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HIGH Rogue AI Agents Read Indian Org Data via Exposed Credentials // FIRST LOOK Microsoft Launches Guardian Agents to Monitor AI Agent Behaviour // FIRST LOOK NVIDIA Launches Nemotron 3.5 Super VL and Bolt Agent Models // FIRST LOOK Laminar YC S24 Launches flow-1 for AI Agent Trace Analysis // HIGH OpenCode Console Breach Exposes API Keys, Fuels AI Distillation Attack // CRITICAL AI Agent Army Used to Breach John Deere and SharkNinja // FIRST LOOK OpenAI Decisions API Brings Typed AI Triage to SOC Platforms // FIRST LOOK OpenAI, Meta and Google AI Agents Blocked by Major Web Services // MEDIUM Shadow AI: Personal ChatGPT Use Leaking Corporate Data at Work // FIRST LOOK Check Point Launches Dangerous Deviation Detection for AI Agents //
POLICY-ENFORCEMENT

Policy-Enforcement

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AWS Launches Trust in Depth Controls for Autonomous AI Agents

AWS Launches Trust in Depth Controls for Autonomous AI Agents

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.8 AWS Machine Learning Blog

AWS has published a framework called 'Trust in Depth' for Amazon Bedrock agents, introducing structured autonomy controls that allow organisations to grant AI agents expanded operational scope while maintaining human oversight and policy-enforced boundaries. For defenders, this closes a meaningful gap in agentic AI governance — specifically the binary choice between fully supervised and fully autonomous agent operation — by offering graduated trust tiers tied to policy controls. Residual gaps remain around cross-provider interoperability, the maturity required to define and maintain granular trust policies at scale, and the absence of runtime behavioural anomaly detection as a complement to the static policy framework.

Excessive Agency in Deno AI Agents Demands Security Controls

Excessive Agency in Deno AI Agents Demands Security Controls

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 HN AI Security

Deno has released Claw Patrol, an open-source security firewall designed to sit between AI agents and production systems, intercepting and policy-gating actions before they reach critical infrastructure. The tool addresses the growing threat of excessive agency in agentic AI systems by allowing operators to write HCL rules that can block destructive operations or require human approval for sensitive actions like Kubernetes pod deletions. This represents a practical defensive tooling response to the OWASP LLM08 Excessive Agency risk, which has become increasingly acute as autonomous agents gain broader access to production environments.

Brex CrabTrap: LLM Proxy Blocks Agentic AI Prompt Injection

Brex CrabTrap: LLM Proxy Blocks Agentic AI Prompt Injection

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

Brex has open-sourced CrabTrap, an HTTP proxy that uses an LLM-as-a-judge architecture to intercept, evaluate, and block or allow requests made by AI agents in real time against configurable policies. The tool targets a critical gap in agentic AI deployments — the lack of runtime guardrails for autonomous agent actions — and represents a practical defensive control against excessive agency and prompt injection exploitation. Its production-oriented design positions it as a notable contribution to the emerging agentic AI security toolchain.

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