MAESTRO AI Security Audit Platform

MAESTRO Framework Components

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Multi-Agent

Analyze interactions between AI agents, humans, and systems in complex environments

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Environment

Assess deployment contexts, infrastructure, and operational boundaries

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Security

Evaluate authentication, authorization, encryption, and access controls

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Threat

Identify adversarial attacks, prompt injection, model poisoning

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Risk

Quantify likelihood and impact of identified threats

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Outcome

Define security objectives and success metrics

OWASP AI Top 10 Vulnerabilities

LLM01: Prompt Injection

Malicious inputs that manipulate LLM behavior and bypass safeguards

LLM02: Insecure Output Handling

Insufficient validation of LLM outputs leading to XSS, CSRF, SSRF, and code execution

LLM03: Training Data Poisoning

Manipulation of training data to introduce vulnerabilities or biases

LLM04: Model Denial of Service

Resource-heavy operations causing service degradation or high costs

LLM05: Supply Chain Vulnerabilities

Compromised components, data sets, or third-party dependencies

LLM06: Sensitive Information Disclosure

Leakage of confidential data through model outputs

LLM07: Insecure Plugin Design

Inadequate access control and validation in LLM plugins

LLM08: Excessive Agency

LLM systems with too much autonomy or permissions

LLM09: Overreliance

Excessive dependence on LLM outputs without oversight

LLM10: Model Theft

Unauthorized access to proprietary models via extraction or replication

MITRE ATLAS Threat Tactics

🎯 Reconnaissance

Gather information about ML systems and capabilities

• Discover ML artifacts
• Identify ML model architecture

🔓 Resource Development

Establish resources for attacking ML systems

• Acquire ML attack capabilities
• Develop adversarial data

🚪 Initial Access

Gain entry into ML pipeline or system

• Supply chain compromise
• Exploit public-facing applications

⚙️ ML Model Access

Obtain ML model for analysis or theft

• Model extraction via API
• Physical access to model

🔬 Execution

Run malicious code in ML environment

• Command and scripting interpreter
• User execution

⏱️ Persistence

Maintain presence in ML system

• Poison training data
• Backdoor ML model

🎭 Defense Evasion

Avoid detection during attack

• Evade ML model detection
• Obfuscate adversarial data

💥 Impact

Manipulate, disrupt, or destroy ML systems

• Erode ML model integrity
• Denial of ML service

🛡️ MAESTRO AI Security Audit Platform

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