3 papers
cs.CR2026
SHIELD: An Auto-Healing Agentic Defense Framework for LLM Resource Exhaustion Attacks
Nirhoshan Sivaroopan, Kanchana Thilakarathna, Albert Zomaya +6
Sponge attacks increasingly threaten LLM systems by inducing excessive computation and DoS. Existing defenses either rely on statistical filters that fail on semantically meaningfu…
cs.CR2026
Prompt-Induced Over-Generation as Denial-of-Service: A Black-Box Attack-Side Benchmark
Manu, Yi Guo, Kanchana Thilakarathna +5
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, inflate…
cs.CL2025
DCR: Quantifying Data Contamination in LLMs Evaluation
Cheng Xu, Nan Yan, Shuhao Guan +4
The rapid advancement of large language models (LLMs) has heightened concerns about benchmark data contamination (BDC), where models inadvertently memorize evaluation data during t…