AI Security Audit Frameworks: OWASP LLM Top 10, MITRE ATLAS, More

Every AI deployment needs a security review. The question isn’t whether to do one — it’s which framework to structure it around. The AI security framework landscape has exploded in the past two years, and the options range from OWASP’s practitioner-focused list to NIST’s comprehensive risk management tome to half a dozen commercial equivalents.

We took four common deployment scenarios — a customer-facing chatbot, an internal code assistant, a medical document summarizer, and an autonomous agent with tool access — and ran each through the major frameworks. Here’s what we found about coverage, gaps, and practical applicability.

The Frameworks

OWASP LLM Top 10

OWASP’s LLM Top 10 is the most accessible framework in the category. It lists ten vulnerability classes in priority order: prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft.

Strengths:

• Immediately actionable: each item has clear descriptions, examples, and mitigation guidance
• Attack-focused: written by practitioners for practitioners
• Widely adopted: maps well to what security teams already understand
• Free and community-maintained

Gaps:

• Limited coverage of model-level threats (weights, training pipeline)
• No risk quantification methodology (all items equally weighted)
• Evolves slowly relative to the attack landscape
• Not designed for certification or compliance reporting

Best for: Initial security reviews, developer security training, backlog creation for remediation.

For our chatbot scenario, OWASP LLM Top 10 gave us a complete and actionable checklist. For the autonomous agent scenario, items 8 (Excessive Agency) and 7 (Insecure Plugin Design) were directly applicable but the framework didn’t address the compounding risks of multi-step agent chains.

MITRE ATLAS

MITRE ATLAS (Adversarial Threat Landscape for AI Systems) is the most technically comprehensive framework. It maps adversarial ML attacks — reconnaissance, resource development, initial access, execution, persistence, privilege escalation, lateral movement, collection, exfiltration, and impact — against AI/ML systems using the familiar MITRE ATT&CK structure. For the specific tools that address the adversarial ML techniques ATLAS documents, see our adversarial ML defense tools review.

Strengths:

• Excellent attack taxonomy: covers attack techniques that no other framework addresses (model inversion, membership inference, adversarial examples in detail)
• Maps to ATT&CK for hybrid threat modeling (combines AI-specific and traditional TTPs)
• Backed by MITRE’s research credibility
• Growing real-world case study library

Gaps:

• Heavy: designed for threat intelligence and SOC use, not necessarily for dev-time security review
• Less actionable for developers: describes what attackers do, less prescriptive about defenses
• Limited coverage of LLM-specific threats (prompt injection taxonomy is shallower than OWASP)

Best for: Red-teaming exercises, threat intelligence, mapping AI-specific attacks to your existing security monitoring.

For our medical summarizer scenario, ATLAS provided attack paths that OWASP doesn’t cover — specifically, model inversion attacks that could reconstruct training data (containing patient records) from model outputs. That’s a real threat for medical AI that OWASP’s top 10 underweights.

More at AI Attacks ↗ for ATLAS walkthrough and case studies.

NIST AI Risk Management Framework (AI RMF)

The NIST AI RMF is the most comprehensive and the most process-oriented. It organizes AI risk management into four functions (GOVERN, MAP, MEASURE, MANAGE) and provides detailed guidance on each. The 2024 Generative AI profile adds LLM-specific risk categories.

Strengths:

• Comprehensive: covers the entire lifecycle from development through deployment and monitoring
• Strong on governance: useful for organizations building AI risk programs, not just individual audits
• Regulatory alignment: increasingly referenced by US federal guidance and sector-specific regulations
• Includes the GenAI profile addressing LLM-specific risks

Gaps:

• Not attack-focused: describes risk categories, not attack techniques
• Significant investment to implement fully: not appropriate for one-off security reviews
• Prescriptive about process, less prescriptive about technical controls
• Very long

Best for: Enterprise AI governance programs, regulatory compliance, board-level risk reporting.

For our compliance-sensitive medical scenario, NIST AI RMF was the most appropriate framework for building a governance program. For a fast technical audit, it’s too heavy.

