Multi-Agent System for Polymorphic Threats

Status: Experimental Deployment  

MASPT is a multi-agent simulation environment built to test how AI systems respond to evolving, shape-shifting threats.

About MASPT: A Framework for Dynamic AI Security Research

MASPT (Multi-Agent System for Polymorphic Threats) is a simulation environment designed, by Nick Norman, to investigate the resilience of AI systems when confronted with adaptive, shape-shifting digital threats. This framework employs a system of three intelligent agents, each with a defined role, to simulate adversarial pressure, observe system performance, and identify detection limitations.

MASPT sits at the intersection of several advanced AI domains:

  • Adversarial Machine Learning
    The Polymorphic Agent creates manipulated inputs to bypass detection, exposing blind spots in security models before real attackers do.

  • Multi-Agent Systems
    MASPT uses a closed-loop of agents that challenge, defend, and verify—mimicking how real adversaries and defense systems evolve over time.

  • Bias Mitigation
    The Verifier Agent audits the system’s decisions, flagging unfair or skewed patterns, with human-in-the-loop support for added accountability.

  • Reinforcement Learning (In Progress)
    We're exploring learning loops that help both attacker and defender agents evolve—simulating a dynamic "arms race" of tactics.

MASPT gives organizations a safer, smarter way to probe the limits of their AI security—before real threats do. 

Agent Roles at a Glance

MASPT is designed around a set of coordinated agents—each with a focused role in simulating, detecting, and verifying polymorphic threats. By distributing tasks across separate agents, the system mirrors real-world threat dynamics while surfacing blind spots, testing defense layers, and identifying bias with both machine and human insight.
Here’s how the agents operate:

Evolve & Attack (Polymorphic Agent)

This agent generates evolving threat behaviors—designed to mimic real-world attacks. It randomly alters tone, structure, and metadata to sneak past defenses. Each attempt is logged and tracked to expose the system to a wide range of changing threats—avoiding repetition and encouraging smarter adaptation over time.

Analyze & Flag (Detector Agent)

The Detector Agent reviews each incoming message and applies predefined rules to flag potential threats. It focuses on pattern recognition, context analysis, and anomaly detection. If it believes something’s suspicious, it records the reasoning and confidence score for further review.

Audit & Confirm (Verifier Agent)

The Verifier Agent sits outside the core environment. After the Detector finishes, this agent re-evaluates flagged and missed messages using independent logic to look for false positives or bias. It plays a critical role in stress-testing the system’s fairness and reliability—especially for edge cases the detector might overlook.

Who is MASPT for?

MASPT is designed for organizations and researchers who need a safe, intelligent space to explore how AI systems hold up under adaptive, evolving threats. As a simulation, it doesn’t replace your defenses—it helps you understand them. From bias and detection gaps to resilience under pressure, MASPT offers a way to study what works, what breaks, and what needs to be rethought before it’s too late.
Interested in learning more about this simulation?
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