AI Hacking Services

Stage 1: Define Objectives

We establish AI system security objectives aligned with business requirements, regulatory compliance, and risk tolerance for machine learning applications.

Stage 2: Define Technical Scope

Our team maps AI system architecture including data pipelines, model training infrastructure, inference engines, and API endpoints to establish comprehensive assessment boundaries.

Stage 3: Application Decomposition

We perform detailed decomposition of AI applications, identifying model types, training datasets, feature engineering processes, and deployment patterns that impact security posture.

Stage 4: Threat Analysis

Using AI-specific threat intelligence, we identify relevant attack vectors including adversarial examples, model extraction, data poisoning, and privacy inference attacks applicable to your AI systems.

Stage 5: Vulnerability Analysis

Our security analysts examine AI system components for known vulnerabilities, misconfigurations, and architectural weaknesses that could enable identified threats.

Stage 6: Attack Modeling

We develop detailed attack scenarios specific to your AI implementation, including attack trees for model compromise, data exfiltration, and system manipulation.

Stage 7: Risk and Impact Analysis

We quantify potential impact of successful AI attacks on business operations, regulatory compliance, and competitive advantage to prioritize remediation efforts.

Prompt Injection and LLM Security

Large Language Models and generative AI systems require specialized security testing approaches:

• Prompt Injection Testing: Crafting malicious prompts that bypass safety filters and extract sensitive information
• Context Manipulation: Exploiting context windows and attention mechanisms to influence model behavior
• Jailbreaking Assessments: Testing model alignment and safety mechanisms against adversarial inputs
• API Security Testing: Evaluating ChatGPT, Claude, and custom LLM API implementations for vulnerabilities

Computer Vision Security Testing

Visual AI systems face unique attack vectors requiring specialized assessment techniques:

• Adversarial Patches: Physical world attacks using printed patterns to fool computer vision systems
• Deepfake Detection: Assessing systems designed to identify synthetic media for bypass vulnerabilities
• Object Detection Evasion: Testing autonomous systems and security cameras against adversarial examples
• Biometric Spoofing: Evaluating facial recognition and authentication systems for security weaknesses

RL Systems

RL systems require specialized testing methodologies due to their dynamic learning nature:

• Reward Hacking: Assessing whether agents can exploit reward functions to achieve unintended objectives
• Policy Manipulation: Testing robustness of trained policies against adversarial state modifications
• Multi-Agent Security: Evaluating security in distributed RL environments and game-theoretic scenarios
• Safe Exploration Testing: Assessing safety mechanisms during agent training and deployment phases

Privacy-Preserving AI Security

Our services include assessment and implementation of privacy-preserving machine learning techniques:

• Differential Privacy: Implementing noise injection mechanisms to protect individual privacy in training data
• Homomorphic Encryption: Enabling computation on encrypted data without decryption
• Secure Multi-Party Computation: Facilitating collaborative learning without data sharing
• Federated Learning Security: Securing distributed training while maintaining data locality

Custom Attack Framework Development

We develop and deploy sophisticated tools for AI security assessment:

• Adversarial Example Generators: Custom tools for creating domain-specific adversarial inputs
• Model Inversion Frameworks: Specialized software for extracting training data from deployed models
• Gradient-Based Attack Tools: Implementing state-of-the-art optimization techniques for model exploitation
• Automated Vulnerability Scanning: Continuous monitoring systems for AI model security assessment

Threat Intelligence for AI Systems

Our threat intelligence services provide ongoing monitoring and analysis:

• AI Attack Pattern Analysis: Monitoring emerging attack techniques and vulnerability research
• Model Vulnerability Database: Maintaining comprehensive records of AI system vulnerabilities
• Threat Actor Profiling: Analyzing adversaries targeting AI systems across different industries
• Zero-Day AI Vulnerability Research: Identifying previously unknown attack vectors in AI systems

AI Governance and Risk Management

We help organizations establish comprehensive AI security governance using PASTA-based frameworks:

• PASTA-Based Risk Assessment: Implementing systematic threat modeling for AI system risk quantification
• Model Validation Protocols: Establishing procedures for ongoing security assessment of deployed models using PASTA stages
• Incident Response Planning: Creating specialized response procedures for AI security breaches based on PASTA attack modeling
• Security Metrics and KPIs: Defining measurable indicators for AI system security performance aligned with PASTA objectives

Regulatory Compliance Support

Our services ensure compliance with evolving AI regulations:

• GDPR AI Compliance: Ensuring AI systems meet European privacy regulations
• CCPA AI Requirements: Addressing California Consumer Privacy Act requirements for AI systems
• Industry-Specific Standards: Meeting sector-specific AI security requirements (ISO 27001, SOC 2)
• Algorithmic Accountability: Implementing transparency and explainability requirements for AI decisions