Mara Voss

Mara Voss writes about the security implications of autonomous systems from the inside out—having spent years building, breaking, and rethinking agentic pipelines before the term entered mainstream vocabulary. Her work focuses on the places where AI autonomy and adversarial reality collide: tool call boundaries, memory persistence, trust propagation across agent graphs, and the quiet failures that don't make incident reports. She writes for practitioners who are already in the weeds, not for audiences who need convincing that agentic security is worth taking seriously.

Articles by Mara Voss

• The Agentic SOC Is Here: How Salesforce, Datadog, and GreyNoise Are Putting Autonomous Agents on the Front Line —
• Stripe, Snap, and Sondera on Stopping Prompt Injection Without Neutering Your Agents — Stripe, Snap, and Sondera's practitioners explain why prompt engineering is not a security boundary for agentic systems - and what capability-based authorization actually looks like in production.
• Inside Trail of Bits' AI-Native Transformation: Dan Guido on Building a Security Firm Around Autonomous Agents — Dan Guido and Trail of Bits show why AI-native security consulting requires rearchitecting for autonomous scale - not wrapping LLMs around existing processes.
• 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.
• Inside LLM Security Flaws: What the Latest Vulnerability Research Means for Agentic AI Systems —
• How Open-Source Security Layers Are Closing the Safety Gaps in Agentic AI Systems —
• How Agentic AI Coding Tools Are Becoming Prime Targets for IDE-Based Malware Attacks —
• 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.
• Detecting Malicious Tool Calls in Agentic AI Systems —
• 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.
• 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.
• Privilege Escalation in Agentic Systems: How Agents Acquire Permissions They Shouldn't Have — Privilege escalation in agentic systems happens when agents acquire access beyond their intended scope - and most frameworks make this easy by conflating capability with authorization.
• Tool Call Interception: The Attack Vector Hidden in Plain Sight — Tool call interception lets attackers manipulate what your agent believes about the world - without ever touching the model.
• Why Teams Skip Threat Modeling — And How to Change That — Threat modeling for agentic systems is unfamiliar territory - here is why teams skip it and how to build the habit without overhauling your workflow.
• 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.
• The AI Security Blind Spots Most Teams Don't Talk About — AI security blind spots hide in the gaps between familiar frameworks and newer risks - here's how to find them before someone else does.