🤖 GenAI Security Framework

GenAI Security Overview

Framework Structure

The GenAI Security Framework is built on industry best practices and emerging standards, incorporating insights from OWASP LLM Top 10, NIST AI Risk Management Framework, and real-world AI security incidents.

AI Risk Landscape

The AI threat landscape continues to evolve rapidly, with new attack vectors and vulnerabilities emerging as AI systems become more sophisticated and widely deployed.

Emerging Threats:

• Adversarial AI: Model extraction, membership inference, and adversarial examples
• Prompt Engineering Attacks: Advanced injection techniques and jailbreaking methods
• Data Poisoning: Training data contamination and backdoor attacks
• Model Inversion: Reverse engineering sensitive training data from model outputs
• Supply Chain Attacks: Compromised pre-trained models and malicious dependencies

Prompt Injection & Security

Injection Attack Types

Direct Prompt Injection

Attacks where malicious instructions are directly embedded in user prompts to manipulate AI behavior.

• Command Injection: Embedding system commands in prompts
• Role Hijacking: Convincing the AI to adopt a different persona or role
• Instruction Override: Overriding system instructions with user-provided commands
• Context Manipulation: Altering the conversation context to influence responses

Indirect Prompt Injection

Attacks delivered through external data sources that the AI system processes, such as documents or web content.

• Document-Based Injection: Malicious instructions embedded in processed documents
• Web Content Injection: Exploiting AI systems that browse the internet
• Data Source Poisoning: Compromising external data sources
• Chain-of-Thought Manipulation: Influencing reasoning processes through crafted inputs

Defense Mechanisms

Detection Strategies

Real-time Detection

• Entropy Analysis: Measuring information entropy in prompts
• Semantic Similarity: Comparing prompts against known attack patterns
• Linguistic Analysis: Detecting unusual language patterns or structures
• Context Anomalies: Identifying sudden context changes or contradictions

Post-Processing Analysis

• Response Deviation: Detecting unusual response patterns
• Sentiment Analysis: Monitoring emotional tone and content safety
• Content Classification: Categorizing outputs for policy violations
• Factual Verification: Cross-referencing outputs with trusted sources

Model Security & Integrity

Model Protection

Access Control Mechanisms

• Role-Based Access Control (RBAC): Granular permissions for model access
• Attribute-Based Access Control (ABAC): Context-aware access decisions
• Multi-Factor Authentication: Enhanced authentication for sensitive operations
• API Key Management: Secure key generation, rotation, and revocation
• Network Segmentation: Isolating model infrastructure

Integrity Verification

• Digital Signatures: Cryptographic verification of model authenticity
• Hash Verification: Ensuring model files haven't been tampered with
• Checksum Validation: Automated integrity checks during deployment
• Provenance Tracking: Maintaining complete model lineage

Version Management

Deployment Security

Secure Infrastructure

• Container Security: Hardened container images and runtime protection
• Orchestration Security: Kubernetes security policies and network policies
• Secrets Management: Secure storage and injection of sensitive configuration
• Certificate Management: TLS/SSL certificate automation and rotation
• Network Security: WAF, DDoS protection, and traffic filtering

Runtime Protection

• Resource Monitoring: CPU, memory, and GPU usage tracking
• Behavioral Monitoring: Detecting anomalous model behavior
• Performance Baselines: Establishing normal operation parameters
• Health Checks: Automated model health and availability monitoring

Data Privacy & Protection

Privacy Frameworks

PII Detection & Anonymization

Detection Techniques

• Named Entity Recognition (NER): ML-based identification of personal identifiers
• Regular Expression Patterns: Rule-based detection of structured data
• Contextual Analysis: Understanding data context and sensitivity
• Statistical Methods: Identifying quasi-identifiers through analysis
• Custom Classifiers: Domain-specific PII detection models

Anonymization Methods

Consent Management

Consent Collection

• Granular Consent: Purpose-specific consent for different AI use cases
• Dynamic Consent: Real-time consent updates and modifications
• Consent Proof: Cryptographic proof of consent collection
• Withdrawal Mechanisms: Easy consent revocation processes

Rights Management

• Right to Access: Providing individuals with their data and processing information
• Right to Rectification: Correcting inaccurate personal data
• Right to Erasure: Deleting personal data upon request
• Right to Portability: Providing data in machine-readable formats
• Right to Explanation: Explaining AI decision-making processes

