Introduction
The integration of Generative AI and Large Language Models (LLMs) into business operations has expanded the potential attack surface, necessitating a focus on security vulnerabilities. The OWASP Top 10 for LLM Applications [2] [3], released in August 2023 [3], highlights the primary security concerns associated with LLMs, including prompt injection [2] [3], insecure output handling [2] [3], and training data poisoning [3].
Description
Generative AI and Large Language Models (LLMs) are increasingly integrated into business operations [3], expanding the potential attack surface [3]. The OWASP Top 10 for LLM Applications [2] [3], released in August 2023 [3], identifies the main security issues affecting LLMs [3], with the top vulnerabilities including prompt injection (both direct and indirect) [3], insecure output handling [2] [3], training data poisoning [3], model inversion [1], and the exploitation of model biases [1].
Insecure output handling poses significant risks, as it can lead to various threats, including Cross-Site Scripting (XSS) [2], Cross-Site Request Forgery (CSRF) [2], Server-Side Request Forgery (SSRF) [2], privilege escalation [2], and remote code execution (RCE) [3]. The Ollama vulnerability [3], reported in June [3], exemplifies this risk [3], where insufficient validation allowed an attacker to send crafted HTTP requests to its API [3], potentially corrupting files and hijacking the system [3].
Testing APIs is crucial for securing LLM applications [3]. Recent tests revealed indirect prompt injection vulnerabilities in popular GenAI applications [3], where malicious prompts could extract sensitive information [3]. For example [3], Gemini for Workspace was shown to be susceptible to such attacks [3], allowing researchers to manipulate its output by inserting malicious instructions into its data source [3]. Additionally, sensitive information disclosure can occur when LLM outputs are accepted without proper scrutiny, leading to unintentional exposure of sensitive data. Regular monitoring of LLM outputs is essential [2], including examining multiple model responses for individual prompts to mitigate risks associated with hallucinations from any single model.
Training data poisoning is another significant threat [3], illustrated by the discovery of malicious models uploaded to the Hugging Face AI repository [3]. Researchers also accessed Meta and Llama LLM repositories using unsecured API access tokens [3], which could lead to model poisoning and data theft [3]. This highlights the importance of testing and detection to benchmark outputs and prevent model degradation [3].
To mitigate these risks [3], organizations should implement API testing on AI applications to identify vulnerabilities outlined in the OWASP Top 10 [3]. This proactive approach enables developers to take corrective actions and establish necessary checks and balances [3], reducing the likelihood of malicious exploitation or ungoverned AI behavior [3]. Prompt security measures [2], such as blocking prompt injections and monitoring for abnormal usage [2], are essential to safeguard against these vulnerabilities and ensure safe interactions with Generative AI applications [2].
Conclusion
The integration of Generative AI and LLMs into business operations presents significant security challenges that must be addressed to prevent exploitation. By focusing on vulnerabilities such as prompt injection, insecure output handling [2] [3], and training data poisoning [3], organizations can implement effective mitigation strategies. Proactive measures, including regular API testing and monitoring, are crucial to safeguarding against potential threats. As AI technology continues to evolve, maintaining robust security protocols will be essential to ensure safe and reliable interactions with AI applications in the future.
References
[1] https://www.polymerhq.io/blog/generative-ai-security-preparing-for-2025/
[2] https://www.prompt.security/blog/the-owasp-top-10-for-llm-apps-genai
[3] https://www.cybersecurityintelligence.com/blog/testing-apis-against-the-owasp-llm-top-10-8048.html
