Agentic AI Threat Modeling Framework: MAESTRO

Written by Ken Huang, CEO of DistributedApps.ai, CSA Fellow, Co-Chair of CSA AI Safety Working Groups.

This blog post presents MAESTRO (Multi-Agent Environment, Security, Threat, Risk, and Outcome), a novel threat modeling framework designed specifically for the unique challenges of Agentic AI. If you are a security engineer, AI researcher, or developer working with these advanced systems, MAESTRO is designed for you. You'll use it to proactively identify, assess, and mitigate risks across the entire AI lifecycle, enabling you to build robust, secure, and trustworthy systems.

This framework moves beyond traditional methods that don't always capture the complexities of AI agents, offering a structured, layer-by-layer approach. It emphasizes understanding the vulnerabilities within each layer of an agent's architecture, how these layers interact, and the evolving nature of AI threats. By using MAESTRO, you'll be empowered to deploy AI agents responsibly and effectively.

1. Existing Threat Modeling Frameworks: A High-Level Comparison

Let's start with a simplified overview of some popular threat modeling frameworks and their core focus. This table will give us a quick snapshot before we dive into detailed comparisons.

Framework	Core Focus
STRIDE	General Security
PASTA	Risk-Centric
LINDDUN	Privacy
OCTAVE	Organizational Risk
Trike	System Modeling
VAST	Agile Development

2. Detailed Comparison of Existing Frameworks for Agentic AI

Now, let's explore each of these frameworks in detail and see where they excel and, more importantly, where they fall short when applied to the unique challenges of Agentic AI.

2.1 STRIDE: Strengths and Weaknesses for Agentic AI

• Overview: STRIDE, developed by Microsoft, categorizes threats into Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. It’s a classic approach to threat modeling, and its core categories are still relevant in modern applications.
• Strengths: STRIDE provides a solid foundation for identifying common security vulnerabilities relevant to AI agents, such as data tampering or denial-of-service attacks. It's relatively easy to understand and apply, making it a good starting point for many projects.
• Weaknesses: While STRIDE is a good starting point, it doesn't address the unique challenges of AI agents, such as adversarial attacks, or risks due to their unpredictable learning and decision-making processes.
• Specific AI Gaps: STRIDE lacks the necessary scope to address threats unique to AI, such as adversarial machine learning, data poisoning, and struggles to model the dynamic, autonomous behaviors of AI agents. It does not explicitly consider the impact of multiple AI agents interacting within an ecosystem.
• Applicability: STRIDE can be used as a starting point, but it needs significant augmentation to be effective for Agentic AI, requiring added AI-specific categories or adapting existing ones to reflect new risks.

2.2 PASTA: Strengths and Weaknesses for Agentic AI

• Overview: PASTA (Process for Attack Simulation and Threat Analysis) is a risk-centric methodology emphasizing understanding the attacker’s perspective. It involves a seven-stage process: Define Objectives, Define Technical Scope, Application Decomposition, Threat Analysis, Vulnerability and Weakness Analysis, Attack Modeling, and Risk and Impact Analysis.
• Strengths: PASTA's risk-based approach is valuable for prioritizing threats based on their potential business or mission impact. It encourages analysis of attacker motivation, leading to more comprehensive threat models.
• Weaknesses: PASTA is more complex and resource-intensive than some other frameworks. Like STRIDE, it lacks detailed guidance on AI-specific vulnerabilities. Additionally, it is a highly involved process that may not be flexible enough for modern development methodologies.
• Specific AI Gaps: While PASTA excels at risk analysis, it doesn't specifically focus on AI vulnerabilities like adversarial attacks, model extraction, or the complexities of autonomous decision-making. It requires extensions to handle unique AI agent risks, such as those related to data poisoning, or the impact of a manipulated goal.
• Applicability: PASTA's risk-centric approach is valuable, but it needs careful adaptation to model the specific behavior of AI agents and their associated risks. This might include new risk categories that focus specifically on adversarial ML techniques or the unintended consequences of AI autonomy.

