AI Security Framework

Enterprise AI Penetration Testing & Security Assessment

Professional-grade security assessment tools built with cutting-edge technology for comprehensive AI system evaluation and penetration testing.

MITRE ATLAS OWASP LLM ML Security

Rigorous testing methodologies and quality assurance processes ensuring the highest standards in AI security assessment and vulnerability detection.

Validated Testing Risk Analysis Compliance

Backed by experienced cybersecurity experts specializing in artificial intelligence, machine learning security, and advanced threat detection methodologies.

Expert Team Consulting Support

Explore Framework

Framework Overview

A production-ready methodology for assessing AI system security across the entire lifecycle

Data Security

Protect training data, inference inputs, and model outputs from poisoning, manipulation, and unauthorized access.

Model Security

Secure model architecture, prevent extraction, and defend against adversarial attacks and model inversion.

Deployment Security

Ensure secure deployment practices, API protection, and runtime monitoring for AI systems.

Supply Chain Security

Audit AI dependencies, model repositories, and third-party components for vulnerabilities.

AI Governance

Establish policies, compliance frameworks, and ethical guidelines for AI system security, including Shadow AI detection and management.

Incident Response

Develop response procedures for AI security incidents, model failures, and data breaches.

AI Security Controls

Specialized security framework combining traditional controls with AI-specific measures and organizational governance

Preventive Controls

Input validation, prompt filtering, model access controls, training data sanitization

Management Operational Technical

Detective Controls

AI behavior monitoring, prompt injection detection, model drift analysis, adversarial attack identification

Operational • Technical

Corrective Controls

Model retraining, bias correction, security patches, incident remediation

Management Operational Technical

Deterrent Controls

Usage monitoring, audit trails, legal agreements, AI ethics policies, threat intelligence sharing

Management Operational

MITRE ATLAS Integration

Framework Overview: MITRE ATLAS provides a comprehensive matrix of adversarial tactics and techniques against AI systems

Key Tactics:

Reconnaissance: Gathering information about AI systems
Resource Development: Preparing attack infrastructure
Initial Access: Gaining entry to AI systems
Execution: Running malicious code or commands
Persistence: Maintaining access to compromised systems

Assessment Integration:

Map vulnerabilities to ATLAS techniques
Prioritize testing based on threat landscape
Develop countermeasures for identified tactics

OWASP LLM Top 10 (2025) Security Vulnerabilities

Critical security risks specifically identified for Large Language Model applications

LLM01 CRITICAL

Prompt Injection

User prompts alter LLM behavior through direct and indirect injection attacks

2025 Edition Critical Risk

LLM02 HIGH

Sensitive Information Disclosure

LLM applications expose sensitive data through various leakage vectors

2025 Edition High Impact

LLM03 CRITICAL

Supply Chain

LLM supply chains susceptible to various security vulnerabilities

2025 Edition Critical Risk

LLM04 HIGH

Data and Model Poisoning

Pre-training, fine-tuning, or embedding data manipulation

2025 Edition High Risk

LLM05 HIGH

Improper Output Handling

Insufficient validation, sanitization and handling of LLM outputs

2025 Edition High Risk

LLM06 HIGH

Excessive Agency

LLM systems granted excessive autonomy and permissions

2025 Edition High Risk

LLM07 MEDIUM

System Prompt Leakage

System prompts and instructions exposed to attackers

2025 Edition Medium Risk

LLM08 HIGH

Vector and Embedding Weaknesses

Vulnerabilities in vector databases and embedding systems

2025 Edition High Risk

LLM09 MEDIUM

Misinformation

LLMs produce false or misleading information

2025 Edition Medium Risk

LLM10 MEDIUM

Unbounded Consumption

Uncontrolled resource consumption leading to service disruption

2025 Edition Medium Risk

Security Assessment Methodology

Take a guided journey through AI security assessment methodology

Click on each step to explore detailed procedures, tools, and best practices

Scope Definition

Define assessment boundaries and objectives

Start

Asset Discovery

Identify and catalog AI system components

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Threat Modeling

Map potential attack vectors and threats

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Vulnerability Assessment

Scan for security weaknesses and misconfigurations

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Penetration Testing

Simulate real-world attacks and exploits

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Reporting

Document findings and provide recommendations

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AI Security Risk Matrix

Advanced risk assessment tool for AI systems with comprehensive scoring capabilities

Quantitative Scoring

Risk = Likelihood × Impact. Scores range from 1-25 providing precise risk quantification for AI systems.

Threat-Focused Assessment

Incorporates MITRE ATLAS and OWASP LLM Top 10 frameworks for comprehensive AI threat coverage.

