Practitioner research on securing autonomous AI agent systems. Threat models, defensive controls, red-team frameworks, and detection patterns — built from hands-on experience, not theory.
A 6-stage attack lifecycle mental model for autonomous AI agent systems. RECON → INJECT → HIJACK → ESCALATE → EXFILTRATE → PERSIST. Extends MITRE ATLAS and OWASP LLM Top 10 into agent-specific vectors.
Read the full analysis →Six practitioner articles across the security lifecycle. ~170 minutes of content, 77 verified references.
Pre and post tool-call enforcement with real code. 3 architectures: CLI, SDK, IDE.
Tool poisoning, SSRF, rug pulls. 6 attack vectors with real CVEs and documented incidents.
AgentDojo, PyRIT, Garak, CyberSecEval. Quantitative data and methodology.
Monitoring, confidence scoring, anomaly detection. OpenTelemetry, LangSmith, Driftbase.
8 frameworks, 6 governance gaps, board-level data. NIST, EU AI Act, Singapore MGF.
6 security categories, organized by the practitioner lifecycle.
New research on agentic AI security — threat models, defensive patterns, red-team frameworks. Practitioner content, no spam.