bedrock AgentCore Harness

FIRST LOOK ATLAS OWASP HIGH ▲ 8.2 AWS Machine Learning Blog Jun 19, 2026

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

FIRST LOOK ATLAS OWASP HIGH ▲ 8.2 AWS Machine Learning Blog Jun 18, 2026

AWS has shipped autonomous agents in Amazon Quick, an AI assistant that continuously executes tasks — including CRM updates, email drafting, and compliance monitoring — on behalf of users while connected to dozens of enterprise data sources and applications. This dramatically expands the attack surface for business-context compromise: a single successful prompt injection or account takeover can now translate into persistent, automated actions across an organisation's entire connected app ecosystem. Defenders must treat these agents as privileged service accounts with broad, continuous write-access, requiring dedicated monitoring, least-privilege scoping, and explicit human-in-the-loop gates for sensitive actions.

Amazon Bedrock AgentCore Ships with RAG and Memory

FIRST LOOK ATLAS OWASP HIGH ▲ 7.2 AWS Machine Learning Blog Jun 16, 2026

Amazon Bedrock AgentCore now enables production-grade agentic systems that combine RAG retrieval, persistent cross-session memory, and direct user-facing endpoints authenticated only via Cognito Bearer tokens — all surfaced through a single /invocations endpoint. This architecture creates compounded attack surfaces where adversarially crafted content in S3-backed knowledge bases can propagate through the retrieve_and_generate pipeline directly into technician workflows. The persistent AgentCore Memory layer introduces a new cross-session context poisoning vector that does not exist in stateless LLM deployments.

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

ATLAS OWASP MEDIUM ▲ 7.2 HN AI Security Jun 12, 2026

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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