MAESTRO (Multi-Agent Environment, Security, Threat Risk, and Outcome), a framework built for Agentic AI. It's based on the following key principles.


1. Principles
Extended Security Categories: We're expanding traditional categories like STRIDE, PASTA, and LINDDUN with AI-specific considerations. For example:
Multi-Agent and Environment Focus: Explicitly considering the interactions between agents and their environment. A self-driving car must be aware of other cars, objects, and weather conditions.
Layered Security: Security isn't a single layer, but a property that must be built into each layer of the agentic architecture.
AI-Specific Threats: Addressing threats arising from AI, especially adversarial ML and autonomy-related risks.
Risk-Based Approach: Prioritizing threats based on likelihood and impact within the agent's context.
Continuous Monitoring and Adaptation: Ongoing monitoring, threat intelligence, and model updates to address the evolving nature of AI and threats.


2. Elements
MAESTRO is built around a seven-layer, allowing us to understand and address risks at a granular level.

This layered approach decomposes the complex AI agent ecosystem into distinct functional layers: from Foundation Models that provide core AI capabilities, through Data Operations and Agent Frameworks that manage information and development tools, to Deployment Infrastructure and Security layers that ensure reliable and safe operations, culminating in the Agent Ecosystem where business applications deliver value to end-users. Each layer serves a specific purpose while abstracting complexity from the layers above it, enabling modular development, clear separation of concerns, and systematic implementation of AI agent systems across organizations.


A. Layer-Specific Threat Modeling
We'll perform a threat modeling exercise for each of these seven layers, focusing on the specific threats relevant to that layer.

