Abstract
Denial of service attacks on generative artificial intelligence systems, e.g., large language models (LLMs), can include sending LLMs requests that include expensive prompts designed to consume computing resources and degrade model performance. This disclosure describes techniques to automatically detect such prompts and then configure firewall rules that prevent such prompts in subsequent requests from reaching the LLM. Per the techniques, prompts provided to an LLM are matched against input and output token size as well as resource utilization to identify prompts that deviate significantly from a baseline. Expensive prompts are identified, and semantically similar prompts are automatically generated using the same LLM or another model. A subset of the generated prompts that are semantics similar to expensive prompts are identified by comparing respective vector embeddings. The subset of prompts and the received expensive prompts are provided to a pre-trained LLM that generates firewall rules, e.g., web application firewall (WAF) rules. Incoming requests from applications are evaluated based on the rules, and expensive prompts are blocked from reaching the LLM or are rate-limited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Namer, Assaf; Kulkarni, Prashant; Jeansson, Erik; Maltzman, Brandon; and Vagts, Hauke, "Automatically Detecting Expensive Prompts and Configuring Firewall Rules to Mitigate Denial of Service Attacks on Large Language Models", Technical Disclosure Commons, (January 29, 2024)
https://www.tdcommons.org/dpubs_series/6642