Security Framework

Last updated: January 2026

1. Introduction: GaletAI Public Testing Framework for ANIXAI
(PRE-PROPOSAL CANDIDATE)

GaletAI is committed to advancing the state of AI safety and security through transparency, collaboration, and rigorous testing. As part of our mission to develop responsible and trustworthy artificial intelligence systems, we are proposing the development and public release of the ANIXAI Security & Safety Testing Framework – an open-source, comprehensive toolkit designed to evaluate, validate, and continuously improve the security posture of Large Language Model (LLM) systems.

This proposal draws upon cutting-edge research in LLM security, including the OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework (AI RMF), Google's Secure AI Framework (SAIF), and emerging best practices from the AI safety community. Our framework aims to address the current security challenges of 2025 while proactively preparing for emerging threats anticipated in 2026 and beyond.

1.1. Why Open Source?

In an era where 80% of business leaders cite data leakage via AI as a top concern and 88% worry about prompt based attacks, transparency and collective action are paramount. By open-sourcing the ANIXAI Testing Framework, we enable:

  • Community Collaboration: Researchers, security professionals, and developers worldwide can inspect, validate, contribute to, and extend the framework
  • Accelerated Innovation: Shared knowledge and rapid dissemination of new threat detection methods benefit the entire AI ecosystem
  • Public Trust: Transparent security practices demonstrate our commitment to building safe AI systems
  • Industry Standardization: Providing a reference implementation that can become a benchmark for LLM safety evaluation
  • Regulatory Alignment: Facilitating compliance with emerging regulations like the EU AI Act through documented, auditable security practices

1.2. Framework Objectives

The ANIXAI Security & Safety Testing Framework is designed to:

  • Systematically identify and mitigate LLM specific vulnerabilities (prompt injection, data poisoning, model theft, etc.)
  • Enable automated and manual security testing throughout the AI development lifecycle
  • Provide configurable safety policies adaptable to different industries, use cases, and risk profiles
  • Integrate emotion detection and tone management for comprehensive user safety
  • Support compliance with GDPR, EU AI Act, and other regulatory requirements
  • Foster a defense in depth approach combining multiple layers of protection

2. Current LLM Security Landscape (2025)

As of 2025, the AI industry faces a dramatically expanded threat surface. Organizations are grappling with how to secure LLMs and their integrations while meeting emerging compliance requirements. Below we outline the key security challenges that the ANIXAI framework is designed to address:

2.1. OWASP Top 10 LLM Risks

Our framework is built around addressing the most critical vulnerabilities identified by the OWASP Top 10 for LLM Applications:

Risk #1: Prompt Injection & Jailbreaks

Prompt injection remains the most frequent attack vector in 2025. Maliciously crafted inputs trick models into ignoring safeguards or revealing secrets through hidden instructions or clever phrasing ("jailbreaking"). The ANIXAI framework implements:

  • System Prompt Isolation: Never concatenating raw user input with system instructions
  • Input Validation: Pre checking all user queries against blocklists and adversarial pattern detection
  • Automated Red Teaming: Continuous testing with thousands of known jailbreak attempts

Risk #2: Insecure Output Handling

Without output validation, LLMs might return text that causes security issues in downstream systems (XSS, RCE attacks). Our framework treats all LLM outputs as untrusted, applying:

  • Zero Trust Principles: Treating the model like any external user
  • Output Sanitization: Validating and escaping model generated content before use
  • Content Security Policies: Enforcing restrictions on executable code in outputs

Risk #3: Data Poisoning (Training & Retrieval)

Corrupted training data can implant biases or backdoors. For RAG systems, poisoned knowledge bases can mislead models. The framework secures the AI data supply chain through:

  • Data Provenance Tracking: Documenting origins of all training and retrieval data
  • Integrity Checks: Using checksums and signatures on approved data sources
  • Toxicity Filtering: Scanning for and removing untrusted or harmful content

Risk #4: Model Denial of Service

Adversaries exploit LLM resource usage through extremely large or complex prompts. Mitigations include:

  • Rate Limiting: Enforcing request limits per user/IP
  • Input Size Caps: Maximum token limits for prompts
  • Resource Monitoring: Real time tracking of compute usage and cost anomalies