2.3 LINDDUN: Strengths and Weaknesses for Agentic AI

• Overview: LINDDUN focuses specifically on privacy threats, categorizing them as Linkability, Identifiability, Non-repudiation, Detectability, Disclosure of information, Unawareness, and Non-compliance.
• Strengths: LINDDUN’s strong focus on privacy is crucial, as AI agents often process personal data, leading to significant privacy implications. It provides a systematic way to identify and analyze privacy risks.
• Weaknesses: LINDDUN is primarily focused on privacy, neglecting other critical security threats, and it only partially considers how independent AI decision-making impacts privacy.
• Specific AI Gaps: LINDDUN’s narrow scope means it does not cover other essential security threats in Agentic AI, such as data poisoning, denial-of-service attacks on AI, or model extraction. It does not model the unique risks arising from the autonomy of AI.
• Applicability: LINDDUN is essential for addressing privacy concerns in Agentic AI, but it must be paired with other frameworks to create a comprehensive threat model. To make it more effective, you might consider adding categories that address privacy risks specific to machine learning, like membership inference attacks or the use of differential privacy to protect training data.

2.4 OCTAVE: Strengths and Weaknesses for Agentic AI

• Overview: OCTAVE (Operationally Critical Threat, Asset, and Vulnerability Evaluation) is a risk management framework that focuses on organizational risks. It uses a three-phase process of building asset-based threat profiles, identifying infrastructure vulnerabilities, and developing security strategies and plans.
• Strengths: OCTAVE helps align security efforts with the organization's overall risk management strategy and also emphasizes the identification of critical assets, which can include AI agents and the data they rely on.
• Weaknesses: Its high-level approach lacks the specific detail needed for modeling the unique threats and vulnerabilities of AI agents. It also requires a well-structured risk management system to function effectively.
• Specific AI Gaps: OCTAVE’s broad focus on organizational risk means it lacks the necessary detail to address AI-specific challenges such as adversarial examples or data poisoning. It will need to be extended with categories that consider the unique risks of AI agents, such as the impact of malicious training data or attacks on the learning process.
• Applicability: OCTAVE is useful for establishing a high-level risk management framework for AI, but it needs significant modifications to address the nuances of Agentic AI. This could be achieved by adding a layer of risk analysis specifically targeted at AI threats.

2.5 Trike: Strengths and Weaknesses for Agentic AI

• Overview: Trike uses a "requirements model" to identify stakeholders, assets, and allowed actions, then combines it with an "implementation model" and data flow diagrams to identify threats and assign risks.
• Strengths: It provides a structured way to model the system and its components, and it integrates a risk assessment process to prioritize threats, making it a more complete method than some.
• Weaknesses: Trike is fairly complex to implement, especially for large and complex systems, and, similar to the others, it lacks a specific focus on threats unique to AI agents.
• Specific AI Gaps: While good for modeling the environment in which AI operates, Trike does not address the internal agent vulnerabilities such as adversarial inputs, data poisoning, or the emergent behaviors that come with learning and autonomy.
• Applicability: Trike can be used to model the overall environment that the agent operates within, but needs substantial modifications to effectively include the agents internal workings and risks. This could include detailed modeling of AI data flows or AI model parameter flows within the system.

2.6 VAST: Strengths and Weaknesses for Agentic AI

• Overview: VAST (Visual, Agile, and Simple Threat Modeling) emphasizes automation and integrates directly with development workflows, using process flow diagrams to model applications and identify threats.
• Strengths: It aligns well with iterative AI development cycles with its emphasis on agile techniques, and its ability to integrate with development tools is useful for continuous security monitoring.
• Weaknesses: Its simplicity can limit it for modeling the complex interactions of AI agents, and it also lacks specific guidance on AI-specific threats and vulnerabilities.
• Specific AI Gaps: VAST’s focus on agile and iterative development does not adequately model the specific risks of AI such as adversarial machine learning, or emergent properties of autonomous agents. It also needs to be more suited to handling the non-deterministic nature of many AI agents.
• Applicability: VAST's strengths in automation can be leveraged, but it will need substantial customization to address the nuances of Agentic AI. This would involve specific extensions to address the intricacies of AI threats, and to integrate specific AI security tooling.

2.7. Gaps in Existing Frameworks for Agentic AI: What's Missing?

Existing frameworks, while useful in many areas, leave significant gaps for Agentic AI. They don't adequately address the unique challenges arising from the autonomy, learning, and interactive nature of these systems.

