OWASP, MITRE ATLAS, and NIST AI RMF look at AI security from three different perspectives. An overview of what each framework stands for — and how, in combination, they form a viable protection concept.
Classic security approaches only go so far with LLM-based systems: prompts, context data, data pipelines, and agentic logic become risk points in their own right, and non-deterministic model behavior eludes static analysis and signatures. Three frameworks have become established points of reference — each with its own perspective: the OWASP GenAI Security Project's Top 10 for LLM Applications provides the testing and requirements perspective for development and security teams, MITRE ATLAS catalogs the tactics and techniques of real-world attackers, and the NIST AI RMF structures overarching risk management. Together they cover the entire AI lifecycle — from secure building through adversarial testing to governance. This interplay also matters from a regulatory standpoint: the EU AI Act, NIS2, DORA, and the GDPR require demonstrable security measures for AI systems.
The OWASP GenAI Security Project's catalog names the most critical vulnerability classes in LLM applications — from Prompt Injection (LLM01:2025) through Sensitive Information Disclosure (LLM02) and Supply Chain (LLM03) to Excessive Agency (LLM06). Six of the ten risks directly concern data security. Development and AppSec teams use the list as a testing and requirements perspective when building and testing LLM systems.
MITRE ATLAS catalogs real-world attack tactics and techniques against AI systems — analogous to the ATT&CK matrix, with tactics from reconnaissance through resource development to exfiltration and technique IDs such as AML.T0051. The framework consistently takes the attacker's perspective, making it the foundation for red and purple teaming against AI systems.
The NIST AI Risk Management Framework structures risk management across the entire AI lifecycle along the four core functions GOVERN, MAP, MEASURE, and MANAGE. It addresses trustworthiness and governance and is aimed at CISOs and risk management — as a framework in which technical findings are translated into policies, processes, and responsibilities.
The three frameworks do not compete — they complement each other: OWASP supports secure development (developers, DevSecOps), MITRE ATLAS supports adversarial validation (red/purple teams), and NIST AI RMF anchors governance and risk (CISO, risk management). Concrete measures can be mapped to all three — input validation and prompt filtering, for example, to LLM01, AML.T0051, and the MAP/MANAGE functions.
The sequence Assess, Test, Govern, Protect has proven effective: first, an inventory of all LLM deployments and the current security status; then adversarial testing against the OWASP Top 10 and MITRE ATLAS tactics; followed by building AI governance based on the NIST AI RMF. The final step is the continuous implementation of layered security measures in ongoing operations.
For tool selection, the OWASP GenAI Security Project maintains a vendor-neutral Solutions Landscape, updated quarterly: open-source and commercial solutions are classified along the LLMSecOps lifecycle phases — from Scope & Plan through Test & Evaluate to Govern — and mapped to the Top 10 risks. A dedicated landscape for agentic architectures has existed since Q3 2025; the associated risks are described in the OWASP Top 10 for Agentic Applications 2026.
The content on this page is based on the following publicly available guides and studies.
Vendor-neutral mapping of open-source and commercial security solutions to the Top 10 risks along the LLMOps lifecycle phases from Scoping/Planning to Govern.
Compact overview of the LLM/GenAI security solutions landscape, including the LLM and Gen AI App SecOps Framework.
Maps DevOps and SecOps tasks as well as solutions to the phases of the agentic AI lifecycle from Scope & Plan to Govern; updated quarterly.
VamiSec supports you with AI and LLM pentests along the OWASP Top 10 and MITRE ATLAS, and with consulting on building your AI governance based on the NIST AI RMF. Schedule a no-obligation initial consultation.