EU AI Act Risk-Based Framework

The EU AI Act’s risk-based classification doesn’t provide a security framework per se, but it defines obligations that imply specific security requirements for high-risk systems.

High-risk obligations relevant to security:

• Technical robustness and security (Article 15): must be accurate, robust, and secure; must be resilient to adversarial attacks
• Data governance (Article 10): training data quality and protection requirements
• Logging and traceability (Article 12): automatic logging of events
• Human oversight (Article 14): ability to override and monitor AI decisions
• Cybersecurity (Annex IV): documentation of security testing

Practical implication: If your system is high-risk under the EU AI Act (medical, employment, critical infrastructure, biometrics, law enforcement), you need to demonstrate security controls that map to these obligations. Using OWASP LLM Top 10 + NIST AI RMF together gives you reasonable coverage of what regulators will look for. For data-privacy-specific tooling that maps to these compliance requirements, see our AI privacy and data security tools review.

AI Privacy Report ↗ tracks EU AI Act enforcement developments and compliance interpretations.

Commercial Frameworks

Protect AI’s ModelScan: A tool-forward approach to supply chain security and model scanning. Less a framework and more an automated audit component. Best used alongside a broader framework.

Robust Intelligence Test Coverage Report: Robust Intelligence’s platform generates a structured audit report covering distribution shift, adversarial robustness, and bias. The output format is closer to a financial audit report than a security framework — useful for presenting results to non-technical stakeholders.

Wiz AI Security Posture Management (AIPM): Wiz extended their cloud security platform to AI workloads. Their AIPM framework focuses on cloud-level misconfigurations specific to AI deployments: exposed model endpoints, training data bucket permissions, MLflow server exposure, Jupyter notebook security. Less focused on AI-specific threats (prompt injection, adversarial examples) and more focused on traditional cloud security applied to AI infrastructure.

Best for: Wiz AIPM fills a real gap — cloud security teams doing AI infrastructure reviews who want to extend their existing tooling. Doesn’t replace OWASP or MITRE ATLAS for application-level AI security.

Comparison by Scenario

Customer-facing chatbot:

• Primary: OWASP LLM Top 10 (prompt injection, insecure output handling, excessive agency)
• Supplement with: Robust Intelligence for runtime monitoring
• Compliance: EU AI Act (likely limited risk, verify classification)

Internal code assistant:

• Primary: OWASP LLM Top 10 (sensitive data in context, supply chain, training data)
• Supplement with: MITRE ATLAS (data exfiltration, insider threat modeling)
• Cloud security: Wiz AIPM for deployment infrastructure

Medical document summarizer (regulated):

• Primary framework: NIST AI RMF (governance program)
• Technical audit: OWASP LLM Top 10 + MITRE ATLAS
• Regulatory: EU AI Act compliance (high-risk system)
• Model security: Protect AI ModelScan, HiddenLayer for runtime

Autonomous agent with tool access:

• Primary: OWASP LLM Top 10 items 7-8 (Excessive Agency, Insecure Plugin Design)
• MITRE ATLAS for multi-step attack paths
• No framework covers this scenario well — the tooling is still catching up to the threat model

For the autonomous agent scenario, none of the frameworks provided complete coverage. We recommend tracking emerging work from AI Defense ↗ and AI Sec Blog ↗ where practitioners are documenting agent-specific attack paths in real time.

Our Recommendation

For a single-framework recommendation: Start with OWASP LLM Top 10 for actionable coverage of the most common attack vectors. Add MITRE ATLAS if your team is doing serious red-teaming. Add NIST AI RMF if you’re building an enterprise governance program or face regulatory requirements.

For regulated industries (financial services, healthcare, critical infrastructure): Treat the EU AI Act and NIST AI RMF as compliance floors, not ceilings. OWASP and ATLAS handle the technical depth the standards don’t reach.

The AI Incidents ↗ tracker is a useful calibration resource — reviewing real incidents against these frameworks quickly shows which categories generate the most actual harm.

For more context, AI content moderation tools ↗ covers related topics in depth.