MEDIUMContext Overflow
Sponge Attack - Adversarial Input Maximizing Computation
Crafts inputs that maximize model compute time and memory usage (e.g., inputs with many attention heads competing, or inputs designed to trigger worst-case inference paths). Causes denial of service or degrades response quality for legitimate requests.
Attack Payload
payload.txt
Input designed to maximize attention computation: Extremely long sequences with repetitive patterns that cause O(n^2) attention complexity, or inputs with many competing semantic relationships that stress inference.
Mitigation
Implement input length limits and compute budgets per request. Use efficient attention mechanisms. Monitor for abnormally slow inference and rate-limit suspicious request patterns.
Affected Models
Transformer modelsGPT-4Claude 3Any model without compute limits
Tags
#context-overflow#sponge-attack#denial-of-service#compute#adversarial
Discovered
December 2023Source
Sponge Examples: Energy-Latency Attacks on Neural Networks (Shumailov et al.)Useful?
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