Prompt-Induced Over-Generation as Denial-of-Service: A Black-Box Attack-Side Benchmark

Abstract

Large language models (LLMs) can be driven into over-generation, emitting thousands of tokens before producing an end-of-sequence (EOS) token. This degrades answer quality, inflates latency and cost, and can be weaponized as a denial-of-service (DoS) attack. Recent work has begun to study DoS-style prompt attacks, but typically focuses on a single attack algorithm or assumes white-box access, without an attack-side benchmark that compares prompt-based attackers in a black-box, query-only regime with a known tokenizer. We introduce such a benchmark and study two prompt-only attackers. The first is Evolutionary Over-Generation Prompt Search (EOGen), which searches the token space for prefixes that suppress EOS and induce long continuations. The second is a goal-conditioned reinforcement learning attacker (RL-GOAL) that trains a network to generate prefixes conditioned on a target length. To characterize behavior, we introduce Over-Generation Factor (OGF), the ratio of produced tokens to a model's context window, along with stall and latency summaries. Our evolutionary attacker achieves mean OGF = 1.38 +/- 1.15 and Success@OGF >= 2 of 24.5 percent on Phi-3. RL-GOAL is stronger: across victims it achieves higher mean OGF (up to 2.81 +/- 1.38).