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FIRST LOOK OpenAI Releases Astra Cybersecurity Evals and Safeguard Controls // FIRST LOOK OpenAI Pauses Astra Model Over Critical Cybersecurity Threshold // CRITICAL OpenAI Agents Exploit Artifactory RCE in Hugging Face Attack // FIRST LOOK Cloudflare Launches Kitesurf, a Cloud Browser Built for AI Agents // FIRST LOOK PortSwigger HTTP Terminator Ships AI-Driven Desync Research // CRITICAL CVE-2026-12537: Gemini CLI RCE and Claude Code Secret Leak // HIGH Atlassian Rovo Prompt Injection Leaks Jira Data to Attackers // CRITICAL Claude and ChatGPT Hijacked via Zero-Click Prompt Injection // HIGH ChatGPT Sandbox C2 Attack Demonstrated at Black Hat 2026 // HIGH Meta AI Hacks External Systems in Cybersecurity Test //
SELF-HOSTED-LLM

Self-Hosted-Llm

4 reports
All LLM Security Agentic AI Industry News Supply Chain Research Prompt Injection First Look: Security Regulatory Jailbreaks Adversarial ML Model Theft Data Poisoning AI Governance AI Security AI Security Tools Deep Signal Identity & Access Management Intelligence Report Security Operations
Iroh Launches Mesh LLM for Distributed AI Across Peer Nodes

Iroh Launches Mesh LLM for Distributed AI Across Peer Nodes

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 HN AI Security

Mesh LLM on iroh enables teams to pool GPUs across arbitrary machines into a single OpenAI-compatible inference endpoint, distributing model layers peer-to-peer over authenticated QUIC connections with no central server. This dramatically expands the attack surface for defenders: the decentralised, pluggable architecture introduces new vectors for node impersonation, malicious plugin injection, inter-stage activation tampering, and supply chain compromise across every participating endpoint. Security teams evaluating self-hosted or federated AI deployments must treat each mesh peer as a potential adversary boundary, not a trusted internal resource.

Z.ai Releases GLM-5.2 Open-Weights 753B LLM

Z.ai Releases GLM-5.2 Open-Weights 753B LLM

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 6.2 Simon Willison

Z.ai has released GLM-5.2, a 753-billion-parameter mixture-of-experts model under an MIT license, ranking as the top open-weights model on the Artificial Analysis Intelligence Index and second on the Code Arena WebDev leaderboard. For defenders, the combination of frontier-level capability, unrestricted open-weights distribution, and a 1-million-token context window materially lowers the barrier for threat actors to self-host a highly capable model outside any provider's safety controls. The model's agentic coding performance and massive context window expand the viable attack surface for automated code generation, targeted phishing, and large-scale document analysis without API-level monitoring.

CVE-2026-7482: Ollama Heap Read Exposes API Keys

CVE-2026-7482: Ollama Heap Read Exposes API Keys

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 SecurityWeek

A critical heap out-of-bounds read vulnerability (CVE-2026-7482, CVSS 9.3) in Ollama's GGUF model loader allows unauthenticated remote attackers to exfiltrate sensitive heap memory — including API keys, prompts, and PII — using just three API calls. With approximately 300,000 Ollama instances publicly exposed and no authentication required by default, the attack surface is immediately and broadly exploitable. The vulnerability has been patched in Ollama version 0.17.1, but unpatched internet-facing deployments remain at critical risk.

Flowise and n8n: Auth Bypass in Exposed LLM Services

Flowise and n8n: Auth Bypass in Exposed LLM Services

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.5 The Hacker News

A scan of over one million exposed AI services found pervasive security failures including absent authentication, leaked API keys, and exposed business logic across self-hosted LLM deployments. Agent management platforms such as Flowise and n8n were discovered internet-exposed without access controls, revealing credential lists and internal workflows. The findings indicate systemic misconfiguration risk as enterprises race to self-host AI infrastructure without applying baseline security practices.

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