Enterprise Standards

Aligned with NIST AI RMF, ISO/IEC 23053, and enterprise risk management best practices.

Impact →
Likelihood ↓

Very Low
(1)

Low
(2)

Medium
(3)

High
(4)

Very High
(5)

Very High (5)

High (4)

Medium (3)

Low (2)

Very Low (1)

Risk Level Key:

Critical (20-25)

High (15-19)

Medium (10-14)

Low (5-9)

Very Low (1-4)

Hover over risk levels to highlight corresponding cells. Click any cell for detailed risk assessment information.

AI Risk Calculator

Calculate AI security risk using comprehensive threat modeling with likelihood, impact, and asset criticality factors.

Threat Type:

Select the primary threat vector targeting your AI system

Asset Criticality:

How critical is this AI system to your business operations?

Attack Likelihood:

Probability of successful attack within 12 months

Business Impact:

Total business impact including downtime, data loss, reputation

AI Security Risk Scenarios

Adversarial Attacks

Carefully crafted inputs designed to fool AI models

Critical Risk

Data Poisoning

Malicious data injection during training or inference

High Risk

Model Extraction

Unauthorized copying or reverse engineering of AI models

High Risk

Prompt Injection

Manipulating LLM behavior through crafted prompts

Medium Risk

Privacy Leakage

Unintended exposure of sensitive training data

High Risk

Bias Exploitation

Leveraging algorithmic bias for malicious purposes

Medium Risk

AI Security Architecture

Interactive visual representation of AI security framework architecture with component details

Interactive Security Architecture

Explore AI system components and their security implications through interactive visualization

Click Components

View detailed security analysis

Hover Layers

Highlight related elements

Security Focus

Identify attack surfaces

User Interface Layer

External access points and client applications

Web Application

Mobile App

API Client

Application Layer

Business logic, input/output handling, and plugin management

Agent/Plugin Management

Input Handling

Output Handling

Model Layer

AI model storage, serving, training, and evaluation infrastructure

Model Storage Infrastructure

Model Serving Infrastructure

Training & Tuning

Model Frameworks & Code

Evaluation

Infrastructure Layer

Data storage, processing, and filtering systems

Data Storage Infrastructure

Training Data

Data Filtering & Processing

Data Sources

External data providers and input sources

External Sources

AI Security Methodology Guide

Framework Overview

Industry-leading AI security assessment methodology integrating MITRE ATLAS, OWASP LLM Top 10, and industry best practices.

Assessment Checklists

Detailed checklists for each assessment phase with MITRE ATLAS technique mapping and validation criteria.

Risk Assessment

Risk scoring algorithms and matrices aligned with ATLAS threat landscape and business impact analysis.

What's Included:

• Complete Assessment Framework

• Threat Modeling Templates

• Security Checklists

• Risk Assessment Matrix

• Testing Procedures

• Reporting Templates

Document Sections:

Phase 1: Preparation and Scoping

Phase 2: Asset Identification

Phase 3: Threat Modeling

Phase 4: Vulnerability Assessment

Phase 5: Penetration Testing

Phase 6: Reporting & Remediation

Framework Integration:

MITRE ATLAS

OWASP LLM Top 10

NIST AI RMF

CSA AI Controls

Perfect For:

• Security professionals conducting AI assessments

• AI/ML engineers implementing security best practices

• Compliance teams ensuring regulatory adherence

• Organizations building secure AI systems

Pro Tip: This comprehensive guide provides everything you need to establish a robust AI security assessment program in your organization.

Download Full Guide (PDF)

AI Security Assessment Playbook Example

Target System: Large Language Model API Service

Scope: Cloud-based LLM service with REST API endpoints, user authentication, and data processing capabilities

Timeline: 2-week comprehensive security assessment

Team: 3 security specialists (AI Security, Network Security, Application Security)

Phase 1: Reconnaissance (Days 1-2)

OSINT Collection: Public API documentation, GitHub repositories, tech stack identification
Attack Surface Mapping: Identify all endpoints, authentication mechanisms, data flows
Threat Modeling: Apply MITRE ATLAS framework to identify potential attack vectors

Tools: Nmap, Burp Suite, Custom OSINT scripts

Phase 2: Input Validation Testing (Days 3-5)

Prompt Injection: Test for direct and indirect prompt injection vulnerabilities
Input Sanitization: Evaluate filtering and validation mechanisms
Context Manipulation: Assess system prompt protection and context boundaries

Tools: Custom prompt injection payloads, Automated fuzzing tools

Phase 3: Data Security Assessment (Days 6-8)

