As large language models become more prevalent, a comprehensive understanding of the LLM threat landscape remains elusive.
While the AI threat landscape changes every day, there are a handful of LLM vulnerabilities that we know pose significant risk to enterprise operations today.
If cyber teams have a strong grasp on what these vulnerabilities are and how to mitigate them, enterprises can continue innovating with LLMs without taking on undue risk.
With LLMs, the possibility of data leaks is a real and growing concern.
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.
From sourcing and processing data to selecting and training the application, every step should bake in limitations that lower the risk of a breach.
The effectiveness of AI models hinges on data quality.
Throughout the model development process-from pre-training, to fine-tuning and embedding-training datasets are vulnerable to hackers.
Most enterprises leverage third-party models where an unknown person manages the data, and cyber teams can't blindly trust that the data hasn't been tampered with.
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.
Data sanitization and scrubbing: Be sure to check all the data and sources before they go into the models.
PII must be redacted before putting it into the model.
Red team exercises: Conduct LLM-focused red team exercises during the testing phases of the model's lifecycle.
Specifically, prioritize testing scenarios that involve manipulating the training data to inject malicious code, biases, or harmful content, and employ a diverse range of attack methods, including adversarial inputs, poisoning attacks, and model extraction techniques.
Advanced models like GPT-4 are often integrated into systems where they communicate with other applications.
In a model denial of service attack, an assailant engages with the model in a manner that excessively consumes resources, such as bandwidth or system processing power, ultimately impairing the availability of the targeted system.
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.
Identifying the right rate limit for your application will depend on model size and complexity, hardware and infrastructure, and the average number of requests and peak usage time.
Safeguarding LLMs requires a multifaceted approach, involving careful consideration of data handling, model training, system integration, and resource usage.
This Cyber News was published on www.helpnetsecurity.com. Publication date: Wed, 10 Jan 2024 06:43:05 +0000