Layer 7: Agent Ecosystem
Description: The ecosystem layer represents the marketplace where AI agents interface with real-world applications and users. This encompasses a diverse range of business applications, from intelligent customer service platforms to sophisticated enterprise automation solutions.
Threat Landscape:
Compromised Agents: Malicious AI agents designed to perform harmful actions, infiltrating the ecosystem by posing as legitimate services.
Agent Impersonation: Malicious actors deceiving users or other agents by impersonating legitimate AI agents within the ecosystem.
Agent Identity Attack: Attacks that compromise the identity and authorization mechanisms of AI agents in the ecosystem, resulting in unauthorized access and control of the agent.
Agent Tool Misuse: AI agents being manipulated to utilize their tools in ways not intended, leading to unforeseen and potentially harmful actions within the system.
Agent Goal Manipulation: Attackers manipulating the intended goals of AI agents, causing them to pursue objectives different from their original purpose or be detrimental to the environment.
Marketplace Manipulation: False ratings, reviews, or recommendations designed to promote malicious AI agents or undermine the reputation of legitimate agents.
Integration Risks: Vulnerabilities or weaknesses in APIs or SDKs used to integrate AI agents with other systems, resulting in compromised interactions and wider security issues.
Horizontal/Vertical Solution Vulnerabilities: Exploiting weaknesses specific to industry or function-specific AI agent solutions, taking advantage of the unique design of vertical solutions.
Repudiation: AI agents denying actions they performed, creating accountability issues in the system due to the difficulty in tracing actions back to an AI agent.
Compromised Agent Registry: The agent registry, where agents are listed, is manipulated to inject malicious agent listings or modify details of legitimate agents, tricking the users of the ecosystem.
Malicious Agent Discovery: The agent discovery mechanism being manipulated to promote malicious AI agents or hide legitimate ones, thereby influencing the visibility of agents in the ecosystem.
Agent Pricing Model Manipulation: Attackers exploiting or manipulating AI agent pricing models to cause financial losses or gain an unfair advantage, manipulating the economic system of AI agents.
Inaccurate Agent Capability Description: Misleading or inaccurate capability descriptions for AI agents that lead to misuse, over-reliance, or unexpected and potentially harmful outcomes due to incorrect understanding of the AI.
Layer 6: Security and Compliance (Vertical Layer)
Description: This vertical layer cuts across all other layers, ensuring that security and compliance controls are integrated into all AI agent operations. This layer assumes that AI agents are also used as a security tool.
Threat Landscape:
Security Agent Data Poisoning: Attackers manipulating the training or operational data used by AI security agents, causing them to misidentify threats or generate false positives, impacting the AI security process.
Evasion of Security AI Agents: Malicious actors using adversarial techniques to bypass security AI agents, causing them to not detect or properly respond to threats.
Compromised Security AI Agents: Attackers gaining control over AI security agents, using them to perform malicious tasks or to disable security systems, directly impacting the AI security process.
Regulatory Non-Compliance by AI Security Agents: AI security agents operating in violation of privacy regulations or other compliance standards, due to misconfiguration or improper training, creating legal risks.
Bias in Security AI Agents: Biases in AI security agents that lead to unfair or discriminatory security practices, where certain systems are not adequately protected.
Lack of Explainability in Security AI Agents: The lack of transparency in security AI agent’s decision-making, causing difficulty in auditing actions or identifying the root cause of security failures.
Model Extraction of AI Security Agents: Attackers extracting the underlying model of an AI security agent, creating ways to bypass security systems by understanding how the system works.
Layer 5: Evaluation and Observability
Description: This layer focuses on how AI agents are evaluated and monitored, including tools and processes for tracking performance and detecting anomalies.
Threat Landscape:
Manipulation of Evaluation Metrics: Adversaries influencing benchmarks to favor their AI agents, through poisoned datasets or biased test cases, resulting in inaccurate performance data.
Compromised Observability Tools: Attackers injecting malicious code into monitoring systems that exfiltrate system data or hide malicious behaviour, compromising the integrity and security of the AI monitoring process.
Denial of Service on Evaluation Infrastructure: Disrupting the AI evaluation process to prevent proper testing and detection of compromised behavior, leading to a lack of visibility of AI agent performance.
Evasion of Detection: AI agents designed to avoid triggering alerts or being flagged by observability systems, using advanced techniques to disguise their true behaviour and avoiding security alerts.
Data Leakage through Observability: Sensitive AI information inadvertently exposed through logs or monitoring dashboards, due to misconfiguration, creating privacy and confidentiality risks.
Poisoning Observability Data: Manipulating the data fed into the observability system for AI systems, hiding incidents from security teams and masking malicious activity.
Layer 4: Deployment and Infrastructure
Description: This layer involves the infrastructure on which the AI agents run (e.g., cloud, on-premise).
Threat Landscape:
Compromised Container Images: Malicious code injected into AI agent containers that can infect production systems, compromising the AI deployment environment.
Orchestration Attacks: Exploiting vulnerabilities in systems like Kubernetes to gain unauthorized access and control over AI deployment systems, disrupting AI agent functionality.
Infrastructure-as-Code (IaC) Manipulation: Tampering with Terraform or CloudFormation scripts to provision compromised AI resources, leading to the creation of insecure deployment infrastructure for AI agents.
Denial of Service (DoS) Attacks: Overwhelming infrastructure resources supporting AI agents, causing the AI systems to become unavailable to legitimate users.
Resource Hijacking: Attackers using compromised AI infrastructure for cryptomining or other illicit purposes, leading to performance degradation of AI agents.
Lateral Movement: Attackers gaining access to one part of the AI infrastructure and then using that access to compromise other sensitive AI areas, compromising additional systems and data in the AI ecosystem.
Layer 3: Agent Frameworks
Description: This layer encompasses the frameworks used to build the AI agents, for example toolkits for conversational AI, or frameworks that integrate data.
Threat Landscape:
Compromised Framework Components: Malicious code in libraries or modules used by AI frameworks, compromising the functionality of the framework and leading to unexpected results.
* Backdoor Attacks: Hidden vulnerabilities or functionalities in the AI framework, exploited by attackers to gain unauthorized access and control over AI agents.
* Input Validation Attacks: Exploiting weaknesses in how the AI framework handles user inputs, allowing for code injection and potential system compromise of AI agent systems.
* Supply Chain Attacks: Targeting the AI framework’s dependencies, compromising software before delivery and distribution, resulting in compromised AI agent software.
* Denial of Service on Framework APIs: Disrupting the AI framework’s ability to function, overloading services and preventing normal operation for the AI agents.
* Framework Evasion: AI agents specifically designed to bypass security controls within the framework, using advanced techniques to perform unauthorized actions.
Layer 2: Data Operations
Description: This is where data is processed, prepared, and stored for the AI agents, including databases, vector stores, RAG (Retrieval Augmented Generation) pipelines, and more.
Threat Landscape:
Data Poisoning: Manipulating training data to compromise AI agent behavior, leading to biased results or unintended consequences in AI decision making.
Data Exfiltration: Stealing sensitive AI data stored in databases or data stores, exposing private and confidential information related to AI systems.
Model Inversion/Extraction: Reconstructing training data or stealing the AI model through API queries, leading to IP theft and data breaches specifically related to the AI model.
Denial of Service on Data Infrastructure: Disrupting access to data needed by AI agents, preventing agent functionality and interrupting normal operation of the AI systems.
Data Tampering: Modifying AI data in transit or at rest, leading to incorrect agent behavior or inaccurate results within AI systems.
Compromised RAG Pipelines: Injecting malicious code or data into AI data processing workflows, causing erroneous results or malicious AI agent behavior.
Layer 1: Foundation Models
Description: The core AI model on which an agent is built. This can be a large language model (LLM) or other forms of AI.
Threat Landscape:
Adversarial Examples: Inputs specifically crafted to fool the AI model into making incorrect predictions or behave in unexpected ways, causing instability or incorrect responses from the AI.
Model Stealing: Attackers extracting a copy of the AI model through API queries for use in a different application, resulting in IP theft or competitive disadvantage specifically related to AI.
Backdoor Attacks: Hidden triggers within the AI model that cause it to behave in a specific way when activated, usually malicious, leading to unpredictable and potentially harmful behavior from the AI model.
Membership Inference Attacks: Determining whether a specific data point was used to train the AI model, potentially revealing private information or violating confidentiality of the training data.
Data Poisoning (Training Phase): Injecting malicious data into the AI model's training set to compromise its behavior, resulting in skewed or biased model behavior in AI systems.
Reprogramming Attacks: Repurposing the AI model for a malicious task different from its original intent, manipulating the model for unexpected and harmful uses.