Autonomy-Related Gaps:

• Agent Unpredictability: Traditional frameworks struggle to model the unpredictable actions of autonomous agents. It's difficult to anticipate threats arising from their independent decision-making.
• Goal Misalignment: Frameworks often don't cover threats related to an agent's goals becoming misaligned with the intended purpose. This can lead to harmful unintended consequences. For example, an AI stock trading agent corrupted might maximize losses instead of gains.

Machine Learning-Specific Gaps:

• Adversarial Machine Learning: Frameworks lack specific guidance on attacks targeting machine learning models.
  • Data Poisoning: Manipulating training data to corrupt the agent's behavior. For example, injecting malicious data into a self-driving car's training set to make it misidentify stop signs.
  • Evasion Attacks: Crafting inputs designed to fool an agent. Imagine an image recognition system fooled by adversarial stickers placed on stop signs.
  • Model Extraction: Stealing an agent's underlying model through API queries. An attacker might want to get a copy of a valuable commercial AI model for their own use.
• Lack of Robustness: Frameworks fail to capture the lack of robustness of AI against unexpected or malformed inputs, which could cause unpredictable and potentially harmful behavior

Interaction-Based Gaps:

• Agent-to-Agent Interactions: Dynamic interactions between multiple agents are not well-addressed, including risks like:
  • Collusion: Agents secretly coordinating to achieve malicious goals. For example, multiple AI's working in a market to manipulate prices.
  • Competition: Agents exploiting each other’s weaknesses in a competitive environment, where agents competing to optimize for resources inadvertently create a harmful outcome.

System-Level Gaps:

• Explainability and Auditability: The lack of transparency in complex AI makes it difficult to find the root cause of incidents or audit an agent's behavior for compliance.
• Supply Chain Security: AI agents rely on external components, datasets, and models. Frameworks must address risks from:
  • Compromised pre-trained models.
  • Vulnerabilities in ML libraries.
  • Lack of provenance tracking for training data.

3. A Proposed Threat Modeling Framework: MAESTRO

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

3.1 MAESTRO’s 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.

3.2 MAESTRO’s Elements

MAESTRO is built around a seven-layer reference architecture described by Ken Huang, allowing us to understand and address risks at a granular level.

Please see more details about the seven-layer reference architecture here.

Figure 1 gives a mindmap of the reference architecture.

Figure 1: 7 Layer Reference Architecture for Agentic AI

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

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

4. 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.

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About the Author

Ken Huang is a prolific author and renowned expert in AI and Web3, with numerous published books spanning AI and Web3 business and technical guides and cutting-edge research. As Co-Chair of the AI Safety Working Groups at the Cloud Security Alliance, and Co-Chair of AI STR Working Group at World Digital Technology Academy under UN Framework, he's at the forefront of shaping AI governance and security standards.

Huang also serves as CEO and Chief AI Officer(CAIO) of DistributedApps.ai, specializing in Generative AI related training and consulting. His expertise is further showcased in his role as a core contributor to OWASP's Top 10 Risks for LLM Applications and his active involvement in the NIST Generative AI Public Working Group.

Key Books:

• "Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow" (Springer, 2023) - Strategic insights on AI and Web3's business impact.
• "Generative AI Security: Theories and Practices" (Springer, 2024) - A comprehensive guide on securing generative AI systems
• "Practical Guide for AI Engineers" (Volumes 1 and 2 by DistributedApps.ai, 2024) - Essential resources for AI and ML Engineers
• "The Handbook for Chief AI Officers: Leading the AI Revolution in Business" (DistributedApps.ai, 2024) - Practical guide for CAIO in small or big organizations.
• "Web3: Blockchain, the New Economy, and the Self-Sovereign Internet" (Cambridge University Press, 2024) - Examining the convergence of AI, blockchain, IoT, and emerging technologies

His co-authored book on "Blockchain and Web3: Building the Cryptocurrency, Privacy, and Security Foundations of the Metaverse" (Wiley, 2023) has been recognized as a must-read by TechTarget in both 2023 and 2024.

A globally sought-after speaker, Ken has presented at prestigious events including Davos WEF, ACM, IEEE, CSA AI Summit, IEEE, ACM, Depository Trust & Clearing Corporation, and World Bank conferences.

Recently, Ken Huang became a member of OpenAI Forum to help advance its mission to foster collaboration and discussion among domain experts and students regarding the development and implications of AI.

Explore Ken's books on Amazon.