Papers with denial-of-service
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection (2025.naacl-long)
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| Challenge: | Toolcalling has changed Large Language Model (LLM) applications by integrating external tools, but it also introduces new security vulnerabilities, particularly in the tool scheduling mechanisms of LLM, which have not been extensively studied. |
| Approach: | They propose a framework that exploits vulnerabilities in Large Language Models through adversarial tool injection to execute privacy theft, launch denial-of-service attacks, and manipulate business competition. |
| Outcome: | The proposed framework exploits vulnerabilities in LLM tool-calling systems through adversarial tool injection. |
PD3F: A Pluggable and Dynamic DoS-Defense Framework against resource consumption attacks targeting Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing work lacks mitigation strategies against resource consumption attacks . existing work does not provide mitigation strategies for real-world LLM deployments . |
| Approach: | They propose a pluggable and dynamic doS-Defense framework which employs a two-stage approach to defend against resource consumption attacks from both the input and output sides. |
| Outcome: | The proposed framework significantly mitigates resource consumption attacks, improving users’ access capacity by up to 500% during adversarial load. |
NaturalSloth: Revisiting Denial-of-Service Attacks on Large Language Models (2026.acl-long)
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| Challenge: | Longer generations consume more GPU time, increase latency, and reduce throughput in multi-tenant systems. |
| Approach: | They propose an adversarial dataset of natural instruction-based DoS prompts to scale the dataset while preserving malicious intent and increasing semantic diversity. |
| Outcome: | The proposed framework scales with a human-curated seed set of natural instruction-based DoS prompts while preserving malicious intent and increasing semantic diversity. |