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0day-Rubbish — Redefining vulnerability disclosure in the AI era. We mass-produce exploitable 0days and disclose them directly, using event-driven pressure to elevate vendor security standards and advance the field. | Kitploit
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0day-Rubbish

Redefining vulnerability disclosure in the AI era. We mass-produce exploitable 0days and disclose them directly, using event-driven pressure to elevate vendor security standards and advance the field.

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0day Rubbish

0day vulnerabilities have become rubbish in the AI era.

License: MIT Latest batch Max CVSS PoC Website Watchers Discussions Last commit

🌐 Official Website: https://0day-rubbish.com/blog


🎯 Why This Exists

Traditional vulnerability disclosure is broken. It's slow, bureaucratic, and ineffective. In the AI era, we can mass-produce 0days at scale—making individual vulnerabilities less valuable but more impactful when disclosed directly.

We believe event-driven security hardening is the most effective approach: only when vendors face real, exploitable threats do they prioritize fixes.

🔄 Our Disclosure Process

Step 1: AI Discovery

Our automated AI systems continuously scan for vulnerabilities across real-world software, identifying potential 0-days through pattern analysis, fuzzing, and intelligent code review.

Step 2: Verification & PoC Development

Each finding undergoes manual validation. We develop working proof-of-concept exploits to confirm exploitability and assess real-world impact.

Step 3: Periodic Public Disclosure

Every week — Monday or Tuesday — we disclose a new batch of verified, exploitable 0-day vulnerabilities we've discovered and validated:

  • Full technical analysis and root cause
  • Working PoC exploit code
  • Affected versions and systems
  • Impact assessment
  • Recommended mitigations

No delays. No bureaucracy. Just facts.

To all vendors: We hope you can complete fixes before hackers exploit these vulnerabilities.

⚡ Core Principles

  • Real-world impact only: We disclose only vulnerabilities that affect real-world systems with actual user bases
  • No worthless targets: Non-exploitable vulnerabilities or devices with negligible user adoption are excluded—they're rubbish with zero value
  • Speed over protocol: Direct disclosure drives faster action than traditional channels
  • Proof over claims: Every disclosure includes working exploits
  • Impact over quantity: Focus on high-severity, widely-deployed vulnerabilities
  • Transparency: Full technical details, no hidden agendas
  • Non-profit: Driven by passion for security research, not financial gain

🤝 Collaboration

We partner with:

  • Top AI model providers advancing automated security research
  • Security researchers exploring AI-powered discovery

🤖 AI Models Used

Our automated vulnerability discovery leverages cutting-edge large language models from leading AI providers:

  • Anthropic (Claude) - Deep security pattern recognition and reasoning
  • OpenAI - Advanced reasoning and code analysis
  • DeepSeek - Specialized vulnerability detection
  • Z.ai (GLM) - Long-context code analysis
  • Moonshot (Kimi) - Long-context security analysis

📋 Disclosed Vulnerabilities

An AI-driven research process (multi-LLM ensemble: Claude, OpenAI, DeepSeek, GLM, Kimi) discovers 0-days in real-world enterprise software. Every advisory below ships a full root-cause analysis plus a working, reproducible exploit script — no detection-only writeups, no withheld details.

Latest Batch — Batch 15 (7 advisories)

Execution surfaces that trust their own operators and their own configuration: a license gate that compares a raw request URI instead of the routed path and then issues an administrative principal to a caller holding no credential, a database that ships its own system password and offers a scripting language to whoever holds it, a render farm whose job manager performs no identity check unless somebody switches one on, a firmware upgrade that is executed without ever being verified, and two appliances whose management forms format one user-supplied field straight into a shell. None of the seven needed a memory-corruption bug or a novel primitive. Each is a product doing precisely what it was written to do, with one check missing.

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