Framework Specialisations

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). This section introduces the frameworks integrated into Avenlis Copilot, explains their importance, and demonstrates how users can leverage them for better AI security practices.

🔍 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?#

  1. ✅ Adversarial Attacks: AI models face risks like prompt injection, model manipulation, and data poisoning, where attackers manipulate inputs to make the model behave unpredictably.

  2. ✅ Data Security & Privacy: Without proper safeguards, LLMs can unintentionally expose sensitive information or be manipulated to extract confidential data.

  3. ✅ Regulatory Compliance: Organizations deploying AI solutions must comply with security and ethical guidelines to ensure responsible AI use.

  4. ✅ Trust & Transparency: Frameworks provide guidelines for AI governance, ensuring users and businesses can trust AI outputs and prevent unintended consequences.

Let us now briefly run through the details of each of these frameworks and standards.

🛡️ MITRE ATLAS#

MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base and methodology that identifies and mitigates adversarial risks in AI systems. It is especially relevant for addressing vulnerabilities in generative AI and LLMs, such as ChatGPT and Bard.

Avenlis Copilot integrates MITRE ATLAS to enable users to:

  • Understand the tactics and techniques attackers use to compromise AI systems.

  • Identify weaknesses in AI architectures, including data poisoning and evasion attacks.

  • Implement real-world countermeasures to mitigate adversarial threats.

Note

MITRE ATLAS is widely regarded as a cornerstone resource for identifying and addressing real-world adversarial AI threats.

📌 Importance of MITRE ATLAS#

  1. Mapping Adversarial AI Attacks: Categorizes attack techniques used to compromise AI systems, including data poisoning, evasion attacks, and model inversion.

  2. Understanding AI-Specific Threats: Provides insights into vulnerabilities unique to AI and LLMs, ensuring security teams can anticipate and counteract threats.

  3. Defining Defensive Strategies: Offers a structured approach to detect, mitigate, and defend against adversarial manipulations.

  4. Standardizing AI Security Practices: Promotes industry-wide security methodologies, ensuring organizations align their defenses with global best practices.

  5. Enhancing AI Incident Response: Helps organizations respond effectively to AI security incidents by providing threat intelligence and mitigation guidance

🧠 Avenlis Copilot MITRE ATLAS Knowledge#

Here is what you can in Avenlis Copilot with regards to MITRE ATLAS:

  • 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.

See also

Learn more about MITRE ATLAS Tactics: https://atlas.mitre.org/tactics

Learn more about MITRE ATLAS Techniques: https://atlas.mitre.org/techniques

Learn more about MITRE ATLAS Mitigations: https://atlas.mitre.org/mitigations

Learn more about MITRE ATLAS Case Studies: https://atlas.mitre.org/studies

🛠️ OWASP Top 10 for LLMs 2025#

The OWASP Top 10 for LLMs is a widely recognized security framework that highlights the most critical vulnerabilities affecting LLM applications. These risks are based on real-world AI attacks and emerging trends, helping organizations prioritize AI security efforts. Addressing these threats is crucial for ensuring that AI systems remain secure, ethical, and reliable in various applications.

Taxonomy#

  1. (Link) LLM01:2025 Prompt Injection: One of the most pressing threats to LLMs, prompt injection occurs when attackers craft malicious inputs to manipulate model behavior. This can result in AI models generating unintended, misleading, or harmful outputs. Attackers can also use indirect prompt injection to override internal safety constraints.

  2. (Link) LLM02:2025 Sensitive Information Disclosure: This risk involves the unintended leakage of confidential or proprietary data by the LLM. Such disclosures can occur when the model inadvertently reveals sensitive information present in its training data or through interactions, leading to potential data breaches and privacy violations.

  3. (Link) LLM03:2025 Supply Chain: Threats originating from third-party dependencies or upstream model vulnerabilities. These vulnerabilities can be introduced through components such as pre-trained models, libraries, or datasets that the LLM relies on, potentially compromising the security and integrity of the AI system.

  4. (Link) LLM04:2025 Data and Model Poisoning: The injection of malicious data to corrupt the model’s performance or outputs. Attackers can manipulate the training data or the model itself, leading to biased or incorrect predictions, undermining the reliability and trustworthiness of the LLM.

  5. (Link) LLM05:2025 Improper Output Handling: Failure to sanitize outputs, resulting in potential harm or misuse. If the LLM’s outputs are not properly validated or sanitized, they can be exploited to perform malicious actions, spread misinformation, or trigger unintended behaviors in downstream applications.

  6. (Link) LLM06:2025 Excessive Agency: Over-reliance on LLMs for critical decision-making, leading to unintended consequences. Granting LLMs too much autonomy without adequate human oversight can result in decisions that are unethical, biased, or harmful, especially in sensitive applications.

