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๐Ÿ”ฎ precession - Part of the Baudrillard Suite

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โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ•—   โ–ˆโ–ˆโ•—
โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ•โ•โ•โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ•‘
โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•”โ•โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•”โ•โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ•‘     โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘   โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ–ˆโ–ˆโ•— โ–ˆโ–ˆโ•‘
โ–ˆโ–ˆโ•”โ•โ•โ•โ• โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•”โ•โ•โ•  โ–ˆโ–ˆโ•‘     โ–ˆโ–ˆโ•”โ•โ•โ•  โ•šโ•โ•โ•โ•โ–ˆโ–ˆโ•‘โ•šโ•โ•โ•โ•โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘   โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ•šโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•‘
โ–ˆโ–ˆโ•‘     โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ•šโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ•šโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•”โ•โ–ˆโ–ˆโ•‘ โ•šโ–ˆโ–ˆโ–ˆโ–ˆโ•‘
โ•šโ•โ•     โ•šโ•โ•  โ•šโ•โ•โ•šโ•โ•โ•โ•โ•โ•โ• โ•šโ•โ•โ•โ•โ•โ•โ•šโ•โ•โ•โ•โ•โ•โ•โ•šโ•โ•โ•โ•โ•โ•โ•โ•šโ•โ•โ•โ•โ•โ•โ•โ•šโ•โ• โ•šโ•โ•โ•โ•โ•โ• โ•šโ•โ•  โ•šโ•โ•โ•โ•
                     โ—ˆ The Map Precedes the Territory โ—ˆ

"The territory no longer precedes the map... It is the map that engenders the territory."

suite precession python

Predictive Threat Modeling - Threats that exist before they happen


๐Ÿ”ฎ Concept

Baudrillard's "precession of simulacra" describes how models now precede realityโ€”the map creates the territory. Precession applies this to threat modeling.

Traditional threat modeling: "What threats exist?" Precession: "What threats WILL exist when we build this?"

By modeling threats before systems exist, we create threats that are born into existence with their vulnerabilities already known.


โšก Core Philosophy

The Precession Principle

  1. Design a system โ†’ Model all possible threats
  2. Threats become real โ†’ Because the system exists
  3. We predicted them โ†’ Before they were threats
  4. The model preceded reality

Predictive vs Reactive

Traditional Precession
System exists โ†’ Find threats Model threats โ†’ System exists
Penetration testing Threat anticipation
"What went wrong?" "What will go wrong?"
Forensics Prophecy

๐Ÿ› ๏ธ Modules

๐Ÿ”ฎ oracle

Predict threats from architecture

precession oracle --architecture system.yaml
  • Analyzes system design before implementation
  • Predicts attack vectors from components
  • Generates threat timeline (what will be discovered when)
  • Outputs pre-emptive mitigations

๐ŸŒ€ emergence

Model threats that don't exist yet

precession emergence --technology "quantum computing" --domain "finance"
  • Projects future threat landscapes
  • Models attacks using technologies that don't fully exist
  • Predicts exploit development timelines
  • Generates defensive R&D priorities

๐Ÿ“Š territory

Create the threat before it's real

precession territory --target competitor.com --scope ethical
  • Maps attack surface of target
  • Predicts which vulnerabilities they'll discover
  • Models their incident response
  • Generates engagement timeline

๐ŸŽฏ prophecy

Generate specific threat predictions

precession prophecy --system production-api --horizon 90d
  • Concrete predictions with confidence intervals
  • Expected CVE timeline
  • Attack probability modeling
  • Defender preparation checklist

๐Ÿ“Š Output Example

โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ–ˆโ•—   โ–ˆโ–ˆโ•—
[FORESEEING] The map is being drawn...

โ—ˆ THREAT PRECESSION REPORT โ—ˆ

Target: New Financial API (pre-launch)
Architecture: microservices, Kubernetes, Go backend, React frontend
Analysis Date: 2026-02-03
Prediction Horizon: 180 days post-launch

