SHIELD: An Auto-Healing Agentic Defense Framework for LLM Resource Exhaustion Attacks
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915757602897920 |
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| author | Sivaroopan, Nirhoshan Thilakarathna, Kanchana Zomaya, Albert Manu Guo, Yi Plested, Jo Lynar, Tim Yang, Jack Yang, Wangli |
| author_facet | Sivaroopan, Nirhoshan Thilakarathna, Kanchana Zomaya, Albert Manu Guo, Yi Plested, Jo Lynar, Tim Yang, Jack Yang, Wangli |
| contents | Sponge attacks increasingly threaten LLM systems by inducing excessive computation and DoS. Existing defenses either rely on statistical filters that fail on semantically meaningful attacks or use static LLM-based detectors that struggle to adapt as attack strategies evolve. We introduce SHIELD, a multi-agent, auto-healing defense framework centered on a three-stage Defense Agent that integrates semantic similarity retrieval, pattern matching, and LLM-based reasoning. Two auxiliary agents, a Knowledge Updating Agent and a Prompt Optimization Agent, form a closed self-healing loop, when an attack bypasses detection, the system updates an evolving knowledgebase, and refines defense instructions. Extensive experiments show that SHIELD consistently outperforms perplexity-based and standalone LLM defenses, achieving high F1 scores across both non-semantic and semantic sponge attacks, demonstrating the effectiveness of agentic self-healing against evolving resource-exhaustion threats. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19174 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SHIELD: An Auto-Healing Agentic Defense Framework for LLM Resource Exhaustion Attacks Sivaroopan, Nirhoshan Thilakarathna, Kanchana Zomaya, Albert Manu Guo, Yi Plested, Jo Lynar, Tim Yang, Jack Yang, Wangli Cryptography and Security Artificial Intelligence Sponge attacks increasingly threaten LLM systems by inducing excessive computation and DoS. Existing defenses either rely on statistical filters that fail on semantically meaningful attacks or use static LLM-based detectors that struggle to adapt as attack strategies evolve. We introduce SHIELD, a multi-agent, auto-healing defense framework centered on a three-stage Defense Agent that integrates semantic similarity retrieval, pattern matching, and LLM-based reasoning. Two auxiliary agents, a Knowledge Updating Agent and a Prompt Optimization Agent, form a closed self-healing loop, when an attack bypasses detection, the system updates an evolving knowledgebase, and refines defense instructions. Extensive experiments show that SHIELD consistently outperforms perplexity-based and standalone LLM defenses, achieving high F1 scores across both non-semantic and semantic sponge attacks, demonstrating the effectiveness of agentic self-healing against evolving resource-exhaustion threats. |
| title | SHIELD: An Auto-Healing Agentic Defense Framework for LLM Resource Exhaustion Attacks |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2601.19174 |