The OWASP LLM Top 10: Key Security Risks and Mitigation Strategies

The OWASP (Open Web Application Security Project) foundation has published an authoritative guide that identifies and describes the most critical and common security and risk vulnerabilities in large language model (LLM) applications, serving as an essential reference framework for LLM development, testing, and auditing.

Core Goal

The guide helps developers, security experts, and organizations understand LLM‑specific security risks and provides mitigation measures to build safer, more reliable, and controllable AI applications.

LLM01: Prompt Injection

Description: Attackers craft malicious user prompts that bypass existing instructions, filters, or security mechanisms, causing the LLM to perform unintended actions, similar to SQL injection.

Impact: Data leakage, unauthorized operations, content abuse, and system functionality disruption.

LLM02: Insecure Output Handling

Description: Applications blindly trust and forward LLM output to downstream systems or users without proper validation, sanitization, or sandboxing.

Impact: Potential cross‑site scripting (XSS) on the front end, remote code execution (RCE) on the back end, privilege escalation, or other supply‑chain attacks.

LLM03: Training Data Poisoning

Description: During pre‑training or fine‑tuning, attackers inject biased, back‑doored, or malicious content into the training data, influencing future model behavior.

Impact: Biased, harmful, or inaccurate responses that damage brand reputation and present long‑term remediation challenges.

LLM04: Model Denial‑of‑Service

Description: Attackers send large volumes of complex, resource‑intensive requests, causing the LLM to respond slowly, become unavailable, or incur high operational costs.

Impact: Degraded service availability, soaring costs, and collateral impact on other users.

LLM05: Supply‑Chain Vulnerabilities

Description: Third‑party components that LLM applications depend on—pre‑trained models, datasets, plugins, libraries, platforms—contain vulnerabilities or are tampered with.

Impact: A single weak link can collapse the entire application’s security, leading to data breaches or system takeover.

LLM06: Improper Access Control

Description: The LLM or its integrated systems fail to enforce proper authentication, authorization, and access controls, allowing the model to execute actions beyond user privileges.

Impact: Sensitive information leakage, unauthorized access, and data tampering.

LLM07: Data Leakage

Description: The LLM may unintentionally expose sensitive data in its responses, such as personal information from training data, system prompts, confidential rules, or user conversation history.

Impact: Violations of privacy regulations (e.g., GDPR), commercial secret exposure, and reputational damage.

LLM08: Over‑Delegation

Description: Granting the LLM excessive autonomy to perform sensitive operations (e.g., sending emails, database updates, financial transactions) without human confirmation, safety barriers, or audit trails.

Impact: Execution of dangerous actions, financial loss, and system destruction.

LLM09: Over‑Reliance

Description: Users or systems place undue trust in LLM output without fact‑checking or critical evaluation, especially in high‑risk domains like healthcare, law, or code generation.

Impact: Decisions based on erroneous information, leading to severe consequences (the “hallucination” problem).

LLM10: Model Theft

Description: Attackers issue massive API queries to reverse‑engineer or copy proprietary model weights, architecture, or training data.

Impact: Intellectual‑property loss, weakened competitive advantage, and the stolen model can be used for deeper attacks.

Core Defense Principles and Recommendations

Follow a Secure Development Lifecycle: Integrate security considerations into every phase of LLM application design, development, and deployment.

Implement Defense‑in‑Depth: Do not rely on a single security layer; combine input validation, output filtering, sandboxing, access controls, and audit logging.

Apply the Principle of Least Privilege: Restrict the LLM and its calling components to only the permissions necessary for their tasks.

Human‑in‑the‑Loop: Require manual review and confirmation for high‑risk decisions and actions.

Continuous Monitoring and Auditing: Log LLM inputs and outputs, monitor for anomalous behavior, and conduct regular security assessments and red‑team testing.

Conclusion

The OWASP LLM Top 10 is the most practical roadmap for understanding and addressing LLM security risks today. It emphasizes treating the LLM as an untrusted, potentially malicious "user" rather than a trusted system component. For any organization or individual building or using LLM applications, mastering these ten risks and applying the recommended safeguards is a foundational prerequisite for safe, reliable, and responsible AI deployment.