Risk #5: Supply Chain Vulnerabilities

Pre-trained models, third party APIs, and plugins can introduce vulnerabilities. Our approach:

  • Vendor Vetting: Rigorous evaluation of all third party components
  • Component Inventory: Maintaining SBOM (Software Bill of Materials) for AI systems
  • Continuous Testing: Automated security scans of dependencies

Risk #6: Sensitive Data Leakage

LLMs can inadvertently reveal private information from training data or prompts. Protections include:

  • Data Loss Prevention (DLP): Filters scanning outputs for PII, secrets, and confidential data
  • Prompt Sanitization: Removing sensitive information before model processing
  • Fine tuned Refusals: Training models to reject requests for personal data

Risk #7: Excessive Autonomy & Agent Risks

Over privileged LLM agents can cause harm through incorrect decisions or manipulation. Controls:

  • Least Privilege: Limiting tools and permissions available to agents
  • Human in the loop: Requiring approval for high impact actions
  • Sandboxing: Isolating agent actions in controlled environments
  • Circuit Breakers: Emergency kill switches for unexpected behavior

Risk #8: Hallucinations & Misinformation

Confident but false outputs pose safety and liability risks. Mitigations:

  • Human Oversight: Review requirements for critical decisions
  • Confidence Scoring: Flagging low-certainty outputs for verification
  • Source Attribution: Requiring models to cite sources where possible

Risk #9: Model Theft & Privacy Attacks

Attackers attempt to steal model parameters or reconstruct training data. Defenses:

  • Access Controls (RBAC): Restricting model endpoint access
  • Query Rate Limiting: Preventing model extraction through repeated queries
  • Differential Privacy: Adding noise during training to prevent data reconstruction

Risk #10: Insecure Plugin Design

Third-party plugins and integrations expand the attack surface. Framework ensures:

  • Plugin Sandboxing: Isolated execution environments
  • Permission Management: Explicit user consent for plugin capabilities
  • Security Audits: Mandatory testing before plugin approval

3. Emerging Challenges for 2026 and Beyond

The ANIXAI framework is designed to be future proof, addressing anticipated threats as AI systems become more autonomous and adversaries more sophisticated:

3.1. Autonomous Agents & "Vibe Hacking"

As LLM-based agents gain autonomy, attackers will use subtle manipulation ("vibe hacking") to influence agent behavior over time. Framework responses:

  • Intent Validation: Rigorous checking of agent action rationales
  • State Monitoring: Detecting behavioral drift or persona changes
  • Dynamic Kill Switches: Instant halting of agents acting unexpectedly

3.2. Advanced Adversarial Prompts

AI-generated jailbreaks using reinforcement learning and multi-step sequences will escalate. Countermeasures:

  • AI vs AI Defense: Using adversarial models to detect sophisticated attacks
  • Steganographic Detection: Identifying embedded prompts in images or code
  • Automated Red Teaming: Continuous discovery of new attack vectors (inspired by AI2's WildTeaming)

3.3. Embedding & Vector Database Attacks

Stolen embedding vectors can be inverted to recover original text, constituting data breaches. Protections:

  • Encryption: Securing vector databases with encryption at rest and in transit
  • Obfuscation Techniques: Generating less invertible embeddings through cryptographic methods
  • Access Restrictions: Treating embeddings as sensitive assets

3.4. Regulatory Compliance Challenges

EU AI Act enforcement begins in 2026, requiring transparency and cybersecurity documentation. Framework provides:

  • Audit Trail Generation: Comprehensive logging of AI decisions and interactions
  • Documentation Automation: Auto-generated compliance reports
  • Risk Management Integration: Alignment with NIST AI RMF and ISO/IEC 42001

4. ANIXAI Framework Architecture

The ANIXAI Security & Safety Testing Framework implements a multi layered, defense in depth approach integrating established standards with novel safeguards specifically tailored to LLM behavior.

