LIVE FEED
FIRST LOOK Yellow Teams Bring AI Offense and Defense Into One Security Function // FIRST LOOK Tracebit Ships AWS Context Bombing Defence Against AI Hacking Agents // FIRST LOOK FriendMachine Launches Jacquard Lang for AI-Written Code Review // CRITICAL Check Point 2026 AI Security Report: LLMs Now Run Live Attacks // FIRST LOOK OpenAI GPT-5.6 Sol Ships Faster Parallel Tool-Use for Agents // FIRST LOOK Meta Launches Muse Image with Public Instagram Photo Reuse // FIRST LOOK Estonia Launches State-Issued Digital IDs for AI Agents // HIGH AI Widens Skill-Ability Gap, Enabling Autonomous Cyberattacks // FIRST LOOK OpenAI Expands ChatGPT Into Family and Caregiver Households // FIRST LOOK Iroh Launches Mesh LLM for Distributed AI Across Peer Nodes //
HUMAN-IN-THE-LOOP

Human-in-the-Loop

4 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
FriendMachine Launches Jacquard Lang for AI-Written Code Review

FriendMachine Launches Jacquard Lang for AI-Written Code Review

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.2 HN AI Security

Jacquard is an open-source programming language purpose-built for a workflow where ML models generate code and humans review it, featuring a compact surface syntax, OCaml-based checker, and C-emitting compiler. This human-in-the-loop design introduces a new class of trust boundary risk: defenders must assess whether the review layer provides genuine semantic verification or creates a false sense of security that sophisticated AI-generated code can exploit. Supply chain and prompt-injection-adjacent risks emerge when the AI code-generation step itself becomes a target for adversarial manipulation, producing subtly malicious output that passes superficial human review.

Bayer and Thoughtworks Ship PRINCE Agentic RAG Platform

Bayer and Thoughtworks Ship PRINCE Agentic RAG Platform

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 HN AI Security

Bayer AG and Thoughtworks have published a detailed case study on PRINCE, a production agentic RAG system combining multi-agent orchestration, Text-to-SQL, and human-in-the-loop workflows to answer complex pharmaceutical preclinical research questions and draft regulatory documents. The system's architecture — spanning intent clarification, planning, retrieval, reflection, and writing agents with access to decades of safety study data — introduces a broad attack surface including prompt injection across agent boundaries, SQL injection via natural language, and sensitive data exfiltration through compromised agent outputs. Defenders evaluating similar agentic platforms should treat each inter-agent handoff as a trust boundary requiring independent validation and focus on data leakage controls given the sensitivity of preclinical regulatory data.

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.

Robinhood Prompt Injection Enables Autonomous Trade Attacks

Robinhood Prompt Injection Enables Autonomous Trade Attacks

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 HN AI Security

Robinhood has launched agentic trading and a virtual credit card that allow third-party AI agents to autonomously execute stock trades and payments on behalf of users via a Model Context Protocol (MCP) integration. This architecture introduces significant attack surface through prompt injection, excessive agency, and insecure plugin design risks inherent to LLM-driven autonomous financial action. The delegation of real financial authority to AI agents with limited human-in-the-loop controls represents a systemic risk to retail investors if agent pipelines are compromised or manipulated.

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