Hallucination Control

Detection Methods

Consistency Checking

• Multi-Response Analysis: Comparing multiple outputs for the same input
• Temperature Variation: Testing output stability across different generation parameters
• Temporal Consistency: Checking for consistent responses over time
• Cross-Model Validation: Comparing outputs from different models

Factual Verification

• Knowledge Base Lookup: Cross-referencing with trusted knowledge sources
• Real-time Fact Checking: API-based verification against current data
• Citation Requirements: Mandating sources for factual claims
• Confidence Scoring: Model-based confidence assessment

Mitigation Strategies

Validation Frameworks

Automated Validation Pipeline

• Content Classification: Categorizing outputs by type and risk level
• Fact-checking APIs: Integrating external verification services
• Semantic Analysis: Understanding content meaning and context
• Quality Scoring: Automated quality assessment metrics
• Human-in-the-loop: Expert review for high-risk outputs

Quality Metrics

• Factual Accuracy: Percentage of verifiable statements that are correct
• Relevance Score: Alignment between input queries and outputs
• Coherence Rating: Logical consistency within responses
• Source Attribution: Proper citation and reference tracking

Ethical AI & Bias Mitigation

Bias Detection

Types of AI Bias

• Historical Bias: Reflecting past discrimination in training data
• Representation Bias: Underrepresenting certain groups in datasets
• Measurement Bias: Systematic errors in data collection methods
• Aggregation Bias: Assuming one model fits all subgroups equally
• Confirmation Bias: Seeking information that confirms preexisting beliefs

Detection Methodologies

Fairness Metrics

Responsible AI Practices

Governance Framework

• Ethics Committees: Cross-functional teams overseeing AI ethics
• Impact Assessments: Evaluating societal impact before deployment
• Stakeholder Engagement: Including affected communities in design processes
• Continuous Monitoring: Ongoing fairness and bias assessment
• Transparency Reports: Public documentation of AI system capabilities and limitations

Technical Implementation

• Adversarial Debiasing: Training models to be invariant to sensitive attributes
• Fair Representation Learning: Learning representations that encode fairness constraints
• Post-processing Calibration: Adjusting outputs to achieve fairness goals
• Constraint-based Optimization: Incorporating fairness constraints into training objectives

AI Governance & Compliance

Regulatory Landscape

Emerging AI Regulations

• EU AI Act: Comprehensive AI regulation with risk-based approach
• US Executive Order on AI: Federal standards for AI safety and security
• UK AI White Paper: Principles-based approach to AI regulation
• China AI Regulations: Algorithm recommendations and deep synthesis provisions
• NIST AI Risk Management Framework: Voluntary guidance for AI risk management

Compliance Requirements

Governance Frameworks

AI Governance Model

Audit Trails & Documentation

Documentation Requirements

• Model Cards: Standardized documentation of model capabilities and limitations
• Data Sheets: Comprehensive documentation of training datasets
• System Cards: High-level documentation of AI system behavior
• Risk Assessments: Formal evaluation of potential harms and mitigations
• Testing Reports: Results of safety, performance, and fairness evaluations

Audit Trail Components

• Decision Logging: Recording all AI system decisions and rationale
• Data Lineage: Tracking data sources and transformations
• Model Provenance: Complete history of model development and changes
• Access Logs: Recording who accessed the system and when
• Performance Metrics: Continuous monitoring of system performance

Supply Chain Security

Vendor Assessment

AI Vendor Evaluation Criteria

• Security Practices: Vendor security controls and certifications
• Model Quality: Performance, accuracy, and reliability metrics
• Data Governance: Data handling and privacy practices
• Transparency: Documentation and explainability capabilities
• Compliance: Regulatory alignment and audit capabilities
• Business Continuity: Vendor stability and support capabilities

Due Diligence Process

Dependency Management

Open Source AI Components

• License Compliance: Tracking and managing open source licenses
• Vulnerability Scanning: Automated scanning for known vulnerabilities
• Update Management: Systematic updating of dependencies
• Community Health: Assessing project maintenance and support

Dependency Security Practices

• Software Bill of Materials (SBOM): Complete inventory of components
• Dependency Pinning: Using specific versions to prevent supply chain attacks
• Source Verification: Verifying integrity of downloaded packages
• Isolation: Containerizing dependencies to limit blast radius