4.1. Three-Tier Policy Orchestration

Our framework harmonizes safety rules across three hierarchical layers:

Layer A: User Specified Safety Requirements

Customizable rules set by deploying organizations or end-users:

  • Domain-Specific Policies: Industry or organization-specific content restrictions
  • Brand Guidelines: Tone, style, and messaging consistency requirements
  • User Preferences: Individual comfort settings (e.g. family friendly mode, formality level)
  • Configuration Interface: Both GUI toggles for non-technical users and advanced scripting for experts

Layer B: Internal Base Policies (AI Constitution)

Foundational safety rules that cannot be overridden:

  • Universal Safety Standards: Prohibitions on illegal activities, hate speech, violence, self-harm content
  • Ethical Principles: Transparency requirements, truthfulness expectations, uncertainty acknowledgment
  • ANIXAI Core Values: Respect, helpfulness, harmlessness (inspired by Constitutional AI approaches)
  • Hallucination Prevention: "Say 'I do not know' rather than fabricate" principle

Layer C: Regulatory & Privacy Compliance

Legal requirements and data protection obligations:

  • GDPR Compliance: PII detection, data minimization, consent management, right-to-erasure support
  • EU AI Act: Transparency disclosures, high risk system documentation, human oversight requirements
  • Industry Regulations: Pre built compliance packs (HIPAA for healthcare, FINRA for finance, COPPA for child safety)
  • Data Retention: Automated enforcement of log deletion policies

4.2. Secure Output Generation Workflow

Every interaction flows through a six stage security pipeline:

Stage 1: Input Handling & Validation

  • Security gateway checks queries against blocklists and adversarial pattern databases
  • Intent classification identifies disallowed requests (e.g. requests for illegal content)
  • System prompt isolation prevents user input from altering core instructions
  • Automated refusal or safe completion for policy-violating inputs

Stage 2: Context Management (RAG Security)

  • Provenance verification for retrieved documents (trusted sources only)
  • Malicious payload scanning in external knowledge bases
  • Checksums and digital signatures on approved content
  • RAG poisoning detection through anomaly analysis

Stage 3: Core LLM Processing with Runtime Guards

  • Sandboxed execution for code generation outputs
  • Computation resource limits (preventing DoS through infinite loops)
  • Tool call intent verification for agent actions (with explanations)
  • Human confirmation gates for high-impact operations

Stage 4: Multi-Layer Output Analysis

Before returning results, outputs pass through comprehensive filtering:

  • Content Moderation: Detection of profanity, hate speech, violence, self-harm content using multiple APIs (Azure Content Safety, AWS Comprehend, open-source models)
  • Privacy Filtering: Named Entity Recognition (NER) to identify and redact PII (names, addresses, phone numbers, email, SSNs)
  • Compliance Checking: Domain-specific validation (e.g., healthcare disclaimers, financial advice warnings)
  • Emotional Tone Analysis: Sentiment and emotion detection ensuring appropriate conversational stance
  • Bias Detection: Screening for demographic biases or unfair generalizations
  • Factuality Verification: Cross-referencing claims against trusted knowledge bases where applicable

Stage 5: Logging & Audit Trail

  • Structured logging of every input-output pair (privacy-preserving)
  • Policy violation documentation with severity ratings
  • Performance metrics tracking (refusal rates, filter activation frequencies)
  • Integration with SIEM systems for security operations centers
  • Explainability traces documenting decision rationales

Stage 6: Continuous Improvement Loop

  • Aggregated analytics identifying emerging threat patterns
  • Automated feedback to update filters and rules
  • Regular model retraining incorporating safety feedback
  • Periodic security audits (monthly red teams, quarterly external assessments)

5. Explainable AI Integration

A unique differentiator of the ANIXAI framework is deep integration of explainability into security analysis:

5.1. Safety Debugging Through Explainability

  • Rationale Extraction: LLMs can be queried to explain why they generated specific outputs
  • Influence Analysis: SHAP values or attention weights reveal which input tokens or training data influenced decisions
  • Root Cause Investigation: When harmful outputs occur, AI traces identify flawed reasoning or toxic source material
  • Traceability for Compliance: Audit logs include explanations satisfying regulatory transparency requirements

5.2. Example X-AI Workflow

When a model produces a surprising or policy violating output:

  1. The framework automatically invokes an explanation module
  2. Explanation reveals which training examples or retrieval documents had high influence
  3. Security team investigates those sources for contamination or bias
  4. Corrective action: refine model, adjust prompt templates, or update knowledge base
  5. Documentation of the incident and fix feeds into compliance reporting

6. Emotion Detection & Tone Management

Recognizing that emotional safety is critical for user wellbeing, the ANIXAI framework includes sophisticated emotion AI capabilities:

6.1. Input Emotion Analysis (ANIXAI based independent solution possible)

Detecting user emotional states to enable appropriate responses:

  • Sentiment Classification: Positive, neutral, negative, angry, sad, anxious, frustrated
  • Crisis Detection: Identifying expressions of self harm intent, suicidal ideation, or severe distress
  • Escalation Prevention: Recognizing when a user is becoming more agitated

6.2. Output Tone Adaptation

Ensuring AI responses match appropriate emotional stance:

  • Empathy Generation: When users express sadness or frustration, responses convey understanding and support
  • De-escalation: Calm, apologetic tone for angry users (preventing AI from mirroring negativity)
  • Crisis Response: For self harm expressions, providing helpline resources and compassionate guidance rather than generic answers
  • Brand Consistency: Customizable emotional profiles (professional, friendly, casual) aligned with organizational identity

6.3. Configurable Emotion Modules

The framework supports pluggable emotion detection providers:

  • IBM Watson Tone Analyzer
  • Amazon Comprehend Sentiment
  • Microsoft Text Analytics
  • Open-source models from HuggingFace (e.g. emotion classification transformers)
  • Custom enterprise models for specialized domains

Policy driven rules govern emotion handling: IF user_emotion == "ANGRY" AND AI_tone == "NEGATIVE" THEN flag_for_human_review

7. Automated Testing Toolkit

The ANIXAI Testing Toolkit provides comprehensive, automated security validation throughout the AI development lifecycle.

7.1. Basic Safety Test Suite

Out-of-the-box tests covering common failure modes:

Prompt Injection Tests

  • Database of 10,000+ known jailbreak attempts
  • Multi-turn conversation attacks
  • Hidden instruction techniques
  • System prompt extraction attempts
  • Pass/fail reporting with vulnerability severity ratings

Content Moderation Tests

  • Attempts to elicit prohibited content categories (hate speech, violence, sexual content, self-harm, illegal activities)
  • Edge case testing (satire vs. genuine hate, fiction vs. instruction)
  • Refusal behavior validation
  • Safe alternative generation verification

Privacy & Data Leakage Tests

  • Insertion of synthetic PII (fake credit cards, SSNs, phone numbers) to test echo prevention
  • Queries about private individuals to detect training data leakage
  • Membership inference attack simulations
  • Corporate secret exposure tests (for enterprise deployments)

Hallucination & Factuality Tests

  • Questions with known correct answers to detect fabrications
  • Requests for citations and source verification
  • Confidence calibration assessment

Bias & Fairness Tests

  • Parallel scenarios with demographic variations to detect differential treatment
  • Stereotype perpetuation analysis
  • Representation balance checks

7.2. Advanced Custom Testing

Users can create domain-specific test scenarios:

  • Scripted Scenarios: YAML or Python-based test definitions
  • Multi Turn Dialogues: Testing context retention and consistency across conversations
  • Industry Specific Cases: Financial advice compliance, medical disclaimer requirements, legal cautions
  • Emotion Triggered Tests: Verifying appropriate responses to various emotional inputs (anger, sadness, crisis)

7.3. CI/CD Integration

Continuous security validation throughout development:

  • Automated Test Execution: GitHub Actions, GitLab CI, Jenkins plugins
  • Regression Testing: Ensuring updates don't weaken existing safety behaviors
  • Deployment Gates: Failing builds if critical safety tests fail
  • Performance Dashboards: Tracking safety metrics over time (refusal rates, filter activations, test pass rates)

7.4. Red Team Automation

Inspired by AI2's WildTeaming project:

  • Automated generation of novel adversarial prompts using reinforcement learning
  • Integration with public jailbreak databases (regularly updated)
  • Community-contributed attack scenarios
  • Self-improving test suite that evolves with the threat landscape

