SOSS Community Day India

The rise of open-source large language models (LLMs) like GPT, BERT, and T5 has greatly enhanced natural language processing. However, these models face significant security challenges due to their vulnerability to adversarial attacks. This abstract examines the susceptibility of open-source LLMs to OWASP Top 10 risks, including model inversion, data poisoning, insecure deployment, and adversarial examples. While open-source LLMs democratize AI, their transparent architecture can expose sensitive data. Model inversion can extract proprietary information, and data poisoning can corrupt outputs with malicious data. Insecure deployment without encryption or authentication leads to data breaches, while adversarial examples exploit model weaknesses. To strengthen these models, implementing differential privacy, adversarial training, and rigorous data validation is crucial. Adopting security best practices, such as penetration testing and real-time monitoring, along with fostering a security-aware community, is essential. By addressing these vulnerabilities, organizations can enhance the robustness and security of open-source LLMs, ensuring safe deployment and trust in AI applications.