The Open Worldwide Application Security Project (OWASP) has recently released a list of the top 10 most critical vulnerabilities that impact large language model (LLM) applications. These vulnerabilities pose security risks that developers, designers, architects, managers, and organizations need to be aware of when deploying and managing LLMs. The goal of the list is to educate and raise awareness around these vulnerabilities, as well as provide strategies for remediation to enhance the security posture of LLM applications.

One of the vulnerabilities identified by OWASP is prompt injections. This occurs when an attacker manipulates a large language model through crafted inputs, leading the LLM to execute the attacker’s intentions unknowingly. This can result in data exfiltration, social engineering, and other issues. Preventative measures for prompt injections include enforcing privilege control on LLM access to backend systems and adding a human in the loop for sensitive operations to reduce the risk of unauthorized actions.

Another critical vulnerability highlighted by OWASP is training data poisoning. This involves manipulating training data to introduce vulnerabilities, backdoors, or biases that could compromise the model. Preventative measures for training data poisoning include verifying the supply chain of training data, crafting different models for different use cases, and implementing strict vetting or input filters for training data to control the volume of falsified data.

Insecure output handling is also a significant vulnerability facing LLM applications. This refers to insufficient validation, sanitization, and handling of outputs generated by large language models before passing them downstream to other components and systems. Preventative measures for insecure output handling include treating the model like any other user, applying proper input validation on responses, and following OWASP’s guidelines for effective input validation and sanitization.

Model denial of service is another threat identified by OWASP. This occurs when an attacker interacts with an LLM in a way that uses an exceptionally high amount of resources, leading to a decline in service quality and potentially incurring high resource costs. Preventative measures for model denial of service include implementing input validation and sanitization, capping resource use per request, and continuously monitoring resource utilization to identify abnormal patterns.

Supply chain vulnerabilities are also a concern for LLM applications, especially when using open-source or third-party components, poisoned or outdated pre-trained models, or corrupted training data sets. Preventative measures for supply chain vulnerabilities include careful vetting of data sources and suppliers, using vulnerability scanning and patching to mitigate risks, and maintaining an up-to-date inventory of components to identify new vulnerabilities quickly.

Sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft are additional vulnerabilities that organizations need to be aware of when deploying LLM applications. Preventative measures for these vulnerabilities include implementing data sanitization and scrubbing, applying strict input controls, limiting plugins and tools accessed by LLMs, and enforcing strong access controls.

In conclusion, security leaders and organizations must prioritize the secure use of generative AI technologies, such as large language models. Regular updates, human oversight, contextual understanding, testing, and evaluation are essential to ensure that LLMs function correctly and defend against potential vulnerabilities and security threats. By following best practices and implementing preventative measures, organizations can enhance the security posture of their LLM applications and mitigate the risk of exploitation by malicious actors.

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