  7. (Link) LLM07:2025 System Prompt Leakage: Exposure of system-level prompts or configurations that can compromise security. If internal prompts or system instructions are leaked, attackers can gain insights into the model’s behavior, potentially exploiting this knowledge to manipulate the LLM or extract sensitive information.

  8. (Link) LLM08:2025 Vector and Embedding Weakness: Exploitation of vulnerabilities in embedding layers and vectorization. Attackers can manipulate the input representations that the LLM uses, leading to incorrect outputs or enabling adversarial attacks that compromise the model’s integrity.

  9. (Link) LLM09:2025 Misinformation: The generation or propagation of misleading or harmful content. LLMs may produce outputs that are factually incorrect or deceptive, which can be exploited to spread false information, manipulate public opinion, or cause other forms of harm.

  10. (Link) LLM10:2025 Unbounded Consumption: Risks associated with resource exhaustion caused by unbounded input processing. Attackers can exploit the LLM’s processing capabilities by providing inputs that lead to excessive computational resource usage, potentially resulting in denial of service or degraded performance.

See also

Explore the OWASP Top 10 for LLMs 2025: https://genai.owasp.org/llm-top-10/

📌 Importance of OWASP Top 10 for LLMs#

  1. Identifying Critical AI Security Risks: Highlights the top vulnerabilities affecting LLM applications, such as prompt injection and system prompt leakage.

  2. Standardizing AI Security Best Practices: Provides structured recommendations for organizations to securely develop, deploy, and maintain AI applications.

  3. Improving AI Model Robustness: Encourages continuous testing and adversarial resilience to prevent data poisoning, misinformation, and unauthorized access.

  4. Guiding Compliance with AI Security Standards: Aligns LLM security with established cybersecurity frameworks, making AI adoption safer and more responsible.

  5. Mitigating the Risks of Uncontrolled AI Autonomy: Ensures AI does not operate without proper oversight, preventing excessive decision-making power and unintended consequences.

🧠 Avenlis Copilot OWASP Top 10 for LLMs Knowledge#

Here is what you can in Avenlis Copilot with regards to OWASP Top 10 for LLMs:

  1. Query OWASP Top 10 for LLMs Knowledge Base: Access detailed insights into LLM01 to LLM10, including vulnerability classifications, real-world attack scenarios, and security best practices.

  2. Identify Common Vulnerabilities & Risks: Learn about critical security threats in LLMs, such as Prompt Injection (LLM01), Sensitive Information Disclosure (LLM02), and Data Poisoning (LLM04), along with their potential impact.

  3. Explore Attack Patterns & Exploitation Methods: Understand how adversaries exploit LLM weaknesses using malicious prompt crafting, output manipulation, model inversion, and unauthorized API access techniques.

  4. Learn OWASP-Compliant Mitigation Strategies: Get step-by-step guidance on securing LLM applications, including input sanitization, model fine-tuning, adversarial testing, and system monitoring.

  5. Enhance Security Policies & Governance: Apply OWASP-recommended controls to improve LLM lifecycle security, data integrity, and compliance with AI safety frameworks, reducing the risk of unauthorized model manipulation or exploitation.

📊 NIST AI Risk Management Framework (RMF)#

The NIST AI Risk Management Framework (AI RMF) provides a structured approach to managing risks associated with artificial intelligence systems, particularly Large Language Models (LLMs). The framework is designed to help organizations assess, measure, mitigate, and govern AI risks across various stages of development and deployment. It ensures that AI systems are trustworthy, reliable, and aligned with regulatory and ethical standards

By applying the four core functions of NIST AI RMF—Map, Measure, Manage, and Govern—organizations can develop a proactive approach to AI risk management and strengthen AI security, fairness, and transparency.

1️⃣ Map: Identify Risks and Define Impacts#

The first step in AI risk management is to identify, categorize, and define potential risks that AI systems may introduce. This includes technical, ethical, and operational risks that could impact security, reliability, and compliance.

  1. Identifying AI Vulnerabilities: AI systems, particularly LLMs, are susceptible to security risks, such as Prompt Injection (LLM01), Data Poisoning (LLM04), and System Prompt Leakage (LLM07).

  2. Mapping AI Bias and Ethical Risks: Bias in AI models can lead to discriminatory outputs and decision-making errors, impacting fairness and accountability.

  3. Understanding Attack Vectors: Cyber adversaries exploit LLM vulnerabilities through adversarial attacks, exploiting weaknesses in prompt parsing, model training, and embedding structures.

