0day Rubbish

0day vulnerabilities have become rubbish in the AI era.

🌐 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

Roughly every two weeks 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 7 (10 advisories)

Totals: 10 advisories · 10 vendors · 5 unauthenticated · 5 authenticated (deep-chain) · 4 system-level (root/SYSTEM/LocalSystem) · all with reproducible PoC.

Earlier batches: Batch #1 · Batch #2 · Batch #3 · Batch #4 · Batch #5 · Batch #6

🔁 An Ongoing Series — Weekly Disclosures

This is a continuous disclosure series. Thanks to continuous optimization, the AI-driven discovery pipeline now produces new 0-day findings at a stable daily rate, and we disclose verified batches on a weekly cadence.

• Latest batch: Batch 7 — 10 advisories (live); cumulative 68 across 7 batches
• Next drop: weekly
• Future scope: expanding beyond enterprise IT into ICS / SCADA, energy, and aerospace systems

If you want to catch the next drop the moment it lands:

⭐ Star to bookmark · 👁 Watch (custom → Releases + Discussions) for new batches · 🌐 Follow the blog for per-advisory updates.

📂 Vulnerability Submission Format

All disclosed vulnerabilities follow a standardized directory structure:

product/
└── <vendor>/
    └── <version>/
        └── <vulnerability_type>/
            ├── exploit/          # Exploit scripts and PoC code
            ├── analysis.md       # Detailed vulnerability analysis
            └── summary.md        # Brief vulnerability overview

Directory Rules

• product/: Root directory for all vulnerabilities
• /: Vendor or product name (e.g., apache, cisco, sonicwall)
• /: Affected version range (e.g., 6.11.0, 12.4.2)
• <vulnerability_type>/: Classification (e.g., unauth-rce, auth-bypass, deserialization-rce)

Required Files in Each Vulnerability Directory

1. exploit/: Directory containing working exploit scripts and PoC code
2. analysis.md: Comprehensive technical analysis including root cause, attack vector, and impact
3. summary.md: Concise vulnerability overview with affected versions and quick mitigation steps

Example

product/
└── sonicwall/
    └── sma-12.4/
        └── preauth-deserialization-rce/
            ├── exploit/
            │   └── poc.py
            ├── analysis.md
            └── summary.md

Join us in redefining vulnerability disclosure for the AI era.