Securing the agentic era, one attack surface at a time.
Categories
Attack Surface & Threat Modeling — Agentic systems introduce attack surfaces that traditional threat modeling frameworks weren't built to handle—autonomous tool use, multi-agent trust boundaries, dynamic memory, and delegated credentials all demand a different analytical approach. This category covers how to systematically identify, map, and prioritize threats across the full agentic stack, from design-time architecture reviews through runtime exposure analysis. Expect concrete methodologies, worked examples, and honest assessments of where current frameworks fall short.
Defensive Architecture & Security Controls — Knowing where the attack surface is only half the work—the harder problem is building systems that resist exploitation under real operating conditions. This category covers concrete defensive patterns for agentic systems: sandboxing tool execution, enforcing least-privilege credential delegation, designing trust boundaries in multi-agent pipelines, and implementing runtime controls that don't cripple the autonomy you're trying to protect. The focus is architectural decisions that hold up when things go wrong, not checklists that look good in a review.
Red Teaming & Offensive Research — Defending agentic systems requires understanding how they actually break—not in theory, but under deliberate, adversarial pressure. This category covers offensive techniques, red-team methodologies, and hands-on exploit research specific to autonomous agent architectures: prompt injection campaigns, tool call interception, memory poisoning walkthroughs, and multi-agent lateral movement. If you want to know what an attacker does after they get a foothold in an agentic pipeline, this is where to look.
How to Red-Team an Agentic System: A Practitioner's Methodology — A practitioner's methodology for red-teaming agentic systems - covering tool inventory mapping, authority probing, prompt injection, and lateral movement simulation in autonomous agent environments.
Sandboxing AI Agent Tool Calls: An Isolation Architecture — Tool misuse in agentic systems rarely looks like a breach - it looks like permitted behavior in an unanticipated sequence, which is exactly why tool call sandboxing requires runtime isolation, not just access control.
Defending Agentic AI Memory: Detection and Isolation Controls — Memory poisoning in agentic AI systems corrupts stored state that persists across sessions - here is how to detect it, isolate it, and verify memory integrity before it becomes an incident.
Multi-Agent Lateral Movement: Red-Team Techniques and Detection — Multi-agent lateral movement turns a single compromised agent into full pipeline access. Here are the red-team techniques attackers use — credential harvesting, tool registry poisoning, prompt injection across boundaries — and what detection actually looks like in practice.
LangChain, LangGraph, and AutoGen Security Gaps: 7 Fixes for Agentic Frameworks (2026) — LangChain, LangGraph, and AutoGen have concrete, exploitable security vulnerabilities — in tool calling, state management, and inter-agent trust. Here is where the gaps live, how attackers exploit them, and the seven defensive controls you can implement today.
Memory Poisoning in Long-Running Agents: Attack Patterns — Memory poisoning in long-running agents corrupts the persistent context that drives every future decision - here is how to detect it, isolate it, and respond before the damage spreads.
How to Map Your Attack Surface Before Attackers Do — Learn how to map your attack surface before attackers do - with practical steps for finding exposed assets, building an inventory, and staying ahead of new risks.
Threat Modeling AI Agents: What STRIDE Misses — Threat modeling for agentic AI systems exposes the gaps that STRIDE was never designed to find - corrupted context, dynamic tool trust, and permission escalation that looks like normal operation.