The Open Web Application Security Project (OWASP) is a globally recognized nonprofit organization dedicated to improving the security of software. Known for its open-source projects, OWASP provides freely available resources—ranging from documentation and developer tools to community forums and conferences—to help organizations secure their web applications. Its flagship publication, the OWASP Top 10, has become the industry standard for identifying and addressing the most critical risks in application security.
In 2023, OWASP extended its mission to cover the rapidly growing domain of artificial intelligence by launching the OWASP Top 10 for Large Language Model (LLM) Applications. This new initiative is designed to raise awareness of the unique security risks posed by LLMs and offer practical guidance for mitigating them. As LLMs become deeply embedded in enterprise applications, from customer service bots to internal productivity tools, understanding and addressing these threats is essential to managing operational risk and maintaining trust.
Key Threats in the OWASP Top 10 for LLM Applications
1. Prompt Injection
Prompt injection occurs when attackers manipulate the input prompts given to an LLM, causing it to behave in unintended ways. This can involve direct prompt injection (e.g., overriding system instructions or “jailbreaking”) or indirect injection through external sources like websites or documents. The implications range from unauthorized backend access to social engineering attacks. Mitigation strategies include implementing strict access controls, validating inputs, and requiring human oversight in automated decision-making.
2. Insecure Output Handling
If LLM-generated outputs are not validated or sanitized before being passed to downstream systems, they may trigger severe security issues such as cross-site scripting (XSS), remote code execution (RCE), or server-side request forgery (SSRF). Enterprises should adopt a Zero Trust approach and treat LLMs as untrusted entities—ensuring that all outputs are validated before being processed further.
3. Training Data Poisoning
Attackers may seek to corrupt a model’s behavior by injecting malicious data into its training sets. Poisoned data can reduce model accuracy, introduce bias, or enable adversarial manipulation. Organizations must protect the integrity of their data pipelines by vetting data sources, blocking model access to untrusted content, and applying rigorous data sanitization processes.
4. Model Denial of Service (DoS)
Similar to traditional DDoS attacks, adversaries can overwhelm an LLM with resource-intensive requests to degrade performance or inflate operational costs. These attacks can be difficult to detect due to the inherently high resource demands of LLMs. To counteract this, enterprises should enforce API rate limiting, monitor system resource consumption, and validate inputs to detect abnormal patterns.
5. Supply Chain Vulnerabilities
LLM applications often rely on external components such as pre-trained models, datasets, third-party APIs, and plugins. Each of these represents a potential attack vector if not properly vetted. To minimize risk, organizations should maintain a comprehensive inventory of all third-party components, verify the security posture of suppliers, and regularly audit and patch dependencies.
6. Sensitive Information Disclosure
LLMs can unintentionally reveal sensitive data—such as customer information, confidential business logic, or regulated data—either from training data or through poorly handled prompts. Enterprises must implement robust data sanitization and localization controls to ensure compliance with privacy regulations. Limiting training inputs and output scopes can reduce the likelihood of such disclosures.
7. Insecure Plugin Design
Plugins extend the functionality of LLMs by integrating them with third-party services or internal systems. If not properly designed, they can expose the application to input validation failures, remote code execution, or privilege escalation. Developers should apply secure coding practices, enforce authentication and authorization, and follow the principle of least privilege.
8. Excessive Agency
Some LLM applications are granted too much autonomy—such as the ability to send emails, delete files, or make API calls without oversight. If an LLM malfunctions or is manipulated, this level of access could cause serious damage. Limiting the permissions and operational scope of LLMs, combined with human-in-the-loop controls, helps reduce this risk.
9. Overreliance
LLMs are capable but imperfect. They can hallucinate facts, generate biased content, or misinterpret prompts—all while appearing highly confident. Enterprises that over-rely on LLMs for critical tasks risk disseminating misinformation or making poor decisions. Oversight policies, output verification, and human review are essential to maintaining reliability.
10. Model Theft
LLM models often contain proprietary data and intellectual property. If attackers gain access, they may replicate the model, extract sensitive data, or reverse-engineer it for malicious use. To prevent theft, organizations should enforce strong identity and access management (IAM) controls, log all access activity, and deploy data loss prevention (DLP) technologies.
Building a Secure Future with LLMs
As enterprises increasingly deploy LLMs across business functions, security teams must account for new risks that differ from traditional software threats. The OWASP Top 10 for LLM Applications provides a critical framework for identifying, understanding, and mitigating these threats in a systematic way.
To stay ahead, organizations should incorporate these risks into their security development lifecycle (SDLC), enforce strict governance across data pipelines, and adopt layered defense strategies. By proactively addressing these emerging vulnerabilities, enterprises can unlock the potential of LLMs—without compromising trust, compliance, or resilience.