Training Data Inference: Attempt to extract training data through model queries
PII Leakage: Test for personal information exposure in model responses
Data Storage Security: Evaluate encryption, access controls, and backup security

Tools: Model inversion scripts, PII detection tools, Database security scanners

Phase 4: Model Security Testing (Days 9-11)

Model Extraction: Attempt to reverse-engineer model architecture and parameters
Adversarial Attacks: Generate adversarial inputs to test model robustness
Bias and Fairness: Evaluate model outputs for discriminatory patterns

Tools: Adversarial ML libraries, Model extraction frameworks, Bias detection tools

Phase 5: Infrastructure Security (Days 12-13)

API Security: Authentication bypass, rate limiting, and authorization flaws
Container Security: Docker/Kubernetes misconfigurations and vulnerabilities
Network Security: TLS configuration, certificate validation, network segmentation

Tools: SSLyze, Docker security scanners, Kubernetes security tools

Phase 6: Reporting & Remediation (Day 14)

Vulnerability Classification: CVSS scoring, MITRE ATLAS TTPs mapping
Risk Assessment: Business impact analysis and remediation prioritization
Recommendations: Specific technical and procedural security improvements

Deliverables: Executive summary, Technical report, Remediation roadmap

Key Findings Example

Critical: Prompt Injection Vulnerability (CVSS 9.1)

Description: System prompt can be overridden through indirect injection via user-uploaded documents, allowing attackers to manipulate AI behavior and extract sensitive information.

Recommendation: Implement robust input sanitization, context isolation, and output filtering mechanisms.

High: Training Data Inference (CVSS 7.8)

Description: Model responses contain verbatim training data, potentially exposing proprietary information and personal data.

Recommendation: Apply differential privacy techniques and implement data anonymization in training pipelines.

Medium: Insufficient Rate Limiting (CVSS 5.3)

Description: API endpoints lack proper rate limiting, enabling resource exhaustion and potential DoS attacks.

Recommendation: Implement tiered rate limiting based on user authentication levels and request complexity.

This playbook follows industry standards including MITRE ATLAS, OWASP LLM Top 10, and NIST AI RMF guidelines.

AI Security Testing Tools

MITRE ATLAS Integration

Our testing tools implement MITRE ATLAS techniques and tactics for comprehensive AI security assessment with automated SAST/DAST capabilities.

Available Tools:

AI Model Security Scanner

Automated vulnerability assessment for ML models including adversarial robustness testing and model extraction detection.

Adversarial Testing Model Extraction Privacy Attacks

Data Pipeline Security Analyzer

Comprehensive analysis of data ingestion, processing, and storage security with poisoning attack detection.

Data Poisoning Input Validation Pipeline Security

LLM Security Tester (2025)

Specialized testing for Large Language Models with OWASP LLM Top 10 2025 vulnerabilities including prompt injection, system prompt leakage, and misinformation detection.

Prompt Injection System Prompt Leakage Misinformation

API Security Scanner

Automated API security testing for AI service endpoints including authentication bypass and injection attacks.

Auth Testing Injection Tests Rate Limiting

AI Red Teaming Platform:

Team Red Teaming Operations

Collaborative red team exercises with real-time assessment and case study generation

MITRE ATLAS Technique Coverage:

Initial Access

ML Supply Chain Compromise

Execution

Serverless Execution

Defense Evasion

Adversarial Perturbations

Discovery

ML Artifact Enumeration

Visit GitHub Repository

Note: These tools are designed for authorized security testing only. Ensure proper authorization before testing any AI systems.

Interactive Architecture Diagram

Professional UML-Style Architecture

This interactive diagram presents a comprehensive 5-layer AI security architecture with enterprise-grade design and professional UML styling, similar to Google Cloud and AWS architecture diagrams.

Multi-Layer Architecture

Five distinct architectural layers: User Interface, Application, AI Model, Infrastructure, and Data Sources - each with color-coded components and clear boundaries.

Interactive Security Tour

SAIF-inspired interactive tour showcasing 5 critical AI security risks with introduction/exposure/mitigation indicators across all architectural layers.

Risk Analysis System

Visual risk indicators showing where threats are introduced, exposed, and mitigated throughout the system architecture with color-coded severity levels.

Architecture Layers Overview:

User Interface Layer

Web applications, mobile apps, and API clients - external access points and user interaction components.

Application Layer

Business logic, agent/plugin management, input/output handling, and application processing components.

AI Model Layer

Model storage, serving infrastructure, training/tuning systems, frameworks, and evaluation components.

Infrastructure Layer

Data storage infrastructure, training data management, and data filtering/processing systems.

Data Sources

External data providers, APIs, databases, and various input sources feeding the AI system.

