OWASP Top 10 for LLM Applications

Summary

The OWASP Top 10 for LLM Applications represents the first comprehensive security framework specifically designed for large language model vulnerabilities. Unlike traditional web application security frameworks, this guide addresses the unique attack vectors that emerge when AI models interact with users, data, and systems. From prompt injection attacks that manipulate model behavior to data leakage risks that expose training information, this framework catalogs the most pressing security concerns that organizations deploying LLMs face today.

The Ten Critical Vulnerabilities

LLM01: Prompt Injection

Malicious inputs that manipulate the LLM to execute unintended commands, bypass safety measures, or reveal system prompts. This includes both direct user inputs and indirect attacks through external content sources.

LLM02: Insecure Output Handling

Insufficient validation of LLM outputs before passing them to downstream systems, potentially leading to XSS, CSRF, SSRF, privilege escalation, or remote code execution.

LLM03: Training Data Poisoning

Manipulation of training data or fine-tuning processes to introduce backdoors, biases, or vulnerabilities that compromise model integrity and security.

LLM04: Model Denial of Service

Resource-intensive queries that cause service degradation, increased costs, or system unavailability through excessive computation or memory usage.

LLM05: Supply Chain Vulnerabilities

Risks from third-party datasets, pre-trained models, plugins, or other external components that may contain security flaws or malicious content.

LLM06: Sensitive Information Disclosure

Unintended revelation of confidential data through model outputs, including personal information, proprietary data, or system details from training data.

LLM07: Insecure Plugin Design

Inadequate input validation and access controls in LLM plugins, enabling attacks like remote code execution or privilege escalation.

LLM08: Excessive Agency

Granting LLM-based systems too much autonomy or permissions, leading to unintended actions or decisions with significant consequences.

LLM09: Overreliance

Lack of human oversight and validation of LLM outputs, particularly in critical decision-making processes where errors could cause harm.

LLM10: Model Theft

Unauthorized access to proprietary models through API abuse, side-channel attacks, or other extraction techniques.

Who this resource is for

Security engineers and architects implementing LLM-based applications who need to identify and mitigate AI-specific vulnerabilities that traditional security tools miss.

DevSecOps teams integrating LLMs into existing applications and requiring security checkpoints throughout the development lifecycle.

Product managers and CTOs overseeing AI initiatives who need to understand the unique risk landscape and communicate security requirements to technical teams.

Compliance officers in regulated industries who must ensure LLM deployments meet security standards and regulatory requirements.

Penetration testers and red team professionals expanding their expertise to include AI-specific attack vectors and testing methodologies.

What makes this different from traditional security frameworks

Traditional application security focuses on code vulnerabilities, authentication, and data protection. LLM security introduces entirely new attack surfaces: the model itself becomes both an asset to protect and a potential attack vector. Prompt injection, for example, has no equivalent in conventional web applications—it's a form of "social engineering" against AI systems.

The framework also addresses the probabilistic nature of AI systems, where the same input might produce different outputs, making traditional security testing approaches insufficient. It recognizes that LLMs can be simultaneously victim and accomplice in attacks, being manipulated to perform malicious actions while appearing to function normally.

Implementation roadmap

Phase 1: Assessment (Weeks 1-2) Map your LLM applications against the Top 10 to identify current exposure levels and prioritize vulnerabilities based on your specific use cases and risk tolerance.

Phase 2: Quick Wins (Weeks 3-4) Implement input validation, output sanitization, and basic monitoring for prompt injection attempts. These provide immediate risk reduction with minimal development effort.

Phase 3: Architecture Review (Weeks 5-8) Redesign system components to implement proper sandboxing, limit model permissions, and establish human oversight checkpoints for critical decisions.

Phase 4: Continuous Security (Ongoing) Integrate LLM-specific security testing into CI/CD pipelines, establish monitoring for new attack patterns, and maintain awareness of emerging vulnerabilities in the rapidly evolving LLM landscape.

Common implementation pitfalls

Treating LLM security as an afterthought: Unlike traditional applications where security can sometimes be retrofitted, LLM security requires architectural decisions made early in the development process.

Over-filtering inputs and outputs: Aggressive content filtering can break legitimate use cases. The key is finding the balance between security and functionality through careful tuning and testing.

Ignoring supply chain risks: Many teams focus on their own code while overlooking vulnerabilities in third-party models, datasets, or plugins that form the foundation of their applications.

Underestimating the creativity of attackers: Prompt injection techniques evolve rapidly, and simple keyword-based defenses quickly become obsolete. Robust defenses require understanding the underlying mechanisms, not just blocking known attack patterns.