Deep dives into the technology, research, and best practices for securing AI agents across every modality.
Why adaptive attackers are becoming necessary—and why agents still need system-level proof
OpenAI's GPT-Red shows what changes when an attacker learns from every attempt. We compare human, static, search-based, gradient, and reinforcement learning approaches across models and agentic systems.
What we learned building continuous adversarial testing for multi-agent applications
High-level results from adaptive red teaming across multi-agent workflows, including verified attack success, cross-target transfer, and practical lessons for defenders.
A Flask e-commerce backend with 12 planted vulnerabilities across three detection layers
We built a deliberately vulnerable Flask app with 12 security flaws — from SQL injection to hallucinated packages to three-hop taint chains. Here's a walkthrough of each one and how the scanner catches it.
Why AI agents with system access need a prompt firewall
Autonomous AI assistants like OpenClaw can manage your email, files, and payments. That power creates 31 distinct attack patterns across 5 categories. Here's the threat model — and how to defend against it.
From Amazon Q exploits to Cursor crypto drains — real incidents, real lessons
AI coding agents are becoming a prime attack vector. A comprehensive look at real-world incidents including the Checkmarx 'Lies-in-the-Loop' bypass, Langflow code injection, and what they mean for your security posture.