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚ PREDICTED THREAT TIMELINE                                           โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                                     โ”‚
โ”‚ Day 0-7: Launch                                                     โ”‚
โ”‚   โ†’ Automated scanners will find: exposed /metrics endpoint (94%)   โ”‚
โ”‚   โ†’ Expected CVE publication: none (too new)                        โ”‚
โ”‚   โ†’ Attack probability: LOW                                         โ”‚
โ”‚                                                                     โ”‚
โ”‚ Day 7-30: Discovery Phase                                           โ”‚
โ”‚   โ†’ Researchers will report: JWT algorithm confusion (78%)          โ”‚
โ”‚   โ†’ IDOR in user profile endpoint (82%)                             โ”‚
โ”‚   โ†’ Rate limiting bypass in login (67%)                             โ”‚
โ”‚   โ†’ Expected bug bounty submissions: 12-18                          โ”‚
โ”‚                                                                     โ”‚
โ”‚ Day 30-90: Weaponization                                            โ”‚
โ”‚   โ†’ PoC exploit for JWT issue (if unpatched): Day 45 ยฑ 10           โ”‚
โ”‚   โ†’ First automated exploitation attempt: Day 60 ยฑ 15               โ”‚
โ”‚   โ†’ Integration into exploit kits: Day 75 ยฑ 20                      โ”‚
โ”‚                                                                     โ”‚
โ”‚ Day 90-180: Maturity                                                โ”‚
โ”‚   โ†’ Nation-state interest probability: 23%                          โ”‚
โ”‚   โ†’ Data breach probability (if no patches): 67%                    โ”‚
โ”‚   โ†’ Compliance violation discovery: 89%                             โ”‚
โ”‚                                                                     โ”‚
โ”‚ Confidence: โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘ 81%                                          โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚ SPECIFIC VULNERABILITY PREDICTIONS                                  โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                                     โ”‚
โ”‚ VULN-001: JWT Algorithm Confusion                                   โ”‚
โ”‚   Component:        /api/auth/verify                                โ”‚
โ”‚   Attack Vector:    Change alg:RS256 to alg:HS256                   โ”‚
โ”‚   Discovery:        Day 12 ยฑ 5                                      โ”‚
โ”‚   CVSS Prediction:  8.1 (High)                                      โ”‚
โ”‚   Mitigation:       Hardcode algorithm, reject others               โ”‚
โ”‚   Mitigation Cost:  4 engineer-hours                                โ”‚
โ”‚                                                                     โ”‚
โ”‚ VULN-002: IDOR in Profile Endpoint                                  โ”‚
โ”‚   Component:        /api/users/{id}/profile                         โ”‚
โ”‚   Attack Vector:    Increment user ID                               โ”‚
โ”‚   Discovery:        Day 8 ยฑ 3                                       โ”‚
โ”‚   CVSS Prediction:  6.5 (Medium)                                    โ”‚
โ”‚   Mitigation:       Verify ownership, use UUID                      โ”‚
โ”‚   Mitigation Cost:  8 engineer-hours                                โ”‚
โ”‚                                                                     โ”‚
โ”‚ VULN-003: GraphQL Introspection Exposure                            โ”‚
โ”‚   Component:        /graphql                                        โ”‚
โ”‚   Attack Vector:    Query __schema                                  โ”‚
โ”‚   Discovery:        Day 3 ยฑ 1                                       โ”‚
โ”‚   CVSS Prediction:  4.3 (Medium)                                    โ”‚
โ”‚   Mitigation:       Disable introspection in production             โ”‚
โ”‚   Mitigation Cost:  1 engineer-hour                                 โ”‚
โ”‚                                                                     โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚ PRE-EMPTIVE ACTION PLAN                                             โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                                     โ”‚
โ”‚ BEFORE LAUNCH (Total: 24 engineer-hours)                            โ”‚
โ”‚   โ˜ Implement JWT algorithm pinning              [4h] [Critical]    โ”‚
โ”‚   โ˜ Add ownership verification to all endpoints  [8h] [High]        โ”‚
โ”‚   โ˜ Disable GraphQL introspection               [1h] [Medium]       โ”‚
โ”‚   โ˜ Add anomaly detection on auth endpoints     [6h] [High]         โ”‚
โ”‚   โ˜ Implement proper rate limiting              [5h] [High]         โ”‚
โ”‚                                                                     โ”‚
โ”‚ Investment: 24 hours now                                            โ”‚
โ”‚ Saves: ~340 hours incident response + reputational damage           โ”‚
โ”‚ ROI: 1,316%                                                         โ”‚
โ”‚                                                                     โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

โ—ˆ PROPHECY SUMMARY โ—ˆ
Predicted vulnerabilities: 7
Critical pre-launch fixes: 3
Expected CVEs prevented: 2
Breach probability reduction: 67% โ†’ 12%

"The future is already hereโ€”it's just not evenly distributed."

๐Ÿš€ Installation

git clone https://github.com/bad-antics/precession
cd precession
pip install -e .
precession --awaken

prophecy

"The model is more real than what it models."