0day vulnerabilities have become rubbish in the AI era.
π Official Website: https://0day-rubbish.com/blog
π 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.
π 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:
β Star to bookmark Β· π Watch (custom β Releases + Discussions) for new batches Β· π Follow the blog for per-advisory updates.
π― Why This Exists
Traditional vulnerability disclosure is broken. It's slow, bureaucratic, and ineffective. In the AI era, we can mass-produce 0days at scaleβmaking 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.
π 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.
β‘ 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 excludedβthey'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
π€ Collaboration
We partner with:
- Top AI model providers advancing automated security research
- Security researchers exploring AI-powered discovery
π€ 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
π Vulnerability Submission Format
All disclosed vulnerabilities follow a standardized directory structure:
product/
βββ <product_name>/
βββ <version>/
βββ <vulnerability_type>/
βββ exploit/ # Exploit scripts and PoC code
βββ analysis.md # Detailed vulnerability analysis
βββ 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
- exploit/: Directory containing working exploit scripts and PoC code
- analysis.md: Comprehensive technical analysis including root cause, attack vector, and impact
- summary.md: Concise vulnerability overview with affected versions and quick mitigation steps
Example
product/
βββ apache/
βββ 2.4.49/
βββ path-traversal/
βββ exploit/
β βββ poc.py
β βββ exploit.sh
βββ analysis.md
βββ summary.md
Join us in redefining vulnerability disclosure for the AI era.
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