Blog Article

AI Security Explained: Best Practices for Deploying Generative AI Safely

Arnav Bathla

8 min read

The rapid evolution of generative AI has transformed industries, driving innovation in fields ranging from healthcare to finance. However, as the deployment of these technologies increases, so does the complexity of their security challenges. Security concerns in AI range from initial model design, including fine-tuning and the selection of source models, to runtime interactions, such as prompt injection and jailbreaking. This blog post explores these challenges in detail and discusses how you can think about end-to-end security.

Understanding the Risks: from Model Design to Runtime

Model Design

1. Fine-Tuning Vulnerabilities: Fine-tuning a pre-trained model can introduce biases or errors if the training data is compromised. Careful data validation is critical to prevent these issues.

2. Open Source Model Risks: While open-source models can expedite development, they may include corruption or embedded vulnerabilities. It is essential to thoroughly verify their integrity and apply regular updates.

3. Dataset Vulnerabilities: Data poisoning can occur when datasets are tampered with, leading to manipulated model behavior. Robust data cleansing and verification processes are crucial to safeguard against these risks.

4. Model Serialization Attacks: Serialization allows models to be saved and reused, but unsafe deserialization can open attack vectors, such as the execution of malicious code.

Runtime Security
  1. Prompt Injection: Attackers may manipulate AI outputs by injecting harmful inputs. Secure input processing mechanisms are necessary to mitigate these risks.

  2. Jailbreaking: This technique involves circumventing model restrictions to perform unauthorized actions. Solidifying model boundaries is essential to prevent such exploits.

  3. Injection: AI models can suffer from injection flaws, where untrusted inputs are processed as commands or queries.

  4. Broken Authentication: Weak authentication processes can allow unauthorized access to AI functionalities.

  5. Sensitive Data Exposure: Without proper precautions, AI models can leak sensitive information.

  6. XML External Entities (XXE): Vulnerable configurations may expose AI systems to external entity attacks, risking data disclosure.

  7. Broken Access Control: Systems must enforce strict access controls to different functionalities to avoid exploitation.

  8. Security Misconfiguration: AI platforms can be susceptible to various issues if not configured correctly.

  9. Cross-Site Scripting (XSS): XSS vulnerabilities can arise if AI-generated outputs include unvalidated and unescaped user inputs.

  10. Insecure Deserialization: Flaws in how an AI model’s learning states are restored can be exploited to perform malicious actions.

  11. Using Components with Known Vulnerabilities: Employing outdated or vulnerable components can expose AI systems to additional risks.

  12. Insufficient Logging & Monitoring: Adequate monitoring is crucial to detect and address security breaches promptly.

Layerup: Enhancing Security from Design to Deployment

Layerup provides a comprehensive security framework tailored for generative AI applications, addressing risks from model design to runtime operations.

1. Model Scanning: Through advanced model scanning, Layerup detects vulnerabilities in AI models, offering insights for improvements before deployment.

2. MLBOM (Machine Learning Bill of Materials): Layerup's MLBOM ensures transparency and helps identify any vulnerable elements within your AI model, enhancing overall security.

3. Runtime Protection SDK: Layerup’s SDK is designed to protect AI applications during runtime, safeguarding against prompt injections, jailbreaking, and other security threats.

4. Continuous Security Updates: As AI technology evolves, Layerup provides ongoing updates and patches to secure AI systems against new and emerging threats.

Deploying generative AI products securely requires a proactive security approach from the initial design phase through the complexities of runtime operations. Using security platforms like Layerup can provide the necessary comprehensive protection, ensuring that your AI innovations remain both effective and secure.


Deploying generative AI securely requires a comprehensive approach that covers every stage of the AI lifecycle—from the selection and fine-tuning of models to operational security against sophisticated attacks. With the aid of platforms like Layerup, organizations can bolster their defenses, ensuring that their AI deployments are not only innovative but also secure.

Securely Implement Generative AI


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Securely Implement Generative AI


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Securely Implement Generative AI


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