Skill Guide

OWASP Top 10 for LLM Applications and emerging AI security standards

A structured risk framework identifying the most critical security vulnerabilities specific to Large Language Model (LLM) applications, combined with evolving regulatory and technical standards for securing AI systems.

This skill mitigates critical business risks including data poisoning, model extraction, and prompt injection attacks that can lead to financial loss, reputational damage, and regulatory non-compliance. It enables secure AI deployment, protects intellectual property, and ensures alignment with emerging AI governance frameworks like the EU AI Act and NIST AI RMF.

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9.2 Avg Demand

15% Avg AI Risk

How to Learn OWASP Top 10 for LLM Applications and emerging AI security standards

Focus on: 1) Understanding the OWASP Top 10 for LLMs list (e.g., LLM01: Prompt Injection, LLM04: Model Denial of Service). 2) Basic AI security concepts: data poisoning, model inversion, adversarial attacks. 3) Reviewing foundational standards: NIST AI Risk Management Framework (AI RMF) and ISO/IEC 23894:2023.

Transition from theory by applying security controls to specific attack vectors in controlled environments. Study common implementation mistakes such as over-reliance on LLM output without validation, improper input sanitization, and insecure plugin architectures. Practice threat modeling for LLM-powered applications using frameworks like STRIDE adapted for AI.

Mastery involves designing organization-wide AI security governance, integrating AI risk into enterprise risk management, and developing custom security testing pipelines for LLMs. This includes strategic alignment with legal/compliance teams on AI regulations, mentoring security engineers on AI-specific vulnerabilities, and contributing to or implementing industry standards like MITRE ATLAS.

Practice Projects

Beginner

Project

Basic LLM Vulnerability Assessment

Scenario

You are tasked with security testing a simple chatbot application powered by an API-based LLM.

How to Execute

1. Set up a test environment with a mock LLM API. 2. Systematically test for OWASP LLM01 (Prompt Injection) using curated attack prompts from resources like OWASP's testing guide. 3. Attempt LLM06 (Sensitive Information Disclosure) by trying to extract training data or system prompts. 4. Document findings with severity ratings and basic mitigation recommendations.

Intermediate

Project

Secure LLM Pipeline Implementation

Scenario

Develop a secure RAG (Retrieval-Augmented Generation) pipeline for internal knowledge base queries that must protect sensitive corporate data.

How to Execute

1. Design input validation and sanitization layers to prevent prompt injection (LLM01). 2. Implement strict output filtering and grounding mechanisms to prevent hallucinations and data leakage (LLM06, LLM09). 3. Configure access controls and audit logs for the vector database and embedding models. 4. Conduct red teaming exercises focused on data exfiltration through indirect prompt injection.

Advanced

Case Study/Exercise

AI Security Incident Response Playbook Development

Scenario

Your organization's flagship LLM-powered customer service tool is exhibiting signs of adversarial manipulation, leading to brand damage and potential data leaks.

How to Execute

1. Lead a cross-functional incident response team (security, legal, PR, AI engineering). 2. Utilize threat intelligence to identify the attack pattern (e.g., coordinated prompt injection campaigns). 3. Develop and execute a containment strategy involving model rollback, input filtering updates, and user communication. 4. Post-incident, architect a comprehensive security enhancement roadmap aligned with NIST AI RMF and prepare regulatory compliance reports.

Tools & Frameworks

Security Testing & Red Teaming Tools

Garak (LLM vulnerability scanner)OWASP ZAP with LLM pluginsMicrosoft Counterfit

Use these tools for automated and manual security testing of LLM endpoints and applications. Garak is specifically designed for probing LLM behaviors, while ZAP can be adapted for API security testing of LLM backends.

Governance & Compliance Frameworks

NIST AI Risk Management Framework (AI RMF)ISO/IEC 42001:2023 (AI Management System)EU AI Act Risk Classification

Apply these frameworks for structural governance. NIST AI RMF provides a comprehensive risk management lifecycle. ISO 42001 offers a certifiable management system standard. The EU AI Act sets a legal baseline for high-risk AI systems in Europe.

Technical Mitigation Libraries

LangChain GuardrailsNeMo GuardrailsHugging Face Transformers Safety Modules

Integrate these libraries directly into application code to implement guardrails for input validation, output filtering, and topic control. They provide pre-built or customizable rules to block malicious inputs and safe outputs.

Interview Questions

Answer Strategy

The interviewer is testing systematic thinking and methodology. Use a structured framework like STRIDE or PASTA, adapted for AI. Sample answer: 'I would begin by decomposing the system into components: user input interface, LLM backend, vector database, and output renderer. For each, I apply the STRIDE model-e.g., for the input interface, I assess Spoofing (impersonating a user), Tampering (prompt injection), and Information Disclosure (sensitive query leaks). I then prioritize risks using DREAD, focusing first on LLM01 (Prompt Injection) and LLM06 (Data Leakage) given the sensitive document context.'

Answer Strategy

This tests business acumen, risk communication, and technical guidance. Sample answer: 'I would clearly outline the specific OWASP Top 10 risks this exposes, particularly model theft (LLM10), training data poisoning (LLM04), and excessive agency (LLM08). I would advocate for a phased approach: first, implementing robust input/output guardrails and monitoring as an immediate mitigation; second, conducting a focused security assessment; and third, planning for safety alignment fine-tuning with curated datasets, referencing standards like NIST AI RMF for a risk-based roadmap.'