Model Provenance

Provenance Tracking

• Origin Documentation: Recording source of pre-trained models
• Training Data Lineage: Tracking data sources and preprocessing
• Modification History: Logging all changes and fine-tuning
• Distribution Chain: Recording how models are shared and deployed

Integrity Verification

• Digital Signatures: Cryptographic verification of model authenticity
• Hash Verification: Ensuring models haven't been tampered with
• Trusted Repositories: Using verified sources for model distribution
• Attestation: Formal verification of model properties and capabilities

Incident Response for AI

AI-Specific Incident Types

Model-Related Incidents

• Model Drift: Degraded performance due to changing data patterns
• Adversarial Attacks: Malicious inputs designed to fool the model
• Data Poisoning: Compromised training or inference data
• Model Extraction: Unauthorized copying of proprietary models
• Prompt Injection: Malicious prompt manipulation attacks

Data-Related Incidents

• Data Leakage: Exposure of sensitive training or user data
• Privacy Violations: Unauthorized processing of personal information
• Data Corruption: Integrity issues affecting model performance
• Consent Violations: Processing data without proper authorization

Response Procedures

Forensics & Analysis

AI-Specific Forensics

• Model State Analysis: Examining model weights and parameters
• Input/Output Correlation: Analyzing relationships between inputs and outputs
• Training Data Reconstruction: Attempting to recover training examples
• Adversarial Example Detection: Identifying manipulated inputs
• Behavioral Pattern Analysis: Understanding model decision patterns

Evidence Collection

• Model Artifacts: Preserving model files, weights, and configurations
• Training Data: Securing datasets and preprocessing pipelines
• System Logs: Collecting inference logs and system events
• Performance Metrics: Historical performance and monitoring data
• User Interactions: Analyzing user inputs and feedback

OWASP LLM Top 10

LLM01: Prompt Injection

Description: Manipulating LLMs through crafted inputs that override system instructions or cause unintended behavior.

Attack Scenarios:

• Direct Injection: User directly embeds malicious instructions in prompts
• Indirect Injection: Malicious instructions delivered through external sources
• Jailbreaking: Bypassing safety guardrails and content filters

Prevention Strategies:

• Implement robust input validation and sanitization
• Use prompt isolation techniques to separate system and user contexts
• Deploy output filtering and content validation
• Monitor for unusual behavior patterns

LLM02: Insecure Output Handling

Description: Insufficient validation and sanitization of LLM outputs before passing them to downstream systems.

Common Vulnerabilities:

• Code injection through LLM-generated code
• Cross-site scripting (XSS) in web applications
• SQL injection through database queries
• Command injection in system operations

Mitigation Approaches:

• Implement comprehensive output validation
• Use parameterized queries and prepared statements
• Apply principle of least privilege for downstream systems
• Sanitize outputs before rendering in user interfaces

LLM03: Training Data Poisoning

Description: Manipulation of training data to introduce vulnerabilities, backdoors, or biases into the model.

Attack Types:

• Backdoor Attacks: Inserting trigger patterns that cause specific behaviors
• Data Quality Degradation: Introducing low-quality or incorrect information
• Bias Injection: Deliberately skewing model outputs

Defense Mechanisms:

• Implement rigorous data validation and quality checks
• Use trusted and verified data sources
• Apply anomaly detection to identify poisoned samples
• Maintain data provenance and audit trails

LLM04: Model Denial of Service

Description: Attacks that cause resource consumption issues, leading to service degradation or unavailability.

Attack Vectors:

• Resource-intensive queries consuming excessive compute
• Long input sequences causing memory exhaustion
• High-frequency requests overwhelming the system
• Complex reasoning tasks requiring extended processing

Protective Measures:

• Implement rate limiting and request throttling
• Set maximum input length and complexity limits
• Use resource monitoring and auto-scaling
• Deploy circuit breakers and graceful degradation

LLM05: Supply Chain Vulnerabilities

Description: Risks introduced through compromised components in the AI development and deployment pipeline.

Vulnerability Sources:

• Pre-trained models with unknown provenance
• Compromised datasets from external sources
• Vulnerable dependencies and libraries
• Malicious plugins and extensions

Security Controls:

• Maintain comprehensive software bill of materials (SBOM)
• Verify integrity of all components using digital signatures
• Implement dependency scanning and vulnerability management
• Use trusted repositories and verified sources

LLM06: Sensitive Information Disclosure

Description: Unintentional revelation of confidential information through LLM outputs.