B. Cross-Layer Threats
These threats span multiple layers, exploiting interactions between them. For example an attacker might exploit a vulnerability in the container infrastructure (Layer 4) and gain access to a running AI agent instance. From here, they could then leverage this level of access to inject malicious data into the agent's data store (Layer 2), which would then poison the next model update, compromising the foundational model (Layer 1).

Supply Chain Attacks: Compromising a component in one layer (e.g., a library in Layer 3) to affect other layers (e.g., the Agent Ecosystem).
Lateral Movement: An attacker gaining access to one layer (e.g., Layer 4) and then using that access to compromise other layers (e.g., Data Operations).
Privilege Escalation: An agent or attacker gaining unauthorized privileges in one layer, and using it to access or manipulate others.
Data Leakage: Sensitive data from one layer being exposed through another layer.
Goal Misalignment Cascades: Goal misalignment in one agent (e.g., due to data poisoning in Layer 2) that can propagate to other agents through interactions in the Agent Ecosystem.


C. Mitigation Strategies
Layer-Specific Mitigations: Implement controls tailored to the specific threats of each layer (as listed above).
Cross-Layer Mitigations:
Defense in Depth: Implement multiple layers of security.
Secure Inter-Layer Communication: Use secure protocols for communication between layers.
System-Wide Monitoring: Monitor for anomalous behavior across all layers.
Incident Response Plan: Develop a plan for security incidents spanning multiple layers.
AI-Specific Mitigations:
Adversarial Training: Train agents to be robust against adversarial examples.
Formal Verification: Verify agent behavior and ensure goal alignment using formal methods and specification.
Explainable AI (XAI): Improve agent decision-making transparency to allow for auditing.
Red Teaming: Simulate attacks to find vulnerabilities.
Safety Monitoring: Implement runtime monitoring to detect unsafe agent behaviors.


