OWASP Top 10 for LLM Applications
The definitive list of critical security risks in LLM-based applications. A practical guide for developers and security teams building with large language models.
Overview
The OWASP Top 10 for Large Language Model Applications identifies the most critical security vulnerabilities in applications that use LLMs. First released in 2023 and updated in 2025, it provides a practical framework for understanding and mitigating risks specific to LLM integrations. Unlike traditional OWASP Top 10 (focused on web application vulnerabilities), this list addresses AI-specific attack vectors including prompt injection, training data poisoning, and excessive agency granted to AI agents.
Key Requirements
The core elements your organization needs to address.
Prompt Injection
Attackers craft inputs that override system instructions or manipulate the LLM's behavior. This includes direct prompt injection (user-supplied prompts) and indirect prompt injection (injecting instructions via external data sources the LLM processes). Defenses include input validation, privilege separation, and output monitoring.
Insecure Output Handling
LLM outputs are treated as trusted without proper validation or sanitization. This can lead to XSS, SSRF, privilege escalation, or remote code execution when LLM output is passed directly to backend functions, APIs, or rendered in browsers. All LLM outputs must be treated as untrusted user input.
Training Data Poisoning
Manipulation of training data or fine-tuning data to introduce vulnerabilities, backdoors, or biases into the model. This includes poisoning public datasets, supply chain attacks on training pipelines, and targeted data insertion to influence model behavior on specific inputs.
Model Denial of Service
Attacks that consume disproportionate computational resources through crafted inputs. This includes inputs designed to maximize token generation, recursive prompt patterns, and resource-exhaustive queries. Rate limiting, input size constraints, and resource monitoring are key mitigations.
Supply Chain Vulnerabilities
Risks from third-party components including pre-trained models, training datasets, plugins, and extensions. This covers compromised model weights, malicious fine-tuning, vulnerable dependencies in the ML pipeline, and risks from model marketplaces and repositories.
Sensitive Information Disclosure
LLMs revealing confidential data through their responses. This includes leaking training data (memorization), exposing PII, revealing system prompts, or disclosing proprietary information. Mitigations include data sanitization, output filtering, and differential privacy techniques.
Insecure Plugin Design
Plugins and tool integrations that lack proper access controls, input validation, or principle of least privilege. When LLMs can invoke plugins, insufficient security controls can allow the model to perform unintended actions, access unauthorized resources, or escalate privileges.
Excessive Agency
Granting LLM-based agents too many permissions, too broad access, or too much autonomy. When agents can take real-world actions (sending emails, executing code, modifying databases), insufficient constraints can lead to unintended or harmful outcomes. Apply least privilege and require human approval for high-impact actions.
Overreliance
Users or systems placing excessive trust in LLM outputs without verification. This leads to accepting hallucinated facts, flawed code, or incorrect analysis. Organizations must implement human review processes, fact-checking mechanisms, and clear communication about LLM limitations.
Model Theft
Unauthorized access to, copying of, or extraction of proprietary LLM models. This includes model weight exfiltration, model extraction through repeated querying (model stealing attacks), and unauthorized redistribution. Protect models with access controls, watermarking, and query monitoring.
How BeyondScale Helps
Our approach to getting your organization compliant.
LLM Vulnerability Assessment
We systematically test your LLM-based applications against all 10 vulnerability categories. This goes beyond automated scanning. Our team manually crafts attack scenarios specific to your application's architecture, integrations, and deployment context.
Red-Teaming Against Each Category
We conduct adversarial testing that simulates real-world attacks. This includes prompt injection campaigns, output manipulation testing, plugin security assessment, agency boundary testing, and attempts to extract sensitive information or model internals.
Remediation Guidance
For each vulnerability identified, we provide specific, actionable remediation steps. This is not generic advice. We deliver code-level recommendations, architectural changes, and configuration adjustments tailored to your technology stack and deployment model.
Developer Security Training
We train your development and security teams on LLM-specific security risks and secure development practices. Training covers each OWASP LLM Top 10 category with hands-on exercises using real-world attack and defense techniques.
AI Security Audit Checklist
A 30-point checklist covering LLM vulnerabilities, model supply chain risks, data pipeline security, and compliance gaps. Used by our team during actual client engagements.
We will send it to your inbox. No spam.
Who This Applies To
- Companies building applications that integrate LLMs (GPT, Claude, Llama, etc.)
- Organizations deploying AI chatbots, copilots, or autonomous agents
- Development teams integrating LLM APIs into existing products
- Security teams responsible for applications that use LLMs or AI
- Companies using LLMs for internal tools, customer support, or data analysis
Frequently Asked Questions
Related Frameworks
EU AI Act
The world's first comprehensive AI regulation. Mandatory for any organization deploying AI systems that affect people in the EU.
NIST AI RMF
A voluntary framework for managing AI risks, developed by the National Institute of Standards and Technology. Increasingly referenced in US federal procurement and private-sector governance.
ISO 42001
The international standard for AI management systems. Provides a certifiable framework for organizations that develop, provide, or use AI responsibly.
Get Compliance-Ready
Whether you need a gap analysis, implementation support, or certification readiness, our team can help you meet OWASP LLM Top 10 requirements on a timeline that works for your organization.
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