2025 OWASP Top 10 LLM

Reviewing the 2025 OWASP Top 10 for LLM Applications

The 2025 OWASP Top 10 list for Large Language Model (LLM) applications underscores the critical importance of robust security measures in AI systems. As LLMs become increasingly integrated into enterprise workflows, customer interactions, and autonomous systems, the risks they pose have grown exponentially. At Preamble, we’re proud to address these challenges head-on with solutions that secure AI applications and inspire confidence in their deployment.

For the full OWASP Top 10 list, visit OWASP 2025 Top 10 for LLM Applications

Prompt Injection Tops the List Once Again

Prompt Injection is the number one risk in the OWASP Top 10 for the second consecutive update. This enduring threat highlights the challenges organizations face in securing the core mechanisms of LLMs. Prompt Injection attacks exploit weaknesses in how models process input prompts, enabling malicious actors to bypass safeguards, manipulate outputs, or gain unauthorized access.

Examples of prompt injection include:

• Direct Attacks: Crafting prompts that override a model’s safeguards to reveal sensitive data or elicit unintended behaviors.
• Indirect Attacks: Embedding malicious instructions in external sources, such as websites or files, later processed by the LLM. some text
  • Preamble believes indirect attacks will significantly increase in 2025, disproportionately exploiting AI Agents than standalone LLMs. 
• Multimodal Attacks: Hiding prompts in images or other non-text inputs for manipulation in multimodal systems.

At Preamble, our patented Prompt Injection and multi-tier guardrail solution detects and defends potential threats that could compromise your data or AI workflows. Our solutions ensure that organizations remain resilient to evolving attack vectors.

Comprehensive Coverage for the OWASP Top 10

Beyond prompt injection, the OWASP Top 10 highlights other pressing risks, and Preamble is equipped to address them:

2. Sensitive Information Disclosure: Preamble’s AI monitoring and privacy guardrails prevent sensitive data leakage.
3. Supply Chain Vulnerabilities: Preamble manages secure AI workflows by providing verified third-party components that are safe for use.
4. Data and Model Poisoning: While not directly controlled by Preamble, our input/output guardrails and access controls to our knowledge base reduce the ability to poison data and mitigate the impact of poisoned models.
5. Improper Output Handling: We enforce robust output guardrails and validation to maintain accuracy and prevent exploitation.
6. Excessive Agency: Our user controls, and least privilege principle restrict LLM autonomy to reduce the risk of unauthorized actions.
7. System Prompt Leakage: By isolating system prompts, allowing for custom guardrails, and monitoring API interactions, Preamble minimizes this risk.
8. Vector and Embedding Weaknesses: We provide guardrails and access controls for securing Retrieval-Augmented Generation (RAG) pipelines.
9. Misinformation: Our solutions ensure AI gives answers based on trusted information.
10. Unbounded Consumption: Preamble’s resource control mechanisms prevent unauthorized access to computational resources, mitigating denial-of-service risks and uncontrolled usage.

The Path Forward for AI Security

The 2025 OWASP Top 10 serves as a reminder that securing AI is an ongoing effort requiring collaboration between developers, security experts, and industry leaders. At Preamble, we are committed to leading the charge with innovative solutions, a deep understanding of AI risks, and a relentless dedication to building trust in AI technologies.

Learn more about how Preamble can help your organization navigate the complex landscape of AI security. Together, we can ensure that the transformative power of AI is harnessed responsibly and securely.

For detailed insights into our solutions, visit Preamble’s Website or contact us directly to discuss your AI security needs.

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