OWASP Top 10 for LLM Applications - identification, exploitation patterns, and remediation
A specialized cybersecurity discipline focused on systematically identifying, exploiting, and remediating the ten most critical security vulnerabilities in applications powered by Large Language Models (LLMs), as defined by the OWASP Foundation.
This skill is critical because LLMs are increasingly integrated into core business products, and their unique attack surfaces (prompt injection, data poisoning, insecure output handling) create novel, high-impact risks. Organizations value practitioners who can proactively secure these AI assets to prevent data breaches, reputational damage, and financial loss, directly protecting the bottom line.
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9.1 Avg Demand
18%
Avg AI Risk
How to Learn OWASP Top 10 for LLM Applications - identification, exploitation patterns, and remediation
1. Master the official OWASP Top 10 for LLM Applications list, definitions, and examples. 2. Understand foundational concepts: LLM architecture (transformers, embeddings, APIs), common deployment patterns (RAG, agents), and core security principles (CIA triad). 3. Learn basic Python scripting and familiarize yourself with common LLM APIs (OpenAI, Hugging Face) to understand input/output flows.
1. Transition to hands-on exploitation using controlled environments (e.g., OWASP's WebGoat for LLMs, private test apps). 2. Practice crafting attack payloads for specific vulnerabilities like prompt injection (direct/indirect) and insecure output handling. 3. Develop skills in security code review for LLM-specific code, focusing on input validation, output encoding, and API configuration. A common mistake is focusing only on prompt-based attacks and neglecting traditional application security (SSRF, IDOR) that also affect LLM apps.
1. Architect secure LLM deployment patterns (e.g., designing secure RAG pipelines with guardrails, implementing principle of least privilege for LLM tools/plugins). 2. Lead threat modeling sessions for LLM-integrated systems, aligning findings with business risk. 3. Develop organization-wide standards, playbooks, and secure coding guidelines for LLM development. Master the ability to communicate technical LLM risks to non-technical stakeholders.
Practice Projects
Beginner
Project
Build a Vulnerable LLM Chatbot and Identify Flaws
Scenario
You are tasked with creating a simple customer service chatbot using an LLM API. The goal is to intentionally introduce common vulnerabilities from the OWASP Top 10 list and then document how to identify them.
How to Execute
1. Set up a basic Python/Flask web app integrating an LLM API. 2. Intentionally implement flaws: no input sanitization (enabling prompt injection), leaking system prompts, no output filtering (allowing insecure output). 3. Use manual testing (crafting malicious prompts) and simple scripts to identify and document each vulnerability according to OWASP categories. 4. Write a findings report that maps each issue to the specific OWASP LLM Top 10 item.
Intermediate
Project
Remediate a RAG Application Vulnerability
Scenario
A retrieval-augmented generation (RAG) application for internal document search is suspected of being vulnerable to data poisoning and insecure output handling. You must secure it.
How to Execute
1. Audit the vector database and document ingestion pipeline for poisoning vectors (e.g., malicious document injection). 2. Implement data validation and provenance checks for ingested content. 3. Add output sanitization and context-aware encoding to prevent XSS in rendered LLM responses. 4. Implement and test guardrails (e.g., using a framework like Guardrails AI or Rebuff) to filter toxic, biased, or off-topic outputs before they reach the user. Document the before/after security posture.
Advanced
Case Study/Exercise
Lead a Threat Model for an Autonomous LLM Agent
Scenario
Your organization is deploying an LLM-based agent that can execute tasks like sending emails, querying databases, and making API calls. You are the lead security architect responsible for a pre-launch threat modeling exercise.
How to Execute
1. Facilitate a STRIDE/PASTA-based threat modeling workshop with developers and product owners. 2. Map the agent's tool use, data flows, and trust boundaries to identify compound risks (e.g., an indirect prompt injection leading to unauthorized database queries via the agent). 3. Prioritize risks based on likelihood and business impact. 4. Design and document mitigation strategies, such as human-in-the-loop confirmation for high-risk actions, granular tool permissions, and comprehensive logging/auditing. Produce a final report with actionable security requirements.
Tools & Frameworks
Software & Platforms
OWASP WebGoat for LLMsHugging Face Transformers libraryLangChain / LlamaIndex (with security modules)Burp Suite with LLM plugins
Use WebGoat for safe, vulnerable practice environments. Transformers for understanding model internals. LangChain/LlamaIndex for building and analyzing RAG/agent codebases. Burp Suite for intercepting and manipulating LLM API traffic during pentesting.
Security Frameworks & Methodologies
OWASP LLM Top 10 (2025)STRIDE Threat ModelingNIST AI Risk Management Framework (AI RMF)MITRE ATLAS (Adversarial Threat Landscape for AI Systems)
Apply the OWASP list as your core checklist. Use STRIDE to systematically model threats for complex LLM systems. Leverage NIST AI RMF for organizational governance. Reference MITRE ATLAS for understanding adversary TTPs specific to AI systems.
Interview Questions
Answer Strategy
The interviewer is testing practical exploitation knowledge and understanding of the vulnerability chain. Use the OWASP definition as a base. A strong answer explains the attack vector, demonstrates impact, and connects to remediation. Sample: 'Insecure Output Handling occurs when the application passes raw LLM output to a frontend or backend system without proper sanitization. I would test by crafting prompts that instruct the LLM to output JavaScript, SQL, or system commands. For a web report, I'd aim for stored XSS by getting the LLM to output a script tag in a field that gets rendered without encoding. The exploitation chain is: malicious prompt -> LLM generates malicious payload -> application renders payload -> user's browser executes script. Remediation involves implementing context-aware output encoding (e.g., HTML entity encoding for web) and treating all LLM output as untrusted user input.'
Answer Strategy
This tests the ability to proactively identify high-impact risks and communicate them effectively. The core competency is security architecture and risk prioritization. Sample: 'I would focus on three compound risks from the OWASP Top 10 for LLMs. First, **Excessive Agency (LLM07)**: ensuring the LLM cannot perform unauthorized actions on the database, like deletion or mass export. This requires strict API scoping and human-in-the-loop confirmation. Second, **Sensitive Information Disclosure (LLM06)**: preventing the LLM from leaking PII or internal data in its responses via indirect prompt injection. This requires robust output filtering and data leakage prevention policies. Third, **Insecure Output Handling (LLM05)**: ensuring any data pulled from the database and rendered by the LLM is sanitized to prevent injection attacks like XSS or SQLi. My proposal would be to implement these as non-functional requirements from day one.'
Careers That Require OWASP Top 10 for LLM Applications - identification, exploitation patterns, and remediation