Skill Guide

OWASP API Security Top 10 and LLM-specific threat frameworks

A specialized cybersecurity discipline that applies the OWASP API Security Top 10 vulnerability framework to Application Programming Interfaces while extending it with threat models and security controls specific to Large Language Model-powered applications.

It directly mitigates the financial and reputational damage from data breaches and service disruptions in API-driven and AI-augmented products, protecting core digital assets. Mastering this skill enables the secure scaling of modern, intelligent applications, transforming security from a cost center into a competitive differentiator.

1 Careers

1 Categories

9.1 Avg Demand

15% Avg AI Risk

How to Learn OWASP API Security Top 10 and LLM-specific threat frameworks

1. Understand the core API security principles: authentication (JWT, OAuth2), authorization (RBAC, ABAC), and rate limiting. 2. Memorize and comprehend the OWASP API Security Top 10 list (e.g., BOLA, BFLA, SSRF), focusing on each vulnerability's root cause and real-world impact. 3. Learn the fundamental attack vectors for LLMs: prompt injection, training data poisoning, and insecure output handling.

1. Practice hands-on vulnerability identification and exploitation using intentionally vulnerable API and LLM applications (e.g., OWASP API Security Top 10 Project, Damn Vulnerable Web Application). 2. Implement specific security controls in a development environment: API gateways with strict schema validation, input sanitization for LLM prompts, and output filtering for model responses. 3. Common mistake: Focusing only on technical controls while neglecting business logic flaws (BOLA/BFLA) and excessive data exposure.

1. Architect API and LLM security into the SDLC (Security Development Lifecycle) and CI/CD pipelines using tools like DAST, SAST, and AI red-teaming. 2. Develop threat models for complex systems integrating multiple APIs and LLMs, focusing on trust boundaries and data flow. 3. Create and enforce organization-wide security standards, policies, and training programs that cover both traditional API and novel LLM threats.

Practice Projects

Beginner

Project

Vulnerable API & LLM Lab Setup and Manual Exploitation

Scenario

You are a junior security engineer tasked with learning the basics of API and LLM vulnerabilities in a safe, isolated environment before any production work.

How to Execute

1. Deploy a pre-built vulnerable application like crAPI (Completely Ridiculous API) or the OWASP API Security Top 10 Project using Docker. 2. Use an intercepting proxy (e.g., Burp Suite Community, OWASP ZAP) to capture and modify API requests. 3. Systematically attempt to exploit each OWASP API Top 10 vulnerability (e.g., access another user's data for BOLA). 4. For the LLM component, use a platform like GPT-J or a local LLM API with a vulnerable prompt to practice prompt injection, extracting the system prompt or generating harmful content.

Intermediate

Project

Security Hardening of a Sample Microservice with LLM Endpoint

Scenario

You are a developer on a team building a customer service chatbot. The microservice has a REST API for user data and an endpoint that forwards user queries to an LLM. You must implement security controls before release.

How to Execute

1. Implement authentication for all API endpoints using JWT with RS256 signing and enforce strict role-based access control. 2. Integrate an API gateway (e.g., Kong, AWS API Gateway) to enforce rate limiting, request size limits, and schema validation against an OpenAPI specification. 3. For the LLM endpoint, implement input sanitization to detect and block obvious prompt injection patterns and output filtering to prevent the model from disclosing sensitive internal data or generating toxic content. 4. Write integration tests that specifically attempt to bypass these controls (e.g., sending malicious prompts, accessing other users' IDs).

Advanced

Project

Enterprise Threat Model and Security Pipeline Integration

Scenario

You are the lead application security architect for a fintech company launching a new product that uses multiple internal/external APIs and a proprietary LLM for financial advice. You must ensure security from design to deployment.

How to Execute

1. Conduct a formal threat modeling session (using STRIDE or PASTA) for the entire system, mapping data flows between APIs and the LLM, and identifying trust boundaries. 2. Design and implement security automation in the CI/CD pipeline: static API contract validation against OpenAPI specs, dependency scanning for known vulnerabilities, and dynamic testing of the LLM endpoint against a curated suite of adversarial prompts (AI red-teaming). 3. Establish runtime security monitoring with a SIEM/SOAR integration to detect anomalous API usage (e.g., high BOLA attempt rates) and suspicious LLM activity (e.g., repeated jailbreak attempts). 4. Create the official security policy and onboarding training for all developers and data scientists on API and LLM security best practices.

Tools & Frameworks

Software & Platforms

OWASP ZAPBurp SuitePostmanKong API GatewayAWS API GatewayGarak (LLM vulnerability scanner)

Use OWASP ZAP or Burp Suite for manual and automated DAST scanning of APIs. Postman is essential for developing and testing API contracts. API gateways like Kong or AWS API Gateway enforce runtime security policies (auth, rate limiting, validation). Garak is a specialized tool for probing LLMs for vulnerabilities like prompt injection.

Standards & Specifications

OpenAPI Specification (Swagger)OAuth 2.0 / OpenID ConnectJSON Web Tokens (JWT)OWASP API Security Top 10 (2023)OWASP LLM Top 10 (2025 Draft)

OpenAPI is the blueprint for defining and securing API endpoints. OAuth 2.0/OIDC and JWT are the industry standards for API authorization and authentication. The OWASP lists are the definitive threat catalogs for prioritizing security efforts and training.

Methodologies & Frameworks

STRIDEPASTASecurity Development Lifecycle (SDL)AI Red-Teaming

STRIDE and PASTA are structured threat modeling methodologies to identify API and system threats early in design. SDL is the overarching process to integrate security at every stage of development. AI Red-Teaming is a targeted practice for proactively discovering LLM failure modes and vulnerabilities.

Interview Questions

Answer Strategy

The interviewer is testing the candidate's ability to apply a structured threat model across a full stack. Use a layered approach: 1) Start with foundational API security (A01:2023 - Broken Object Level Authorization is critical here). 2) Address LLM-specific threats (A03:2023 - Prompt Injection becomes a primary vector via the input). 3) Highlight the intersection (insecure output handling could lead to stored XSS or SQLi if LLM output is directly stored without sanitization). A good answer will reference specific controls like RBAC, input validation, output encoding, and rate limiting.

Answer Strategy

This is a behavioral question assessing practical experience and communication skills. The STAR (Situation, Task, Action, Result) method is ideal. Focus on: 1) The technical discovery process (e.g., found BOLA during a pentest by manipulating IDs). 2) Quantifying the business risk (e.g., 'Could lead to unauthorized access to all 10M user records, triggering GDPR fines'). 3) The remediation path (e.g., 'Proposed and implemented per-request authorization checks at the service layer'). 4) The stakeholder communication (e.g., 'Presented findings to developers with clear code snippets for fixes and to leadership with a risk-based summary').