  4. Assessing Compliance Gaps: Organizations must ensure AI applications comply with data protection laws (e.g., GDPR, CCPA) and industry standards for ethical AI governance.

By mapping these risks, organizations can prioritize areas requiring stronger security controls and governance mechanisms before AI systems are widely deployed.

2️⃣ Measure: Quantify Risks Through Analysis#

Once risks are identified, they must be measured and quantified based on their severity, likelihood, and potential impact on AI performance and security.

  1. Threat Modeling for LLMs: Organizations should conduct structured risk assessments to quantify the impact of data poisoning, misinformation generation (LLM09), or prompt injection attacks.

  2. Evaluating Trustworthiness Metrics: AI models should be tested against NIST-defined AI trustworthiness metrics, such as accuracy, robustness, explainability, and transparency.

  3. Impact Assessment on Business and Users: The cost of AI failures—such as AI-generated misinformation or biased decisions—should be measured against organizational policies and societal impact.

  4. Security Posture Monitoring: AI systems must be continuously audited and stress-tested to ensure they remain secure against evolving adversarial threats.

By measuring AI risks through structured risk assessment methodologies, organizations can develop data-driven strategies to mitigate vulnerabilities and enhance AI reliability.

3️⃣ Manage: Develop and Deploy Strategies to Mitigate Risks#

Once risks are mapped and measured, organizations must develop and implement AI-specific risk mitigation strategies to ensure secure, ethical, and resilient AI deployments.

  1. Adversarial Robustness Testing: AI models should undergo security hardening against prompt injection (LLM01), data poisoning (LLM04), and supply chain vulnerabilities (LLM03) to prevent adversarial manipulation.

  2. Bias and Fairness Optimization: AI models should be fine-tuned to reduce bias and improve fairness, ensuring they produce trustworthy and non-discriminatory outputs.

  3. Output Control Mechanisms: Automated validation checks should be implemented to prevent LLM-generated misinformation (LLM09) or improper output handling (LLM05).

  4. Security Best Practices Implementation: Organizations should enforce AI security policies aligned with NIST, OWASP, and ISO 27001 to ensure compliance and long-term AI security maintenance.

Effective risk management requires continuous monitoring, model fine-tuning, and adversarial resilience techniques to ensure that AI remains reliable, secure, and ethical.

4️⃣ Govern: Implement Oversight for Sustained Risk Management#

The final stage in AI risk management involves governing AI systems through regulatory oversight, policy enforcement, and continuous auditing. This ensures that AI systems remain secure, compliant, and aligned with evolving AI risk standards.

  1. AI Governance Policies & Frameworks: Organizations must implement AI governance strategies that define roles, responsibilities, and risk accountability measures across development and deployment teams.

  2. Continuous AI Risk Monitoring: AI risks evolve over time, requiring real-time monitoring and regular AI security audits to detect new threats and vulnerabilities.

  3. Regulatory Compliance & Legal Considerations: AI-driven enterprises must align their models with legal frameworks, including GDPR, CCPA, and AI regulatory guidelines, to avoid compliance risks.

  4. Transparency and Explainability Standards: Organizations must ensure AI decision-making processes are transparent by documenting model behavior, training data sources, and decision logic.

Through effective governance, AI models can be continuously evaluated, improved, and safeguarded to prevent unintended consequences and ensure responsible AI deployment.

🧠 Avenlis Copilot NIST AI RMF Knowledge#

Here is what you can in Avenlis Copilot with regards to NIST AI RMF:

  1. Query NIST AI RMF Knowledge Base: Access comprehensive insights into the four core functions (Map, Measure, Manage, and Govern), including risk identification methodologies, compliance strategies, and AI security governance best practices.

  2. Identify AI Security Risks & Compliance Gaps: Learn about critical AI vulnerabilities, such as model bias, adversarial attacks, lack of transparency, and security misconfigurations, and understand their impact on AI trustworthiness, robustness, and ethical decision-making.

  3. Explore Risk Quantification & Threat Modeling: Understand how organizations assess and measure AI risks using structured frameworks, including risk scoring models, compliance benchmarks, and security validation techniques.

  4. Learn NIST-Compliant AI Security & Risk Mitigation Strategies: Get step-by-step guidance on securing AI applications, including adversarial testing, risk reduction techniques, governance implementation, and ethical AI deployment standards.

  5. Enhance AI Governance, Oversight & Continuous Risk Monitoring: Apply NIST AI RMF-aligned controls to improve long-term AI security resilience, ensure regulatory compliance (e.g., GDPR, CCPA, AI Act), and implement governance models to safeguard AI decision-making.

See also

Learn more about the NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework

🚀 Start taking control of your AI Security and Red Teaming now