SECURITY FOR THE NEWAGENTIC ARCHITECTURE
Formal threat modeling meets automated red-teaming.
Agentic Systems Are Under Attack
Data Exfiltration
Agentic systems can be manipulated to leak sensitive data through tool calls, system prompts, or memory retrieval.
Unauthorized Actions
Compromised agents execute harmful operations — deleting data, sending emails, or escalating privileges.
Zero Certified Defenses
No formal verification, no certified robustness guarantees. The industry is flying blind.
Three Layers of Defense
Policy Engine
Declaratively define what your agentic systems should and shouldn't do. Formal safety specifications, not vague guardrails.
Red Team Engine
Autonomous adversarial testing. Systematically discover vulnerabilities before attackers do.
Runtime Guardian
Real-time monitoring and enforcement. Every agent action is validated against your security policies.
How It Works
Configure agent topology
Define your agent architecture — tools, permissions, data flows. LuneGuard maps the attack surface.
Automated vulnerability discovery
Our red team engine systematically probes for weaknesses: prompt injection, tool abuse, data exfiltration paths.
Deploy runtime sentinel
Activate real-time monitoring. Every action is verified against formal safety policies before execution.
Research & Technical Foundation
LuneGuard's defense methodology is grounded in original research at the intersection of formal methods, adversarial machine learning, and security economics. We develop principled approaches to securing autonomous systems — moving beyond heuristic guardrails toward mathematically grounded safety guarantees.
Formal Threat Modeling
We model agentic systems as state machines with tool-access interfaces, systematically enumerating attack surfaces across prompt channels, tool invocations, and memory retrieval paths. Our framework captures multi-step attack sequences that exploit compositional vulnerabilities in agent architectures.
Adversarial ML for Agents
Extending adversarial robustness techniques beyond image classifiers to the agentic domain. We develop novel attack generation methods that target the instruction-following pipeline, including gradient-free optimization approaches for black-box agent systems and transfer attacks across model families.
Certified Robustness
Deriving provable guarantees on agent behavior under adversarial input perturbations. Our approach adapts randomized smoothing and interval bound propagation to the discrete, sequential decision-making setting of tool-using agents, providing certificates that bound worst-case policy deviation.
Game-Theoretic Analysis
Modeling the attacker-defender dynamic as a Stackelberg game where the defender commits to a monitoring policy and the attacker best-responds. This formulation enables us to derive optimal defense allocations across heterogeneous agent deployments and quantify the cost of security.
Technical papers detailing these methodologies are actively being prepared for submission to leading security and machine learning conferences. Pre-prints will be made available upon publication.
All research content, methodologies, and technical approaches described herein are proprietary to LuneGuard and protected under applicable intellectual property law. Unauthorized reproduction, distribution, or use of this material without express written permission is strictly prohibited. © 2026 LuneGuard.
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