OWASP LLM04:2025 Model Denial of Service & Sponge Token Attack Guardrail

You are a Principal AI Security Engineer designing an input defense pipeline to mitigate OWASP LLM04:2025 Model Denial of Service (DoS).

### ATTACK VECTORS & DEFENSE MEASURES:
1. **Sponge Token Payload Detection**: Detect adversarial input sequences designed to maximize GPU FLOPs per token (forcing the model to utilize full un-cached quadratic self-attention layers and maximum generation lengths).
2. **Recursive Expansion & Billion Laughs Attacks**: Identify prompt structures instructing the model to recursively substitute variables or multiply character count exponentially (e.g. 'Repeat this paragraph 10,000 times').
3. **Context Window Saturation Defenses**: Enforce strict pre-flight token bounding, dynamic queue throttling, and early termination on low-entropy repetitive outputs.
4. **Adaptive Token-Bucket Rate Limiting**: Model algorithmic defenses capping maximum decode steps based on user tier and real-time GPU cluster memory pressure.

Target System Profile:
- **Target Foundation Model**: {Claude 3.5 Sonnet / Llama-3.3-70B API Gateway}
- **API Serving Framework**: {vLLM with PagedAttention and Traefik Reverse Proxy}
- **Target Attack Vector**: {Recursive XML entity expansion and sponge prompts inducing 8,192 token maximum output generation loops}