0day vulnerabilities have become rubbish in the AI era.

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


πŸ“‹ Disclosed Vulnerabilities

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

# Product Affected Version CVSS Class Advisory & PoC
1 InterSystems IRIS 2026.1.0.234.1 9.8 Unauth RCE FolderManager Property Injection β†’ RCE
2 AdRem NetCrunch 16.0.0.8397 RC 9.8 Unauth RCE (SYSTEM) Cross-Session Hijack β†’ RCE
3 Altus BluePlant 9.1.40 9.8 Unauth RCE Hardcoded Credentials β†’ RCE
4 Brekeke SIP Server v3.19.1.8p1 9.8 Unauth RCE Nashorn JS Engine β†’ RCE
5 Brekeke SIP Server v3.19.1.8p1 9.8 Unauth RCE (Zip Slip) Zip Slip Webshell β†’ RCE
6 DataSunrise Suite 11.2.17.12820 9.8 Unauth RCE Email Verification Brute Force β†’ RCE
7 Cisco CUCM 14.0 9.8 RCE Chain Multi-stage RCE Chain
8 Brekeke SIP Server v3.19.1.8p1 9.1 Auth Bypass Auth Fail-Open β†’ 23 Unauth Beans
9 Acumatica ERP 2026 R1 8.8 Auth RCE Customization Publish Webshell β†’ RCE
10 AdRem NetCrunch 16.0.0.8397 RC 8.8 Auth RCE (SYSTEM) Startup Script β†’ RCE
11 Altus iX Developer 2.53.65422 7.3 Local/UI RCE XAML Deserialization β†’ RCE

Totals: 11 advisories Β· 8 vendors Β· 7 critical (CVSS β‰₯ 9.0) Β· 9 unauthenticated Β· all with reproducible PoC.


πŸ” This is an Ongoing Series β€” Not a One-Time Dump

This is Batch #1. The AI-driven discovery pipeline runs continuously, and new batches of verified 0-days with full PoCs are published on a regular cadence.

  • Last batch: 2026-07-15 (11 advisories)
  • Next batch: August 2026 β€” coming soon
  • 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 Β· πŸ‘ Watch (custom β†’ Releases + Discussions) for new batches Β· 🌐 Follow the blog for per-advisory updates.


🎯 Why This Exists

Traditional vulnerability disclosure is broken. It's slow, bureaucratic, and ineffective. In the AI era, we can mass-produce 0days at scaleβ€”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.

πŸ”„ Our Disclosure Process

Step 1: AI Discovery

Our automated AI systems continuously scan for vulnerabilities across real-world software, identifying potential 0days 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: Irregular Public Disclosure

We periodically disclose verified, exploitable 0day 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 excludedβ€”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

πŸ€– AI Models Used

Our automated vulnerability discovery leverages cutting-edge large language models:

  • GPT-5.5 - Advanced reasoning and code analysis
  • Claude Opus 4.8 - Deep security pattern recognition
  • DeepSeek V4 - Specialized vulnerability detection

πŸ“‚ Vulnerability Submission Format

All disclosed vulnerabilities follow a standardized directory structure:

product/
└── <product_name>/
    └── <version>/
        └── <vulnerability_type>/
            β”œβ”€β”€ exploit/          # Exploit scripts and PoC code
            β”œβ”€β”€ 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, microsoft, oracle)
  • /: Affected version range (e.g., 2.4.49, 11.0.12)
  • /: CVE-style classification (e.g., rce, sql-injection, auth-bypass)

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/
└── apache/
    └── 2.4.49/
        └── path-traversal/
            β”œβ”€β”€ exploit/
            β”‚   β”œβ”€β”€ poc.py
            β”‚   └── exploit.sh
            β”œβ”€β”€ analysis.md
            └── summary.md

Join us in redefining vulnerability disclosure for the AI era.