In the evolving landscape of AI cybersecurity, AI agents present both opportunities and unique challenges. It's crucial to understand the gaps in your cybersecurity program to effectively protect these advanced systems. This post will explore AI agent threats, relevant mitigations, and strategies to focus your AI security efforts.
What are AI Agents?
AI agents are advanced systems that utilize artificial intelligence to perform actions or make decisions, often with some degree of autonomy. They are designed to perceive their environment, process information, and take actions to achieve specific goals.
Understanding AI Agent Threat Modeling
The perspectives on AI risk management and the threat landscape for AI agents vary widely. Some believe existing cybersecurity programs are sufficient, while others express alarm about the inadequacy of current controls in an AI-driven world.
We want to be clear that the point of this article is NOT to say MAESTRO is not needed. We acknowledge there are gaps with STRIDE which is why we have to modify it for an AI agent use case. We are using the widely-adopted STRIDE framework to illustrate how much of a gap MAESTRO is actually filling. Use what works best for your needs.
What is MAESTRO?
MAESTRO (Multi-Agentic System Threat Model) is a 7-layer reference architecture for agentic AI developed by the Cloud Security Alliance. This framework is designed to provide a structured approach to threat modeling for AI agents, addressing the limitations of traditional methods like STRIDE.
Why Does MAESTRO Exist?
Current threat modeling frameworks, like STRIDE, are not well-suited for AI agents. STRIDE, while a good starting point, doesn't fully address the unique challenges posed by AI agents, such as adversarial attacks and risks associated with unpredictable learning and decision-making. MAESTRO provides a more tailored approach to threat modeling in this context.
A Modified STRIDE + ML
To better understand the gaps that AI creates in a cybersecurity program, let's modify STRIDE so it can handle the unique challenges of AI agents. This is done by incorporating two new threat categories "Misunderstanding" and "Lack of Accountability" (ML), which can be employed.
- Misunderstanding: This refers to models having undesirable assessments due to a lack of context or malicious intervention, leading to unexpected emerging behaviors.
- Lack of Accountability: This occurs when actions are performed without clear governance or ownership, making it difficult to determine responsibility when issues arise.
Applying STRIDE + ML to AI Agents
Frameworks such as the OWASP Multi Agentic Threat Modeling Guide and the Cloud Security Alliance’ MAESTRO framework can be mapped into STRIDE + ML to provide a clearer view of AI agent threats. This mapping reveals that a significant portion of AI agent threats can be categorized using traditional STRIDE, but a notable percentage require the additional ML categories.
AI Agent Threats and Mitigations
AI agent threats can be categorized, and mitigations can be mapped to these categories. Some threats can be mitigated using existing cybersecurity measures, while others require extending capabilities or implementing new mitigations.
We use the OWASP AI Agent threat taxonomy as it is more concise compared to the Cloud Security Alliance taxonomy. Almost all of the threats in the Cloud Security Alliance threat taxonomy can be categorized into the OWASP taxonomy.
- Threats with Existing Mitigations:
- Spoofing (T9 – Identity Spoofing)
- Repudiation (T8 – Repudiation and Untraceability)
- Information Disclosure (T12 – Agent Communication Poisoning)
- Denial of Service (T4 – Resource Overload)
- Elevation of Privilege (T3 – Privilege Compromise)
- Threats Requiring Expanded Mitigations: This category includes
- Spoofing (T13 – Rogue Agents)
- Tampering (T11 - Unexpected RCE and Code Attacks, T1 – Memory Poisoning)
- Denial of Service (T10 – Overwhelming HITL)
- Elevation of Privilege (T14 – Human attacks on MAS)
- Threats Requiring New Mitigations: These are unique to AI agents
- Misunderstanding (T2 – Tool Misuse, T5 - Cascading Hallucinations, T6 – Intent Breaking & Goal Manipulation)
- Lack of Accountability (T7 – Misaligned & Deceptive Behaviour, T15 – Human Trust Manipulation)
Mitigation Options and AI-Specific Considerations
The Cloud Security Alliance provides a good list of mitigations to focus on. Many of the mitigations should be part of any robust cybersecurity program regardless if AI is used or not. Below are the additional mitigations required specifically for AI systems.
Conclusion
Focusing Your Efforts
The AI agent space is not as unique as many portray it to be, but we also cannot pretend that our existing cybersecurity control strategy is sufficient.
To effectively improve the ROI of your AI agent security efforts, focus on:
- First look at your current capabilities to see how you can address ~⅔ of the AI threat space.
- Look at the market for the emergent ~⅓ of AI threats, as these mitigations are being built now, so unlikely to exist with your current capabilities.
By understanding the nuances of AI agent threats and applying targeted mitigations, organizations can better protect these systems and maximize their return on investment in AI technologies.
See how Aiceberg provides solutions for 3 of the 5 AI specific mitigations called out by CSA:
- Formal Verification
- Explainable AI (XAI)
- Safety Monitoring
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