OWASP LLM Top 10 Security Risks: The Complete 2025 Guide for AI Developers

Large Language Models (LLMs) like GPT-4, Claude, Gemini, and Llama have transformed how businesses operate—from customer service automation to code generation. However, this rapid adoption has exposed organizations to unprecedented security risks. The OWASP LLM Top 10 (2025 version) provides the definitive framework for understanding and mitigating these vulnerabilities.

What is OWASP LLM Top 10?

The OWASP (Open Web Application Security Project) LLM Top 10 is an authoritative, community-driven document identifying the ten most critical security risks specific to Large Language Model applications. The 2025 version reflects the evolving threat landscape as AI agents and autonomous systems become mainstream.

Unlike the traditional OWASP Top 10 for web applications, the LLM Top 10 addresses risks unique to AI systems—from prompt injection attacks that manipulate model behavior to supply chain vulnerabilities in the complex AI ecosystem.

The Complete OWASP LLM Top 10 (2025)

LLM01: Prompt Injection

Prompt injection remains the #1 security risk for LLM applications. Attackers craft malicious inputs that manipulate the model into ignoring original instructions, potentially exposing sensitive data, executing unauthorized actions, or producing harmful outputs.

The 2025 landscape shows increasingly sophisticated attacks:

• Direct Prompt Injection: Malicious prompts entered through user interfaces
• Indirect Prompt Injection: Hidden instructions in external data (websites, documents, RAG sources)
• Multi-modal Injection: Attacks via images, audio, or video processed by vision-language models
• Many-shot Jailbreaking: Using long contexts to gradually manipulate model behavior

LLM02: Sensitive Information Disclosure

LLMs may inadvertently reveal sensitive information through their outputs:

• PII (Personally Identifiable Information) from training data
• System prompts and internal configurations
• Proprietary business information and trade secrets
• API keys or credentials embedded in context windows
• Training data extraction through membership inference attacks

LLM03: Supply Chain Vulnerabilities

LLM applications depend on complex supply chains. The WEF reports that 65% of large firms identify supply chain security as a major cyber resilience challenge. Risks include:

• Compromised pre-trained model weights from untrusted sources
• Poisoned fine-tuning datasets
• Malicious plugins and third-party integrations
• Vulnerable dependencies in ML frameworks
• Compromised RAG data sources

LLM04: Data and Model Poisoning

Attackers manipulate training or fine-tuning data to introduce backdoors, biases, or vulnerabilities:

• Backdoor attacks triggered by specific inputs
• Bias injection affecting model outputs systematically
• RAG poisoning through compromised knowledge bases
• Gradient-based attacks during fine-tuning

LLM05: Insecure Output Handling

When LLM outputs are passed to downstream systems without validation, attackers can achieve:

• Cross-site scripting (XSS) in web applications
• SQL injection via generated queries
• Command injection through executed code
• Server-side request forgery (SSRF)

LLM06: Excessive Agency

With the rise of AI agents, excessive agency has become critical. When LLMs are granted too much autonomy:

• Agents can take harmful actions through manipulation
• Cascading failures across interconnected agent systems
• Privilege escalation through agent chains
• Unintended actions from model hallucinations

LLM07: System Prompt Leakage

System prompts contain critical configuration and are prime targets for extraction:

• Direct extraction through clever prompting
• Inference of prompt components through behavior analysis
• Competitive intelligence theft
• Discovery of security controls for bypass attempts

LLM08: Vector and Embedding Weaknesses

RAG systems and vector databases introduce new attack surfaces:

• Embedding inversion attacks to recover original text
• Adversarial documents designed to manipulate retrieval
• Vector database poisoning
• Cross-tenant data leakage in shared embedding services

LLM09: Misinformation

LLMs can generate and amplify misinformation:

• Hallucinations presented as facts
• Synthetic content for disinformation campaigns
• Manipulation of AI-generated summaries and reports
• Deepfake text that mimics trusted sources

LLM10: Unbounded Consumption

Resource exhaustion attacks target LLM infrastructure:

• Extremely long or complex prompts consuming compute
• Recursive patterns creating infinite loops
• High-volume attacks causing denial of service
• Economic denial-of-sustainability attacks

Mitigation Strategies

Defense against these vulnerabilities requires a layered approach:

• Input Validation: Scan all inputs for injection patterns before processing
• Output Filtering: Validate and sanitize all LLM outputs before use
• Least Privilege: Minimize LLM access to data and functions
• Human Oversight: Require approval for high-risk operations
• Continuous Testing: Regular red team testing against evolving attacks
• Monitoring: Real-time detection of anomalous behavior

Conclusion

The OWASP LLM Top 10 provides essential guidance for securing AI applications. As LLM adoption accelerates and AI agents become mainstream, understanding and addressing these vulnerabilities is critical. Organizations that implement robust security measures will be positioned to harness AI's benefits while managing its risks responsibly.