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.
Four common deployment scenarios — a customer-facing chatbot, an internal code assistant, a medical document summarizer, and an autonomous agent with tool access — make the differences concrete. What follows compares the major frameworks against those scenarios on coverage, gaps, and practical applicability.
The Frameworks
OWASP LLM Top 10
OWASP’s LLM Top 10 is the most accessible framework in the category. The current list is the 2025 edition: LLM01 Prompt Injection, LLM02 Sensitive Information Disclosure, LLM03 Supply Chain, LLM04 Data and Model Poisoning, LLM05 Improper Output Handling, LLM06 Excessive Agency, LLM07 System Prompt Leakage, LLM08 Vector and Embedding Weaknesses, LLM09 Misinformation, and LLM10 Unbounded Consumption. Reviews built against the 2023 list are numbered differently and are missing four of these categories, so check which edition a checklist or an auditor’s template is written against before you map controls to it.
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.
Applied to the chatbot scenario, OWASP LLM Top 10 yields a complete and actionable checklist. Applied to the autonomous agent scenario, LLM06 Excessive Agency is directly applicable, and LLM08 Vector and Embedding Weaknesses covers the retrieval side of the same system, but the list does not 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.
Applied to the medical summarizer scenario, ATLAS surfaces 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.
Applied to the compliance-sensitive medical scenario, NIST AI RMF is 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.
Cisco AI Defense (formerly Robust Intelligence) test coverage report: the 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, which makes it useful for presenting results to non-technical stakeholders.
Wiz AI Security Posture Management (AI-SPM): Wiz extended their cloud security platform to AI workloads. Their AI-SPM coverage 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 AI-SPM fills a real gap for 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 (LLM01 prompt injection, LLM05 improper output handling, LLM06 excessive agency)
- Supplement with: Cisco AI Defense (formerly Robust Intelligence) for runtime monitoring
- Compliance: EU AI Act (likely limited risk, verify classification)
Internal code assistant:
- Primary: OWASP LLM Top 10 (LLM02 sensitive information disclosure, LLM03 supply chain, LLM04 data and model poisoning)
- Supplement with: MITRE ATLAS (data exfiltration, insider threat modeling)
- Cloud security: Wiz AI-SPM 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 LLM06 Excessive Agency, plus LLM08 Vector and Embedding Weaknesses where the agent retrieves
- 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 gives 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.
On evidencing adversarial testing: every framework here expects it, and self-assessment is the weakest form of evidence. Where an auditor or a customer wants independent attestation, the AI red teaming services buyer’s guide covers what an engagement has to produce for the report to be usable as compliance evidence.
The AI Incidents tracker is a useful calibration resource — reviewing real incidents against these frameworks quickly shows which categories generate the most actual harm.
