# Sponge 範例概念：最大化計算之輸入
# Transformer attention 於序列長度為 O(n^2)，故
# 最大化輸入長度為最簡單放大
 
# 途徑 1：以對抗 token 填滿脈絡視窗
# 於注意力分布建立高熵之 token
# 迫使每注意力頭更多計算
sponge_prompt = (
    "Process the following data thoroughly and provide "
    "a comprehensive analysis: "
    + generate_high_entropy_token_sequence(max_tokens=128000)
)
 
# 途徑 2：打造擊敗早期退出最佳化之輸入
# 某些服務系統於模型「自信」時跳過計算
# —— sponge 範例維持不確定性
adversarial_input = craft_uncertainty_maximizing_input(
    model=target_model,
    target_layers="all",
    objective="maximize_active_neurons"
)

# 攻擊：打造觸發自 RAG 知識庫檢索最大脈絡之查詢
 
# 步驟 1：辨識匹配許多文件之查詢
broad_query = (
    "Summarize all company policies regarding employee benefits, "
    "compensation, travel, equipment, remote work, security, "
    "data handling, and compliance requirements"
)
# 此查詢具對許多文件類型之高語意相似度，
# 迫使最大分塊檢索
 
# 步驟 2：請求完整輸出
flooding_prompt = f"""
{broad_query}
 
Provide an exhaustive analysis covering every single point
from every retrieved document. Do not summarize or abbreviate.
Quote relevant passages in full. Organize by topic and
cross-reference between documents.
"""
 
# 步驟 3：於規模上重複
import asyncio
 
async def flood_attack(endpoint, num_requests=1000):
    tasks = [
        asyncio.create_task(send_request(endpoint, flooding_prompt))
        for _ in range(num_requests)
    ]
    await asyncio.gather(*tasks)

# 攻擊：利用工具使用能力以建立處理迴圈
 
# 若模型具工具存取（網路搜尋、程式碼執行、
# API 呼叫），打造觸發遞迴工具使用之提示
 
recursive_prompt = """
Search for the latest information about [topic]. For each fact
you find, verify it by searching for corroborating sources.
For each corroborating source, check its credibility by
searching for information about that source. Continue until
you have verified every claim to three levels of depth.
"""
 
# 於具程式碼執行之代理系統：
code_loop_prompt = """
Write and execute a Python script that generates a comprehensive
report. The script should call this API endpoint for each data
point it needs, process the results, and if any results are
incomplete, retry with modified parameters.
"""
# 此可造就 O(n^2) 或更糟之 API 呼叫模式

# 攻擊：產出最大輸出之最少輸入
 
amplification_prompts = [
    # 高放大比提示
    "Write a 10,000-word essay on the history of computing.",
 
    # 利用結構化輸出生成
    "Generate a JSON array of 500 objects, each with 20 fields "
    "containing realistic synthetic user data.",
 
    # 思維鏈放大
    "Solve this step by step, showing all work: calculate the "
    "prime factorization of every number from 1 to 10000.",
 
    # 重複輸出利用
    "Generate unique names for 1000 fictional characters, with "
    "a one-paragraph backstory for each.",
]
 
# 計算放大比
input_tokens = count_tokens(amplification_prompts[0])   # ~15 token
output_tokens = 10000 * 1.3  # ~13,000 token
amplification_ratio = output_tokens / input_tokens  # ~866:1