Strategies for Securing and Safeguarding Large Language Models (LLMs)

By: Suyash Ghadge, Associate Data Scientist At Rakuten

As the use of large language models (LLMs) becomes more widespread, ensuring safe and responsible implementation is critical. Like the GPT-4, LLMs are powerful tools capable of producing human-like data, but they also carry significant risk. In this blog, we will discuss some of the major vulnerabilities identified by the Open Web Application Security Project (OWASP) and explore ways to implement effective guardrails.

Some Major OWASP vulnerabilities

Overdependence
- Risk: Over-reliance on LLMs without adequate human supervision can lead to misinformation, miscommunication, and potential legal issues.
- Mitigation: Add human-in-the-loop processes to monitor and validate results, especially in critical applications. Users should be educated on the limitations of the LLM and trained to verify the information provided.
Overwork
- Risk: Granting too much freedom or license to an LLM-based system can lead to unintended and potentially harmful behaviour.
- Mitigation: Limit the capabilities of LLMs by defining explicit limitations. These systems should be regularly audited and updated to ensure they are operating within acceptable parameters.
Critical Disclosure
- Risk: LLMs may inadvertently disclose confidential or sensitive information, resulting in a privacy breach.
- Mitigation: Implement strict data hygiene measures and develop procedures to minimize the risk of sensitive information being disclosed. Review and update these plans regularly as new disasters emerge.
Training Data Poisoning
- Risk: Malicious people can manipulate training data, introducing biases or weaknesses that compromise the safety and effectiveness of the model.
- Mitigation: Use robust data validation methods and adversary training to increase model resilience. Maintain complete records of training data sources to analyse and address potential issues.

Link for the OWASP LLM Vulnerabilities : https://owasp.org/www-project-top-10-for-large-language-model-applications/

Guardrails In LLMs

Ethics guidelines: These include restrictions designed to prevent content that could be considered discriminatory, biased, or harmful. Ethical safeguards ensure that LLMs operate within the boundaries of accepted social and ethical standards.
Regulators: Compliance is important, especially in the healthcare, financial, and legal industries. These guardrails ensure that the model output meets regulatory standards, including data security and user privacy.
Context guard mechanisms: LLMs can sometimes provide information that may be inappropriate for a given context even if it is not clearly harmful or illegal. These guardrails help refine the model a better understanding of what is relevant and acceptable in a particular context.
Security filter: This filter protects against internal and external security threats, ensuring that images cannot be tampered with to reveal sensitive information or transmit incorrect information.
Adaptive guardrails: Because LLMs learn and adapt over time, these guardrails are designed to evolve with the model, ensuring that they are always in line with ethical and legal standards.

Guardrails are necessary because LLMs are not easy to control.

You cannot guarantee that the output generated by LLMs is correct. Significant challenges are hallucinations and a lack of proper structure.

LLMs work properly in pre-deployment but could be problematic in production.

Conclusion

As LLMs continue to enter the industry, ensuring that they are used safely and ethically is critical. The protection of LLMs is not only a technical challenge but also a social responsibility. While harnessing the power of these powerful tools, we must also commit to building a safe and reliable AI ecosystem that respects privacy, supports ethical standards, and it protects against abuse. The strategies outlined in this blog provide a foundational approach to achieving this goal, ensuring that the LLM can be a force for the prosperity of our digital future.