0day Rubbish
August 31, 2026 Β· View on GitHub
0day vulnerabilities have become rubbish in the AI era.
π Official Website: https://0day-rubbish.com/blog
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 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.
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 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
π 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 β no detection-only writeups, no withheld details.
Latest Batch β Batch 9 (12 advisories)
| # | Product | Affected Version | CVSS | Class | Advisory & PoC |
|---|---|---|---|---|---|
| 1 | NoMachine Terminal Server (VULN-001) | 10.0.57 | 9.8 | Pre-auth heap corruption (CWE-787β416), RCE-capable | parsePOST heap β corruption |
| 2 | NoMachine Terminal Server (VULN-002) | 10.0.57 | 9.8 | Pre-auth stack overflow, return-address control | parsePOST sprintf β RIP control |
| 3 | StreamSets DataCollector | 6.4.1 | 9.8 | Default creds + Shell Executor β Root | ShellDExecutor β Root RCE |
| 4 | Akana API Platform | 8.4.29 | 9.8 | Unauth path-normalization bypass β ScriptEngine RCE | admin/../ext β engine.eval RCE |
| 5 | Puppet Enterprise | 2025.10.0 | 8.8 | Auth keytool shell injection β Root (CVE-2025-5459 bypass) | java_keystore_passwd β Root RCE |
| 6 | Minuteman UPS NMC | 1.60.3 | 9.8 | Unauth system_param.csp Cmd Injection β Root | WAN config β Root RCE |
| 7 | Lantronix EDS3000PR (VULN-001) | 3.2.0.0R2 | 8.8 | Auth FsUnmount Cmd Injection β Root | FsUnmount path β Root RCE |
| 8 | Lantronix EDS3000PR (VULN-002) | 3.2.0.0R2 | 8.8 | Auth SSL -passin pass:%s Cmd Injection β Root | keytool pass β Root RCE |
| 9 | GeoVision GV-TBL4700 | V1.06 | 8.8 | Auth SNMPv3 net-snmp-config Cmd Injection β Root | szAuthKey β Root RCE |
| 10 | DrayTek Vigor 2960 | v1.5.1.6 | 8.8 | Auth uploadlangs Cmd Injection β Root | cgiEscape gap β Root RCE |
| 11 | Codoforum | 5.4.1 | 7.2 | Auth cat_img polyglot upload β www-data | polyglot upload β RCE |
| 12 | ZesleCP | 3.1.21 | 8.8 | Auth arbitrary file write β cron β Root | save-file β cron Root RCE |
Totals: 12 advisories Β· 10 vendors Β· 4 unauthenticated Β· 8 authenticated (deep-chain) Β· 10 system-level (root/SYSTEM) Β· all with reproducible PoC.
Earlier batches: Batch #1 Β· Batch #2 Β· Batch #3 Β· Batch #4 Β· Batch #5 Β· Batch #6 Β· Batch #7 Β· Batch #8
π An Ongoing Series β 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 9 β 12 advisories (draft); cumulative 90 across 9 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:
β Star to bookmark Β· π Watch (custom β Releases + Discussions) for new batches Β· π Follow the blog for per-advisory updates.
π Vulnerability Submission Format
All disclosed vulnerabilities follow a standardized directory structure:
product/
βββ <vendor>/
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,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
- 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 sonicwall/
βββ sma-12.4/
βββ preauth-deserialization-rce/
βββ exploit/
β βββ poc.py
βββ analysis.md
βββ summary.md
Join us in redefining vulnerability disclosure for the AI era.