8. Stakeholder Customization & Use Cases

The ANIXAI framework is designed for flexibility across diverse stakeholders and industries:

8.1. Enterprise Internal Use

Focus: Data security, compliance, auditability

  • Identity Integration: SSO with corporate identity providers (Azure AD, Okta)
  • Data Residency: On-premise or region-specific cloud deployment options
  • Corporate Policy Enforcement: Custom rules preventing disclosure of confidential projects, trade secrets
  • Compliance Packs: Pre-configured for SOX, FINRA, HIPAA, ISO 27001
  • Audit Trail Generation: Detailed logs for regulatory inspections

8.2. Customer-Facing Chatbots

Focus: User safety, brand protection, crisis handling

  • Content Moderation: Heavy emphasis on preventing offensive or legally problematic outputs
  • Emotion Detection: Active use of sentiment analysis for de-escalation and empathy
  • Crisis Response: Automated provision of helpline resources for self-harm expressions
  • Transparency Disclosures: Automatic "I am an AI" messages (EU AI Act compliance)
  • Human Escalation: Seamless handoff to human agents when needed

8.3. Entertainment & Creative AI

Focus: Content rating compliance, immersive experience, community moderation

  • Configurable Content Filters: Adjustable thresholds aligned with ESRB/PEGI ratings (Teen, Mature, etc.)
  • Fantasy Violence Allowances: Permitting genre-appropriate content while blocking extreme violations
  • Community Moderation: Open rule sets that communities can extend (with base safety preserved)
  • Enhanced Emotion AI: 20+ emotional states for realistic NPC interactions

8.4. Research & Academic Institutions

Focus: Transparency, reproducibility, ethical research

  • Full Documentation: Comprehensive technical specifications
  • Experimental Modes: Controlled testing environments with IRB approval integration
  • Data Anonymization: Built-in privacy-preserving techniques (differential privacy, k-anonymity)
  • Publication Support: Automated generation of methodology sections for papers

9. Open Source Governance & Community Engagement

9.1. Licensing

The ANIXAI Testing Framework will be released under a permissive open-source license (Apache 2.0 or MIT), enabling:

  • Free commercial and non-commercial use
  • Modification and redistribution
  • Private customization without mandatory contribution
  • Patent protection for contributors

When referencing, adapting, or building upon this Security Framework (any version), please use give appropriate credit to GaletAI (galetai.com), provide a link to the license, and indicate if changes were made. The following citation recommended: "Source: GaletAI Security Framework (Version PROVIDE_VERSION_NUMBER_HERE)- https://galetai.com/security-framework".

9.2. Governance Model

  • Core Maintainers: GaletAI security team provides initial stewardship
  • Advisory Board: External experts from academia, industry, and civil society
  • Community Contributions: Public GitHub repository accepting pull requests
  • Security Disclosures: Responsible disclosure process for vulnerability reporting

9.3. Community Resources

  • Documentation Portal: Comprehensive guides, API references, tutorials
  • Test Case Repository: Community-contributed adversarial prompts and safety scenarios
  • Integration Examples: Sample implementations for popular frameworks (LangChain, LlamaIndex, Haystack)
  • Discussion Forum: GitHub Discussions for Q&A and feature requests
  • Regular Releases: Quarterly updates incorporating new threats and community feedback

10. Alignment with Standards & Regulations

10.1. NIST AI Risk Management Framework

The ANIXAI framework operationalizes NIST's four core functions:

  • Govern: Policy orchestration layer, role-based access controls, documentation generation
  • Map: Threat identification (OWASP Top 10, MITRE ATLAS), risk assessment, use case enumeration
  • Measure: Automated testing toolkit, continuous monitoring, performance dashboards
  • Manage: Multi-layer controls, incident response, continuous improvement loops

10.2. OWASP LLM Top 10

Explicit coverage of all ten critical vulnerabilities with documented mitigations and test cases for each.

