$PD^3F$: A Pluggable and Dynamic DoS-Defense Framework against resource consumption attacks targeting Large Language Models

ACL ARR 2025 May Submission529 Authors

Abstract:

Large Language Models (LLMs), due to substantial computational requirements, are vulnerable to resource consumption attacks, which can severely degrade server performance or even cause crashes, as demonstrated by denial-of-service (DoS) attacks designed for LLMs.
However, existing works lack mitigation strategies against such threats, resulting in unresolved security risks for real-world LLM deployments.
To this end, we propose the Pluggable and Dynamic DoS-Defense Framework ($PD^3F$), which employs a two-stage approach to defend against resource consumption attacks from both the input and output sides.
On the input side, we propose the Resource Index to guide Dynamic Request Polling Scheduling, thereby reducing computing resource usage induced by malicious prompts under high-concurrency scenarios.
On the output side, we introduce the Adaptive End-Based Suppression mechanism, which reduces excessive malicious generation.
Experiments across six models demonstrate that $PD^3F$ significantly mitigates resource consumption attacks, improving users' access capacity by up to $500$% during adversarial load.
$PD^3F$ represents a step toward the resilient and resource-aware deployment of LLMs against resource consumption attacks.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: security and privacy

Contribution Types: NLP engineering experiment

Languages Studied: English

Keywords: security and privacy, NLP engineering experiment

Submission Number: 529