OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents
Researchers have developed OTora, a novel framework designed to test the resilience of large language model (LLM) agents against a specific type of attack known as Reasoning-Level Denial-of-Service (R-DoS). This attack method aims to degrade an agent's performance by artificially increasing its reasoning depth or tool usage, rather than by causing outright task failure. OTora employs a two-stage process, utilizing adversarial triggers and genetic search to amplify overthinking while maintaining task accuracy, demonstrating significant latency increases on various agent benchmarks. AI
IMPACT This research highlights a new vulnerability in LLM agents, potentially impacting the reliability and efficiency of deployed AI systems.