Security News > 2024 > January > Top LLM vulnerabilities and how to mitigate the associated risk

As large language models become more prevalent, a comprehensive understanding of the LLM threat landscape remains elusive.

Successful prompt injection attacks can lead to cross-plugin request forgery, cross-site scripting and training data extraction, each of which put company secrets, personal user data and essential training data at risk.

Regardless of whether you use a third-party or owned model, there will always be a risk of "Data poisoning" by bad actors, which can have a significant impact on model performance and subsequently harm a brand's reputation.

The open-source AutoPoison framework provides a clear overview of how data poisoning can impact a model during the instruction tuning process.

Below are a series of strategies cyber teams can implement to mitigate risk and maximize AI model performance.

Because DoS attacks are not new to the cybersecurity landscape, there are several strategies that can be utilized to defend against model denial of service attacks and reduce the risk of rapidly rising costs.

News URL

https://www.helpnetsecurity.com/2024/01/10/llm-vulnerabilities-risk/