AI Red Team Engineering notes, research references, and field notes on AI systems, responsible AI, governance, and compliance.
Global search

Search AI Red Team

Search across the complete knowledge base.

Enter at least two characters to begin.
↑↓ select Enter open Browse the full research archive
Huntress · September 28, 2026

Custom GPT lures led users into a ClickFix malware chain

analysis Agent SecurityAI Red Teaming
Why it matters

Huntress traced malicious custom GPTs that redirected users from ChatGPT to a fake verification page on Google Sites. Victims were instructed to run a terminal command, triggering an MSI installer, DLL sideloading through legitimate signed applications, and a remote-access trojan. Huntress reports at least 40 incidents associated with the campaign’s Google Sites page, but confirmed only two entered through a custom GPT. Its sample analysis also found persistence that could recreate deleted startup entries. Hosting a lure on a trusted AI platform gave it credibility without requiring a compromise of the underlying model.

My takeaway: Correlate browser activity with PowerShell-to-MSI execution, unusual signed-binary locations and newly created persistence. During response, stop the running implant before removing its scheduled task and Run entry so it cannot recreate them.
Keep exploring

More curated notes connected through Agent Security and AI Red Teaming.

OpenAI News · framework

Pacing model development in an era of cyber-critical capabilities

OpenAI says preliminary evidence that Astra may meet its Critical cybersecurity threshold led it to pause frontier reinforcement-learning work for two weeks and keep its largest planned run on hold. New safeguards include stronger workload and network isolation, continuous boundary testing, token-level monitoring that escalates suspicious tool activity, and broader alignment checks for deception, reward hacking, and unauthorized access.

OWASP GenAI Security Project · guide

OWASP Top 10 for Agentic Applications for 2026

OWASP's community guide organizes agentic-system risk into ten categories, including goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behavior. It provides a shared taxonomy and mitigation starting point rather than a certification checklist or evidence that a deployed system is secure.

OpenAI News · framework

A blueprint for democratic governance of frontier AI

OpenAI proposes a three-part U.S. frontier-AI governance model: harmonize emerging state safety laws into a federal baseline, strengthen CAISI as an evaluation and standards institution, and coordinate a broader resilience program. Proposed controls include severe-risk evaluations, transparency reports, independent audits, safety-incident reporting, model-weight security, whistleblower protection, and periodic technical assessments.