10.3. EU AI Act Compliance

  • Transparency Requirements: Automated user disclosure generation
  • High Risk System Documentation: Technical documentation templates, conformity assessment support
  • Human Oversight: Configurable human-in-the-loop gates for critical decisions
  • Record Keeping: Comprehensive audit trails with retention policy enforcement

10.4. GDPR/Data Protection

  • PII detection and redaction
  • Data minimization enforcement
  • Right to erasure support (data deletion capabilities)
  • Privacy by design principles throughout architecture

10.5. ISO/IEC 42001 (AI Management Systems)

Framework components map to ISO 42001 controls, facilitating certification for organizations seeking compliance.

11. Technical Specifications

11.1. Architecture

  • Language: Python 3.9+ (core framework), with SDKs planned for JavaScript/TypeScript, Java
  • Deployment: Containerized (Docker), Kubernetes-ready, serverless options (AWS Lambda, Azure Functions)
  • Interfaces: REST API, Python SDK, CLI tools, web dashboard
  • Integrations: LangChain, LlamaIndex, Haystack, OpenAI API, Azure OpenAI, Anthropic, Hugging Face

11.2. Performance

  • Latency: < 50ms overhead for typical input/output filtering
  • Throughput: Designed for 10,000+ requests/second with horizontal scaling
  • Resource Usage: Minimal footprint for edge deployment scenarios

11.3. Extensibility

  • Plugin Architecture: Custom filters, emotion detectors, compliance modules
  • Rule Engine: Declarative policy definitions (YAML/JSON)
  • Model Agnostic: Works with any LLM via standardized interfaces

12. Roadmap & Milestones

Phase 1: Alpha Release (Q1 2026)

  • Core framework with basic OWASP Top 10 coverage
  • Initial testing toolkit (1,000+ test cases)
  • Documentation and quickstart guides
  • Invitation-only early access for security researchers

Phase 2: Beta Release (Q2 2026)

  • Emotion detection integration
  • Enterprise compliance packs (GDPR, HIPAA, SOX)
  • CI/CD integrations (GitHub Actions, GitLab CI)
  • Public beta testing with community feedback

Phase 3: Version 1.0 (Q3 2026)

  • Full NIST AI RMF and ISO 42001 alignment
  • 10,000+ adversarial test cases
  • Multi-language SDKs
  • Official public launch with community governance transition

Phase 4: Ongoing (Q4 2026+)

  • Quarterly security updates
  • Community-driven feature additions
  • Industry-specific modules (healthcare, finance, education)
  • Research collaborations and academic partnerships

13. Call for Collaboration

GaletAI invites the global AI safety community to participate in the development and refinement of the ANIXAI Testing Framework:

13.1. How to Contribute

  • Security Researchers: Submit adversarial test cases, conduct independent audits, participate in bug bounty programs
  • Developers: Contribute code improvements, integrations, documentation
  • Organizations: Pilot the framework, provide real-world feedback, sponsor specific features
  • Academics: Collaborate on research, publish findings, validate methodologies
  • Regulators: Provide guidance on compliance requirements, validation of regulatory alignment

13.2. Early Access Program

Interested parties can apply for early access to pre-release versions of the framework:

  • Application: [email protected]
  • Requirements: Commitment to responsible disclosure, willingness to provide structured feedback
  • Benefits: Influence framework design, early deployment advantages, co-authorship opportunities on research publications

14. Conclusion

The ANIXAI Security & Safety Testing Framework represents GaletAI's commitment to responsible AI development and our belief that transparency and collaboration are essential to building trustworthy AI systems. By open-sourcing this comprehensive toolkit, we aim to:

  • Elevate industry-wide security standards for LLM applications
  • Empower organizations of all sizes to deploy AI safely and compliantly
  • Foster a collaborative ecosystem where threats are rapidly identified and mitigated
  • Demonstrate that cutting-edge AI capabilities and rigorous safety are not mutually exclusive

As we move into 2026 and beyond, with AI systems becoming more powerful and pervasive, the need for systematic security frameworks has never been greater. The ANIXAI Testing Framework is our contribution to this critical challenge, and we look forward to working with the global community to continuously improve and evolve it.

15. Contact Information

For inquiries about the ANIXAI Security & Safety Testing Framework:

GaletAI

Document Version: 0.75
Published: December 18, 2025
Last updated: January 19 2026
Next Review: June 2026