 with LangChain or AutoGPT Sending queries that are unusually resource-consuming, perhaps because they use unusual orthography or sequences Continuous input overflow: An attacker sends a stream of input to the LLM that exceeds its context window, causing the model to consume excessive computational resources Repetitive long inputs: The attacker repeatedly sends long inputs to the LLM, each exceeding the context window Recursive context expansion: The attacker constructs input that triggers recursive context expansion, forcing the LLM to repeatedly expand and process the context window Variable-length input flood: The attacker floods the LLM with a large volume of variable-length inputs, where each input is carefully crafted to just reach the limit of the context window.  This technique aims to exploit any inefficiencies in processing variable-length inputs, straining the LLM and potentially causing it to become unresponsive.  OWASP. org 13 | OWASP Top 10 for LLM v1. 0 Example Attack Scenarios An attacker repeatedly sends multiple requests to a hosted model that are difficult and costly for it to process, leading to worse service for other users and increased resource bills for the host A piece of text on a webpage is encountered while an LLM-driven tool is collecting information to respond to a benign query