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