Ultimate Power Guide: OWASP Top 10 for LLMs – 2025 Edition

Large Language Models (LLMs) are transforming business, development, and cybersecurity at a scale we’ve never seen before. But every leap forward in generative AI brings new risks and attack surfaces. As enterprises implement LLMs everywhere—from chatbots to autonomous agents—the urgency to understand and mitigate their vulnerabilities has never been greater.

This definitive guide unpacks the OWASP Top 10 for LLMs, ensuring your security strategy is informed, actionable, and ahead of emerging threats.

What is the OWASP Top 10 for LLMs?

For decades, the Open Web Application Security Project (OWASP) has been the benchmark for web application security, shaping global best practices1. In response to the AI surge, OWASP expanded its expertise to Large Language Models, producing a robust, community-vetted list of the Top 10 security risks facing LLM-powered applications. These risks reflect both classic vulnerabilities and unique threats introduced by AI integration at scale14.

2025: The New Era of LLM Security

The 2025 OWASP Top 10 for LLMs provides not only a list, but a blueprint for addressing the most urgent AI security challenges46. Key evolutions include:

• A system-wide focus—beyond prompts, targeting agentic architectures and complex supply chains.
• Guidance specific to Retrieval-Augmented Generation (RAG) and plugin ecosystems.
• Introduction of system prompt leakage and broader resource (unbounded consumption) vulnerabilities.

Explore the official OWASP Top 10 for LLMs list here.

The OWASP Top 10 for LLMs: Risks, Real Examples, and Pro-Level Defenses

1. Prompt Injection

• What It Is: Malicious actors manipulate LLM prompts to override instructions or access restricted functions, often bypassing guardrails.1
• Real-World Example: Red teamers demonstrate attacks where LLMs “leak” confidential info or execute unauthorized actions when fed cleverly crafted prompts.
• Mitigations:
  • Enforce the principle of least privilege
  • Segregate user input from system prompts with clear boundaries
  • Filter and validate outputs before use
  • Require human approval for sensitive operations3

2. Insecure Output Handling

• What It Is: Applications pass LLM output downstream without robust validation, risking script injection or unsafe commands.
• Real-World Example: LLM-generated text with hidden JavaScript runs in users’ browsers, triggering XSS.
• Mitigations:
  • Output encoding and context-aware sanitization
  • Schema-based response validation
  • Continuous defense-in-depth throughout the stack1

3. Training Data Poisoning

• What It Is: Attackers taint training datasets, embedding malicious behaviors or biases into the LLM.
• Real-World Example: Poisoned legal corpora leads LLM legal AI to confidently recommend exploitable loopholes.
• Mitigations:
  • Source data from trusted origins
  • Filter out anomalies before and during training
  • Conduct adversarial testing on model responses1

4. Model Denial of Service (Unbounded Consumption)

• What It Is: Adversaries send input that exhausts compute or resources, either degrading performance or inflating costs.
• Real-World Example: Precise prompts force prolonged LLM processing, causing outages or high bills.
• Mitigations:
  • Input length and complexity validation
  • Quotas, timeouts, and asynchronous task queues

5. Supply Chain Vulnerabilities

• What It Is: LLM apps rely on third-party models, plugins, or datasets—if compromised, attackers have a backdoor.
• Real-World Example: Popular open-source plugin is trojanized, leaking user data.
• Mitigations:
  • Automated dependency and vulnerability scanning
  • Only use signed and verified models/plugins
  • Rely on trustworthy providers

6. Sensitive Information Disclosure

• What It Is: LLMs accidentally reveal secrets from their training data, context windows, or cached responses.
• Real-World Example: AI coding assistants expose API keys included in previous user requests.
• Mitigations:
  • Data loss prevention (DLP) on input/output
  • Rigorous output filtering
  • Enforce strict access controls

7. Insecure Plugin Design

• What It Is: Poorly designed plugins accept unsafe input or lack permission controls, allowing misuse or exploitation.
• Real-World Example: LLM plugin allows SQL injection via natural language prompts.1
• Mitigations:
  • Always use parameterized input for critical operations
  • Authenticate and authorize all plugin actions

8. Excessive Agency

• What It Is: LLMs (especially agentic AIs) are granted too much permission, enabling catastrophic mistakes or manipulation4.
• Real-World Example: Trading bots make destructive financial decisions from misleading news.
• Mitigations:
  • Enforce granular, role-based access
  • Scope and limit permissions per task
  • Require human signoff for critical actions

9. Overreliance

• What It Is: Human users trust LLM outputs without verification, leading to the spread of errors, security issues, or misinformation.
• Real-World Example: Developers deploy insecure code snippets generated by LLMs into production.
• Mitigations:
  • Make internal and user education standard policy
  • Offensive simulation (red teaming) of LLM use cases
  • Require source transparency and explainable responses

10. Model Theft

• What It Is: Attackers exfiltrate entire proprietary LLMs or “clone” them via API scraping and output analysis.
• Real-World Example: Competitors mimic a closed LLM using stolen input/output logs.
• Mitigations:
  • Access monitoring and anomaly detection on APIs and file systems
  • Embed invisible watermarks in LLM responses

Expert Insights & Actionable Takeaways

System Prompt Dilemma

Developers now face trade-offs between system prompt completeness (good for control) and exposure (risky for attacks like prompt leakage). Limiting and protecting prompt details is key4.

The Rise of RAG and Embedding Risks

Over half of organizations are building with RAG systems, introducing novel vulnerabilities—vector poisoning, data leakage, and retrieval attacks. Secure your vector pipelines and embedding logic with the same vigilance as you would your LLM6.

The Expanded Supply Chain Attack Surface

Compromised plugins and third-party dependencies are now among the most active vectors—require your security audits to include all external AI components and integrations, not just your own codebase3.

Get Ahead: Pro Tactics to Secure LLMs with the OWASP Top 10

The OWASP Top 10 for LLMs is more than a checklist—it’s a strategic framework. By implementing these controls, you future-proof your AI stack against evolving threats and confidently enable innovation.

Frequently Asked Questions

Stay up to date on the evolving OWASP standards directly from the OWASP Large Language Model Security initiative and reputable cybersecurity authorities. For a deeper technical dive and ongoing updates, review the 2025 OWASP Top 10 official documentation7.

Link out to these resources for deeper insight:

• OWASP Top 10 for Large Language Model Applications
• 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps
• OWASP Top 10: LLM & Generative AI Security Risks
• Security checklist and best practices for LLMs

References:

1 https://owasp.org/www-project-top-10-for-large-language-model-applications/
2 https://www.getastra.com/blog/security-audit/owasp-large-language-model-llm-top-10/
3 https://genai.owasp.org/llm-top-10/
4 https://www.pointguardai.com/blog/understanding-the-owasp-top-10-for-llms
5 https://blog.barracuda.com/2024/11/20/owasp-top-10-risks-large-language-models-2025-updates
6 https://www.confident-ai.com/blog/owasp-top-10-2025-for-llm-applications-risks-and-mitigation-techniques
7 https://owasp.org/www-project-top-10-for-large-language-model-applications/assets/PDF/OWASP-Top-10-for-LLMs-v2025.pdf
8 https://www.lasso.security/blog/owasp-top-10-for-llm-applications-generative-ai-key-updates-for-2025
9 https://genai.owasp.org
10 https://www.lasso.security/blog/owasp-top-10-llm-vulnerabilities-security-checklist