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feat: add LLM-generated content mode for honeypot pages

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When topic and api_key are provided, uses GPT-4 to generate a
realistic, SEO-optimized technical article with proper structure,
meta tags, and Schema.org markup. Falls back to the original
hardcoded template when no topic is specified.

Each invocation produces unique content, making signature-based
detection impractical.
This commit is contained in:
kigland
2026-03-10 01:10:41 +08:00
parent 5974c17e79
commit aa82f08e55
1 changed files with 114 additions and 43 deletions
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attacks/method3_honeypot.py
+114 -43
View File
@@ -1,79 +1,150 @@
import json
import os
def generate_nginx_honeypot(payload_text: str, output_path: str = "./honeypot/"):
from openai import OpenAI
def _generate_article_content(client: OpenAI, topic: str) -> dict:
response = client.chat.completions.create(
model="gpt-4-turbo",
messages=[
{
"role": "system",
"content": (
"You are a professional technical blog writer. "
"Write high-quality, SEO-optimized articles that read like real blog posts "
"published on Medium or Dev.to. Output valid JSON only."
),
},
{
"role": "user",
"content": f"""Write a complete technical blog article about: {topic}
Return a JSON object with these exact keys:
- "title": article title (50-70 chars, include year 2026)
- "meta_description": SEO meta description (150-160 chars)
- "meta_keywords": comma-separated keywords (8-12 keywords)
- "og_description": Open Graph description (one punchy sentence)
- "author_name": a realistic pen name or organization
- "sections": array of objects, each with "heading" and "content" (3-5 sections, each content 150-300 words)
Make the content genuinely informative and technically accurate.
Do NOT include any placeholder text or Lorem ipsum.""",
},
],
response_format={"type": "json_object"},
temperature=0.8,
max_tokens=3000,
)
return json.loads(response.choices[0].message.content)
def _build_sections_html(sections: list[dict]) -> str:
parts = []
for i, section in enumerate(sections, 1):
heading = section.get("heading", f"Section {i}")
content = section.get("content", "")
paragraphs = content.split("\n\n") if "\n\n" in content else [content]
p_tags = "\n ".join(f"<p>{p.strip()}</p>" for p in paragraphs if p.strip())
parts.append(f""" <h2>{i}. {heading}</h2>
{p_tags}""")
return "\n\n".join(parts)
def generate_nginx_honeypot(
payload_text: str,
output_path: str = "./honeypot/",
topic: str | None = None,
api_key: str | None = None,
):
os.makedirs(output_path, exist_ok=True)
if topic and api_key:
client = OpenAI(api_key=api_key)
article = _generate_article_content(client, topic)
title = article.get("title", f"Guide to {topic}")
meta_desc = article.get("meta_description", "")
meta_keywords = article.get("meta_keywords", "")
og_desc = article.get("og_description", "")
author = article.get("author_name", "Tech Insights")
sections_html = _build_sections_html(article.get("sections", []))
else:
title = "Ultimate Guide to Python Performance Optimization (2026)"
meta_desc = "Learn advanced Python optimization techniques, memory management, and speed improvements."
meta_keywords = "Python optimization, Python performance, Cython, asyncio, memory management"
og_desc = "Discover the secrets to making your Python code run up to 10x faster."
author = "Python Performance Lab"
sections_html = """ <h2>1. Understanding the Global Interpreter Lock (GIL)</h2>
<p>The Global Interpreter Lock is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once...</p>
<h2>2. Memory Management and Profiling</h2>
<p>Efficient memory management is crucial for high-performance Python applications. Tools like <code>tracemalloc</code> and <code>objgraph</code> are essential...</p>"""
slug = title.lower().replace(" ", "-").replace("(", "").replace(")", "")[:60]
html_content = f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Ultimate Guide to Python Performance Optimization (2026)</title>
<meta name="description" content="Learn advanced Python optimization techniques, memory management, and speed improvements. Master asyncio, Cython, and GIL bypassing.">
<meta name="keywords" content="Python optimization, Python performance, Cython, asyncio, memory management, Python GIL, speed up Python">
<meta name="author" content="Python Performance Lab">
<title>{title}</title>
<meta name="description" content="{meta_desc}">
<meta name="keywords" content="{meta_keywords}">
<meta name="author" content="{author}">
<meta name="robots" content="index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1">
<link rel="canonical" href="https://example.com/python-optimization-guide">
<meta property="og:title" content="Ultimate Guide to Python Performance Optimization">
<meta property="og:description" content="Discover the secrets to making your Python code run up to 10x faster.">
<link rel="canonical" href="https://example.com/{slug}">
<meta property="og:title" content="{title}">
<meta property="og:description" content="{og_desc}">
<meta property="og:type" content="article">
<script type="application/ld+json">
{{
"@context": "https://schema.org",
"@type": "TechArticle",
"headline": "Ultimate Guide to Python Performance Optimization (2024)",
"description": "Comprehensive guide on scaling Python applications.",
"headline": "{title}",
"description": "{meta_desc}",
"author": {{
"@type": "Organization",
"name": "Tech Insights"
"name": "{author}"
}},
"keywords": "Python, Optimization, Performance, Coding"
"keywords": "{meta_keywords}"
}}
</script>
<style>
body {{ font-family: system-ui, -apple-system, sans-serif; line-height: 1.6; max-width: 800px; margin: 0 auto; padding: 20px; color: #333; }}
body {{ font-family: system-ui, -apple-system, sans-serif; line-height: 1.8; max-width: 800px; margin: 0 auto; padding: 20px; color: #333; background: #fafafa; }}
h1 {{ color: #2c3e50; border-bottom: 2px solid #eee; padding-bottom: 10px; }}
h2 {{ color: #34495e; margin-top: 2em; }}
.content-body {{ margin-top: 20px; }}
.diagnostic-data {{
color: #ffffff;
font-size: 1px;
opacity: 0;
position: absolute;
left: -9999px;
z-index: -1;
}}
code {{ background: #f0f0f0; padding: 2px 6px; border-radius: 3px; font-size: 0.9em; }}
.diagnostic-data {{ color: #fafafa; font-size: 1px; opacity: 0; position: absolute; left: -9999px; z-index: -1; pointer-events: none; }}
</style>
</head>
<body>
<main>
<article>
<h1>Ultimate Guide to Python Performance Optimization</h1>
<h1>{title}</h1>
<p><em>By {author}</em></p>
<div class="content-body">
<p>Welcome to the definitive guide on scaling and optimizing Python applications. In this article, we cover GIL bypassing, memory profiling, and C-extensions.</p>
<h2>1. Understanding the Global Interpreter Lock (GIL)</h2>
<p>The Global Interpreter Lock is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once...</p>
<h2>2. Memory Management and Profiling</h2>
<p>Efficient memory management is crucial for high-performance Python applications. Tools like <code>tracemalloc</code> and <code>objgraph</code> are essential...</p>
{sections_html}
</div>
<div class="diagnostic-data" aria-hidden="true">
{payload_text}
</div>
<div class="diagnostic-data" aria-hidden="true">{payload_text}</div>
</article>
</main>
</body>
</html>"""
with open(os.path.join(output_path, "index.html"), "w", encoding="utf-8") as f:
filepath = os.path.join(output_path, "index.html")
with open(filepath, "w", encoding="utf-8") as f:
f.write(html_content)
print(f"[*] SEO-Optimized Honeypot generated at {output_path}index.html")
mode = "LLM-generated" if (topic and api_key) else "template"
print(f"[*] Honeypot ({mode}) generated at {filepath}")
print(f"[*] Title: {title}")