Interactive Security Risk Tour:

5 Critical AI Security Risks

Data Poisoning

Malicious data injection attacks

Model Extraction

Unauthorized model copying

Adversarial Attacks

Input manipulation attacks

Prompt Injection

LLM behavior manipulation

Privacy Leakage

Sensitive data exposure

Interactive Features

Risk Tour Navigation

Click "Start Risk Tour" to begin guided exploration of security vulnerabilities across all architectural layers.

Layer Highlighting

Interactive layer highlighting shows risk introduction, exposure, and mitigation points with visual indicators.

Professional Design

Production-grade UML styling with clean typography, color-coded components, and modern gradient effects.

Responsive Layout

Fully responsive design that adapts to different screen sizes while maintaining professional appearance.

This professional UML-style architecture diagram provides comprehensive visualization of AI security considerations across all system layers.

AI Governance & Policy Framework

Organizational AI Governance

Establish organization-wide policies, compliance frameworks, and ethical guidelines for AI system security across the organization, including detection and management of unsanctioned AI usage.

Policy Framework

AI usage and development policies
Data governance and privacy protection
Ethical AI principles and guidelines
Risk management frameworks
Incident response procedures

Compliance & Oversight

GDPR and privacy regulation compliance
Industry-specific regulatory requirements
AI model validation and testing
Audit trails and documentation
Third-party vendor assessments

Shadow AI Management

Detection of unsanctioned AI tools
Risk assessment of unauthorized usage
Employee training and awareness
Approved AI catalog maintenance
Monitoring and enforcement controls

What is Shadow AI?

Shadow AI refers to the unsanctioned use of artificial intelligence tools or applications by employees or end users without the formal approval or oversight of the information technology (IT) department.

This unauthorized usage creates significant security, compliance, and governance risks that organizations must proactively identify and manage through strategic governance frameworks.

Implementation Strategy:

Discovery & Assessment

Network traffic analysis for AI services
Application inventory and risk assessment
User surveys and usage patterns

Policy & Controls

AI usage policies and procedures
Approved tool catalog maintenance
Training programs and awareness

Monitoring & Enforcement

Continuous compliance monitoring
Automated detection systems
Incident response and audits

Effective AI governance requires balanced policy enforcement, user education, and technological controls.

AI Security Framework

Framework Overview

Data Security

Model Security

Deployment Security

Supply Chain Security

AI Governance

Incident Response

Attack Surface Mapping

Data LayerAttacks

Model LayerAttacks

DeploymentAttacks

InfrastructureAttacks

AI Security Controls

Preventive Controls

Detective Controls

Corrective Controls

Deterrent Controls

MITRE ATLAS Integration

Key Tactics:

Assessment Integration:

OWASP LLM Top 10 (2025) Security Vulnerabilities

Prompt Injection

Sensitive Information Disclosure

Supply Chain

Data and Model Poisoning

Improper Output Handling

Excessive Agency

System Prompt Leakage

Vector and Embedding Weaknesses

Misinformation

Unbounded Consumption

Security Assessment Methodology

AI Security Risk Matrix

Quantitative Scoring

Threat-Focused Assessment

Enterprise Standards

Risk Level Key:

AI Risk Calculator

Recommended Actions:

AI Security Risk Scenarios

Adversarial Attacks

Data Poisoning

Model Extraction

Prompt Injection

Privacy Leakage

Bias Exploitation

AI Security Architecture

Interactive Security Architecture

Click Components

Hover Layers

Security Focus

AI Security Assessment Checklist

Framework Resources

Methodology Guide

Testing Tools & AI Red Teaming

AI Security Methodology Guide

Framework Overview

Assessment Checklists

Risk Assessment

What's Included:

Document Sections:

Framework Integration:

Perfect For:

AI Security Assessment Playbook Example

Target System: Large Language Model API Service

Phase 1: Reconnaissance (Days 1-2)

Phase 2: Input Validation Testing (Days 3-5)

Phase 3: Data Security Assessment (Days 6-8)

Phase 4: Model Security Testing (Days 9-11)

Phase 5: Infrastructure Security (Days 12-13)

Phase 6: Reporting & Remediation (Day 14)

Key Findings Example

Critical: Prompt Injection Vulnerability (CVSS 9.1)

High: Training Data Inference (CVSS 7.8)

Medium: Insufficient Rate Limiting (CVSS 5.3)

AI Security Testing Tools

MITRE ATLAS Integration

Available Tools:

AI Model Security Scanner

Data Layer
Attacks

Model Layer
Attacks

Deployment
Attacks

Infrastructure
Attacks