Framework Specialisations
Avenlis Copilot aligns with key industry standards and frameworks to provide actionable insights, reliable guidance, and robust tools for assessing and enhancing the security of AI systems, particularly Large Language Models (LLMs).
Importance of Security Frameworks in AI
AI-driven systems, particularly Large Language Models (LLMs), are increasingly targeted by adversaries. To combat these risks, security frameworks provide a structured approach to identifying, mitigating, and preventing vulnerabilities.
Why Are These Frameworks Important?
Adversarial Attacks
AI models face risks like prompt injection, model manipulation, and data poisoning.
Data Security & Privacy
Without proper safeguards, LLMs can unintentionally expose sensitive information.
Regulatory Compliance
Organizations must meet legal and ethical guidelines for responsible AI use.
Trust & Transparency
Frameworks ensure accountability in AI decision-making processes.
MITRE ATLAS
Key Capabilities
Tactics & Techniques
Understand how attackers compromise AI systems
Identify Weaknesses
Spot vulnerabilities in AI architectures
Countermeasures
Implement real-world defensive strategies
Importance of MITRE ATLAS
Mapping Adversarial AI Attacks: Categorizes attack techniques including data poisoning and model inversion
Understanding AI-Specific Threats: Provides insights into vulnerabilities unique to AI and LLMs
Defining Defensive Strategies: Offers structured approaches to detect and mitigate threats
Standardizing AI Security Practices: Promotes industry-wide security methodologies
Enhancing AI Incident Response: Helps organizations respond effectively to AI security incidents
Avenlis Copilot MITRE ATLAS Knowledge
- • Query the MITRE ATLAS framework to explore adversarial tactics and techniques
- • Access real-world examples and case studies of adversarial AI attacks
- • Learn mitigation strategies tailored to specific attack types
OWASP Top 10 for LLMs 2025
Taxonomy
Prompt Injection
Malicious inputs to manipulate model behavior and override safety constraints
Sensitive Information Disclosure
Unintended leakage of confidential data through model interactions
Supply Chain
Threats from third-party dependencies and upstream model vulnerabilities
Data and Model Poisoning
Injection of malicious data to corrupt model performance
Improper Output Handling
Failure to sanitize outputs, resulting in potential harm or misuse
Excessive Agency
Over-reliance on LLMs for critical decision-making without oversight
System Prompt Leakage
Exposure of system-level prompts that can compromise security
Vector and Embedding Weakness
Exploitation of vulnerabilities in embedding layers
Misinformation
Generation or propagation of misleading or harmful content
Unbounded Consumption
Resource exhaustion from unbounded input processing
Avenlis Copilot OWASP Top 10 for LLMs Knowledge
• Query OWASP Top 10 for LLMs 2025 Knowledge Base: Access detailed insights into LLM01-LLM10 vulnerabilities
• Identify Common Risks: Learn about critical security threats and their potential impact
• Explore Attack Patterns: Understand exploitation methods and malicious techniques
• Learn Mitigation Strategies: Get step-by-step guidance on securing LLM applications
• Enhance Security Policies: Apply OWASP-recommended controls and governance
NIST AI Risk Management Framework (RMF)
- Identifying AI Vulnerabilities (Prompt Injection, Data Poisoning)
- Mapping AI Bias and Ethical Risks
- Understanding Attack Vectors
- Assessing Compliance Gaps
- Threat Modeling for LLMs
- Evaluating Trustworthiness Metrics
- Impact Assessment on Business and Users
- Security Posture Monitoring
- Adversarial Robustness Testing
- Bias and Fairness Optimization
- Output Control Mechanisms
- Security Best Practices Implementation
- AI Governance Policies & Frameworks
- Continuous AI Risk Monitoring
- Regulatory Compliance & Legal Considerations
- Transparency and Explainability Standards
Avenlis Copilot NIST AI RMF Knowledge
• Query NIST AI RMF Knowledge Base: Access comprehensive insights into the four core functions
• Identify AI Security Risks: Learn about critical vulnerabilities and compliance gaps
• Explore Risk Quantification: Understand assessment and measurement methodologies
• Learn NIST-Compliant Strategies: Get guidance on securing AI applications
• Enhance AI Governance: Apply NIST-aligned controls for continuous monitoring