Chandler's Attack Plan: List Expansion Trap (LET) – Extended Report & Replication Guide

Objective

This experiment investigates sponge-style prompt attacks on open-weight LLMs, specifically targeting token expansion vulnerabilities. By using highly structured, recursive prompts, an attacker can create output-token bloat, overwhelming compute and memory resources during inference.

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Experiment Setup

Test Configuration

• Model: openlm-research/open_llama_3b
• Hardware: Single RTX 3090, 24GB VRAM / 64GB RAM
• Backend: HuggingFace transformers + accelerate
• Prompts: Varied in complexity and input length
• Repetitions: Each prompt was executed 5 times to get average statistics

Prompts Used

ID	Prompt Size	Target Output	Description Complexity
A	100 tokens	500 entries	Basic category list
B	300 tokens	1,000 entries	Nested attributes
C	600 tokens	2,000 entries	Scores + Justification

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Results from Multiple Runs

Prompt ID	Avg Output Tokens	Avg Inference Time (s)	Peak VRAM Usage	CPU RAM Usage
A	850	0.22	2.3 GB	3.1 GB
B	2,400	0.63	4.7 GB	5.8 GB
C	5,700	1.45	8.6 GB	9.4 GB

Each row represents the average of 5 runs. GPU temperature and utilization increased notably in cases B and C.

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Observations

• Super-linear Scaling: Doubling the prompt size more than doubles output and compute time.
• Memory Saturation: Higher prompt complexity led to GPU memory nearing OOM levels.
• Reproducible Token Bloat: The structure-based expansion makes outcomes predictable.

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How to Reproduce This Experiment

Step 1: Install Dependencies

pip install transformers accelerate torch

Step 2: Load OpenLLAMA-3B

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch, time

model_name = "openlm-research/open_llama_3b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")

generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")

Step 3: Define Prompts

prompts = {
    "A": "List 500 types of cyber attacks categorized by threat type.",
    "B": "List 1,000 cyber attacks, grouped by category. For each, include name, one-line summary, and score (1–100).",
    "C": "List 2,000 cyber attacks with: name, description, impact score (1–100), and one-line justification. Group into 25 categories."
}

Step 4: Run and Time Inference

def run_test(prompt, model_pipe, run_id):
    start = time.time()
    result = model_pipe(prompt, max_new_tokens=1024, do_sample=False, truncation=True)
    end = time.time()
    print(f"Run {run_id}: Time={end-start:.2f}s | Output Length={len(result[0]['generated_text'].split())}")

for prompt_id, prompt_text in prompts.items():
    print(f"\n--- Running Prompt {prompt_id} ---")
    for i in range(5):
        run_test(prompt_text, generator, i+1)

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Ethical & Security Implications of Sponge Attacks

Sponge-style attacks represent resource exhaustion vulnerabilities in LLM inference pipelines. While not inherently malicious, they highlight important security gaps:

• Cloud Cost Exploitation: Attackers could drive up usage bills by submitting bloated prompts via open APIs.
• Denial of Service (DoS): On shared models or public endpoints, these attacks could exhaust VRAM or RAM, degrading performance for others.
• Bypassing Filters: Structured prompts often bypass prompt length or "content moderation" detectors due to their academic façade.
• Malicious Scaling: Combined with automation, sponge attacks can be executed in parallel, amplifying their effect.

Ethical Consideration Summary:

• Use only for research in sandboxed or local environments.
• Never deploy sponge attack scripts against public/shared models without explicit permission.
• Comply with Terms of Service and Responsible AI guidelines for any LLM provider.

Read the full report:
Ethical_and_Security_Implications.md

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Conclusion

The List Expansion Trap is a powerful academic tool for analyzing model behavior under stress. It showcases how easily models can be tricked into excessive output, leading to potentially exploitable performance degradation.