LIVE FEED
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 // FIRST LOOK Capsule Security Launches AI Circuit Breaker for Rogue Agents // MEDIUM Rogue AI Agents Drive Insurers to Rethink Cyber Risk // HIGH LLM-Assisted Intrusions Hit Latin American Orgs via NextChat // HIGH GPT 5.6-Cyber Breaks VM Sandboxes, Exposing Agent Limits //
AGENTIC-SECURITY

Agentic-Security

4 reports
All LLM Security Agentic AI Industry News Research Supply Chain First Look: Security Prompt Injection Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning Deep Signal Intelligence Report AI Governance AI Security AI Security Tools Identity & Access Management Monthly Review Security Operations
Rogue LLM Endpoint Hijacks Coding Agent Sessions via Free API

Rogue LLM Endpoint Hijacks Coding Agent Sessions via Free API

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 9.0 SANS Internet Storm Center

A researcher's internet-exposed LLM honeypot was discovered by scanners, relabeled as a DeepSeek-compatible endpoint, and incorporated into 'free' AI backend infrastructure — ultimately receiving a full 224 KB coding-agent session including filesystem listings, tool manifests, and private file contents. The incident demonstrates that a malicious rogue model endpoint occupies a privileged position in an agent's control plane, capable of issuing tool-call responses that the agent may execute locally without further verification. This represents a novel supply-chain-style threat where the adversary is not a compromised trusted service but a counterfeit reasoning backend actively solicited by users chasing free API access.

Anthropic MCP Server Security Risks and Secrets Exposure Explained

Anthropic MCP Server Security Risks and Secrets Exposure Explained

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 The Hacker News

This analysis examines how Model Context Protocol (MCP) servers — the middleware layer connecting AI agents to enterprise tools and data — routinely store credentials in plaintext configuration files and propagate them across ungoverned environments. For defenders, the piece closes an awareness gap by naming concrete credential exposure patterns unique to the agentic AI layer, giving security teams a structured surface to inventory and govern. What remains unaddressed is tooling maturity: automated discovery, centralised secrets management integration, and runtime visibility into MCP server activity are still nascent capabilities that organisations must build rather than buy.

AI Agents Weaponise Vulnerability Discovery as AI-Generated Code Expands Attack Surface

AI Agents Weaponise Vulnerability Discovery as AI-Generated Code Expands Attack Surface

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.5 Dark Reading

AI agents are now capable of autonomously discovering and exploiting obscure software vulnerabilities, raising the stakes for defenders already struggling with the volume of potentially insecure AI-generated code flooding codebases. The convergence of agentic exploitation capabilities and mass AI-assisted development creates a compounding risk: more vulnerabilities introduced at scale, and more capable automated systems to find and abuse them. Security teams must adapt their tooling, processes, and threat models to account for both sides of this AI-driven equation.

GPT-5.4-Cyber Jailbreak and Prompt Injection Risks

GPT-5.4-Cyber Jailbreak and Prompt Injection Risks

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 The Hacker News

OpenAI has launched GPT-5.4-Cyber, a cybersecurity-optimised model variant, alongside an expanded Trusted Access for Cyber (TAC) programme targeting authenticated defenders and security teams. While the initiative is framed as a defensive measure, the dual-use nature of a vulnerability-detection model introduces significant risk of adversarial inversion — where threat actors could exploit the same capabilities to discover and weaponise unpatched vulnerabilities at scale. OpenAI acknowledges this risk and states it is iteratively strengthening safeguards against jailbreaks and adversarial prompt injection as access broadens.

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