A Deep Dive into the OWASP Top 10 for LLM Applications 2025
The rapid evolution of Large Language Models (LLMs) has ushered in a new era of technological innovation. However, with this advancement comes a growing concern: the potential security risks associated with these powerful tools. The Open Web Application Security Project (OWASP) has recently released its updated Top 10 list for LLM applications in 2025, highlighting the most critical vulnerabilities that organizations need to address.
The 2025 list reflects a better understanding of existing risks and introduces critical updates on how LLMs are used in real-world applications today. For instance, Unbounded Consumption expands on what was previously Denial of Service to include risks around resource management and unexpected costs—a pressing issue in large-scale LLM deployments.
The Vector and Embeddings entry responds to the community’s requests for guidance on securing Retrieval-Augmented Generation (RAG) and other embedding-based methods, now core practices for grounding model outputs.
System Prompt Leakage has been added to address an area with real-world exploits that were highly requested by the community. Many applications assumed prompts were securely isolated, but recent incidents have shown that developers cannot safely assume that information in these prompts remains secret.
Excessive Agency has been expanded, given the increased use of agentic architectures that can give the LLM more autonomy. With LLMs acting as agents or in plug-in settings, unchecked permissions can lead to unintended or risky actions, making this entry more critical than ever.
Understanding the Top 10
Prompt Injection:
Malicious users can manipulate LLM responses by crafting specific prompts that can lead to unintended behavior, such as disclosing sensitive information or executing malicious actions.
Insecure Output Handling:
Failing to properly validate and sanitize LLM outputs can expose systems to vulnerabilities like cross-site scripting (XSS), cross-site request forgery (CSRF), and SQL injection.
Training Data Poisoning:
Adversaries can introduce malicious data into training sets, leading to biased or harmful model behavior.
Model Denial of Service:
Overloading an LLM with excessive requests can render it unavailable or degrade its performance.
Supply Chain Vulnerabilities:
Compromised third-party libraries or frameworks used in LLM development can introduce security risks.
Sensitive Information Disclosure:
LLMs may inadvertently leak sensitive information, such as proprietary data or personal information, through their outputs.
Insecure Plugin Design:
Poorly designed plugins can expose LLMs to vulnerabilities like unauthorized access and data breaches.
Excessive Agency:
Granting LLMs too much autonomy can lead to unintended consequences and potential harm.
Overreliance:
Overreliance on LLMs without proper human oversight can lead to errors and security risks.
Model Theft:
Unauthorized access to LLM models can result in intellectual property theft and misuse.
Mitigating Risks and Securing LLM Applications
To safeguard LLM applications, organizations should adopt a comprehensive security approach that includes:
Input Validation and Sanitization: Rigorously validate and sanitize all inputs to prevent prompt injection attacks.
Output Filtering and Redaction: Implement filters to remove sensitive information from LLM outputs.
Robust Access Controls: Enforce strong access controls to protect LLM models and data.
Regular Security Audits: Conduct regular security assessments to identify and address vulnerabilities.
Secure Development Practices: Adhere to secure coding practices and use secure development frameworks.
Continuous Monitoring and Threat Detection: Implement robust monitoring and threat detection systems.
User Awareness and Training: Educate users about potential risks and best practices.
By staying informed about the latest threats and implementing effective security measures, organizations can harness the power of LLMs while mitigating risks.