Information Types:

• Personal identifiable information (PII)
• Proprietary business information
• Training data memorization
• System configuration details

Prevention Techniques:

• Implement robust data anonymization in training
• Use differential privacy techniques
• Deploy output filtering for sensitive information
• Regular testing for information leakage

LLM07: Insecure Plugin Design

Description: Security flaws in LLM plugins that extend model capabilities and interact with external systems.

Common Issues:

• Insufficient input validation in plugins
• Excessive permissions and capabilities
• Insecure authentication and authorization
• Poor error handling revealing system information

Secure Development Practices:

• Follow secure coding standards for plugin development
• Implement principle of least privilege
• Use robust input validation and output encoding
• Regular security testing and code reviews

LLM08: Excessive Agency

Description: LLM systems granted excessive permissions or autonomy, leading to unintended or harmful actions.

Risk Scenarios:

• Unauthorized system modifications
• Unintended data deletion or corruption
• Excessive resource consumption
• Inappropriate external communications

Control Mechanisms:

• Implement strict role-based access controls
• Use human-in-the-loop for critical decisions
• Deploy comprehensive audit logging
• Set clear boundaries for autonomous actions

LLM09: Overreliance

Description: Excessive dependence on LLMs without adequate oversight, leading to misinformation or poor decision-making.

Contributing Factors:

• Lack of output verification mechanisms
• Insufficient understanding of model limitations
• Automation bias and overconfidence
• Inadequate human oversight processes

Mitigation Strategies:

• Implement multi-source verification for critical decisions
• Provide clear uncertainty quantification
• Maintain human oversight for high-stakes applications
• Regular training on AI limitations and risks

LLM10: Model Theft

Description: Unauthorized access, extraction, or replication of proprietary LLM models.

Theft Methods:

• Model extraction through API queries
• Direct access to model files
• Knowledge distillation attacks
• Side-channel information leakage

Protection Measures:

• Implement strong access controls and authentication
• Use query rate limiting and monitoring
• Deploy watermarking and fingerprinting techniques
• Regular security assessments and penetration testing

AI Blue Team Operations

Blue Team Fundamentals

AI Blue Team Capabilities

• AI Threat Intelligence: Understanding evolving AI attack vectors and techniques
• Model Behavior Analysis: Monitoring for anomalous model behavior and outputs
• Data Flow Security: Protecting data pipelines and training processes
• Adversarial Detection: Identifying adversarial examples and attacks
• Privacy Monitoring: Detecting potential privacy violations and data leaks

Specialized Skills Required

Monitoring & Detection

AI-Specific Monitoring

Detection Technologies

• Statistical Anomaly Detection: Identifying deviations from normal behavior patterns
• Machine Learning Detection: Using ML models to detect AI-specific attacks
• Rule-based Detection: Pattern matching for known attack signatures
• Behavioral Analytics: User and entity behavior analytics (UEBA) for AI systems
• Network Analysis: Monitoring network traffic for AI-related threats

AI Threat Hunting

Hunting Methodologies

• Hypothesis-Driven Hunting: Testing specific theories about AI attack techniques
• Indicator-Based Hunting: Searching for known indicators of compromise (IoCs)
• Anomaly-Based Hunting: Identifying unusual patterns in AI system behavior
• Intelligence-Driven Hunting: Leveraging threat intelligence for targeted searches

AI Hunting Techniques

Defense Automation

Automated Response Capabilities

• Automated Containment: Isolating compromised models or systems
• Dynamic Filtering: Real-time blocking of malicious inputs
• Model Rollback: Automatic reversion to previous model versions
• Alert Orchestration: Coordinating response across multiple security tools
• Evidence Collection: Automatic gathering of forensic artifacts

Security Orchestration

• SOAR Integration: Incorporating AI security into security orchestration platforms
• Playbook Development: Creating AI-specific incident response playbooks
• Tool Integration: Connecting AI monitoring tools with security platforms
• Workflow Automation: Streamlining AI security operations processes

Continuous Improvement

• Threat Modeling Updates: Regularly updating AI threat models
• Detection Tuning: Improving detection accuracy and reducing false positives
• Capability Assessment: Regular evaluation of blue team effectiveness
• Training and Development: Continuous learning and skill development
• Lessons Learned: Incorporating insights from incidents into future operations

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