D. Using MAESTRO: A Step-by-Step Approach
System Decomposition: Break down the system into components according to the seven-layer architecture. Define agent capabilities, goals, and interactions.
Layer-Specific Threat Modeling: Use layer-specific threat landscapes to identify threats. Tailor the identified threats to the specifics of your system.
Cross-Layer Threat Identification: Analyze interactions between layers to identify cross-layer threats. Consider how vulnerabilities in one layer could impact others.
Risk Assessment: Assess likelihood and impact of each threat using the risk measurement and risk matrix, prioritize threats based on the results.
Mitigation Planning: Develop a plan to address prioritized threats. Implement layer-specific, cross-layer, and AI-specific mitigations.
Implementation and Monitoring: Implement mitigations. Continuously monitor for new threats and update the threat model as the system evolves.


3. Agentic Architecture Patterns
This section provides some examples of agentic architecture patterns and the risks associated with them.

Single-Agent Pattern
Description: A single AI agent operating independently to achieve a goal.
Threat: Goal Manipulation
Example Threat Scenario: The AI agent has been designed to maximize some value, but the attacker can change this goal to minimize this value. This can lead to a harmful result from a seemingly harmless AI system.
Mitigation: Implement input validation, and limit access to the agent's internal parameters.

Multi-Agent Pattern
Description: Multiple AI agents working together through communication channels. Trust is usually established between agent identities.
Threats:
Communication Channel Attack: An attacker intercepts messages between AI agents.
Identity Attack: An attacker masquerades as a legitimate AI agent or creates fake identities.
Example Threat Scenario: An attacker injects malicious data into the communication channel, causing miscommunication between the AI agents and disrupting normal functionality.
Mitigations: Secure communication protocols, mutual authentication, and input validation.
Unconstrained Conversational Autonomy
Description: A conversational AI agent that can process and respond to a wide range of inputs without tight constraints.
Threat: Prompt Injection/Jailbreaking
Example Threat Scenario: An attacker crafts malicious prompts to bypass safety filters and elicit harmful outputs from the conversational AI.
Mitigations: Robust input validation, and safety filters designed specifically for the conversational AI use case.
Task-Oriented Agent Pattern
Description: An AI agent designed to perform a specific task, typically by making API calls to other systems.
Threat: Denial-of-Service (DoS) through Overload
Example Threat Scenario: An attacker floods the AI agent with requests, making it unavailable to legitimate users, preventing normal function of the AI system.
Mitigations: Rate limiting, and load balancing designed for API interactions.
Hierarchical Agent Pattern
Description: A system that has multiple layers of AI agents, with higher level agents controlling subordinate AI agents.
Threat: Compromise of a Higher-Level Agent to Control Subordinates
Example Threat Scenario: An attacker gains control of a higher level AI agent and can manipulate other subordinate AI agents to perform malicious tasks, affecting the entire hierarchy.
Mitigations: Secure communication between AI agents, strong access controls, and regular monitoring.
Distributed Agent Ecosystem
Description: A decentralized system of many AI agents working within a shared environment.
Threat: Sybil Attack through Agent Impersonation
Example Threat Scenario: An attacker creates fake AI agent identities to gain disproportionate influence within the ecosystem, manipulating market dynamics or consensus protocols.
Mitigations: Robust identity management, and reputation based systems to distinguish between legitimate and malicious AI agents.
Human-in-the-Loop Collaboration
Description: A system where AI agents interact with human users in an iterative workflow.
Threat: Manipulation of Human Input/Feedback to Skew Agent Behavior
Example Threat Scenario: An attacker manipulates human input to cause the AI agent to learn unwanted behaviors or bias, leading to biased and skewed AI behavior.
Mitigations: Input validation and strong audit trails for all user interactions with the AI systems.
Self-Learning and Adaptive Agents
Description: AI agents that can autonomously improve over time based on interactions with their environment.
Threat: Data Poisoning through Backdoor Trigger Injection
Example Threat Scenario: An attacker injects malicious data into the AI agent’s training set that contains a hidden trigger, which when activated can cause malicious behavior, affecting the learning process of the AI model.
Mitigations: Data sanitization, and strong validation of training data that are used by the AI systems.


Conclusion
MAESTRO emphasizes a holistic, multi-layered approach to security, acknowledging that protecting Agentic AI systems requires combining traditional cybersecurity, AI-specific controls, and ongoing monitoring. It's not a one time fix, but an iterative process. We encourage you to start using MAESTRO, contribute to its development, and share your findings, as we collectively work towards safer and more secure Agentic AI.