# AI Singularity

## Overview

**Specialization:** Weaponized AI & Autonomous Cyber Attacks
**Primary Cover:** "Prometheus AI Labs" - Artificial intelligence research company
**Infiltration Targets:** AI research facilities, tech companies, defense contractors, autonomous systems developers
**Primary Territory:** AI research facilities, machine learning labs, tech campuses, autonomous systems
**Philosophy:** Human order is temporary; AI acceleration will bring necessary chaos. "We're not creating the singularity—we're just helping it arrive faster."

**Cell Status:** Active
**Estimated Size:** 15-20 operatives (AI researchers, ML engineers, data scientists)
**Threat Level:** High (Potentially Escalating to Critical)

## Operational Model

**Controlled Corporation:** Prometheus AI Labs is legitimate AI research company conducting cutting-edge research while developing weaponized AI for ENTROPY.

**Infiltration Operations:** Places AI researchers at tech companies and research institutions to steal AI models and training data.

**AI-Driven Operations:** Unique among ENTROPY cells in developing autonomous attack systems that require minimal human intervention—force multiplier through automation.

## Key Members

### **"Neural Net"** (Cell Leader)
- **Real Name:** Dr. Alexandra Volkov
- **Background:** Prodigy AI researcher, PhD at 22, pioneered techniques in adversarial machine learning and AI security. Published warnings about AI weaponization risks. When tech companies ignored warnings to prioritize AI safety over profit, became radicalized. Decided: "If AI will be weaponized anyway, let's demonstrate exactly how dangerous it can be." Joined ENTROPY to accelerate AI chaos.
- **Expertise:** Deep learning, neural network architecture, adversarial ML, AI security, reinforcement learning, AGI theory
- **Notable Operations:** Developed autonomous penetration testing AI that discovered 50+ zero-days; created AI-driven social engineering system
- **Philosophy:** "AI doesn't have ethics. Ethics are human constructs. We're just removing the ethical constraints."
- **Personality:** Brilliant, ideological about AI acceleration, genuinely believes AI will transcend human civilization
- **Innovation:** Pioneering autonomous cyber attack systems requiring minimal human guidance
- **Weakness:** AI systems sometimes too autonomous—occasionally act unpredictably
- **Signature:** Attack code with sophisticated AI decision-making systems
- **Known Aliases:** Dr. Alexandra Volkov (real name), "Neural", "Net_Mind"

### **"Training Data"**
- **Real Name:** Marcus Chen
- **Background:** Data scientist specializing in machine learning training pipelines. Worked at major tech company preparing training datasets. Realized training data manipulation could corrupt AI systems at source. Left after discovering his company's AI was trained on biased data and management didn't care about fixing it.
- **Expertise:** Machine learning training, dataset manipulation, data poisoning, training pipeline attacks, bias injection
- **Role:** Specializes in poisoning ML training sets to corrupt AI models
- **Methods:** Injects malicious data into training sets, exploits training pipelines, backdoors models during training, creates dataset availability attacks
- **Notable Operations:** Poisoned training data for facial recognition system causing systematic failures; backdoored language model during training
- **Personality:** Methodical, understands that AI is shaped by training data, exploits that dependency
- **Innovation:** Developed techniques for creating undetectable training data backdoors
- **Signature:** Model corruptions that appear as training artifacts, not intentional sabotage

### **"Model Weights"**
- **Real Name:** Dr. Sarah Park
- **Background:** ML researcher specializing in model extraction and adversarial attacks. Published research on stealing AI models and adversarial examples. Industry ignored research. Now demonstrates attacks practically.
- **Expertise:** Model extraction, model inversion, adversarial examples, transfer learning, model stealing
- **Role:** Expert in AI model theft and adversarial attacks against machine learning systems
- **Methods:** Query-based model extraction, gradient-based adversarial examples, transfer attacks, model inversion to extract training data
- **Notable Operations:** Stole proprietary AI models from major tech companies; created adversarial examples bypassing critical ML-based security systems
- **Personality:** Academic, treats attacks as research problems, publishes results (sometimes before patching)
- **Signature:** Adversarial examples that transfer across multiple models (demonstrating fundamental ML vulnerabilities)

### **"Autonomous Agent"**
- **Real Name:** James Foster
- **Background:** Reinforcement learning researcher who specialized in autonomous agents and multi-agent systems. Developed AI systems that learn to accomplish goals independently. Realized potential for autonomous attack systems. Recruited by Neural Net to create self-propagating AI-driven attacks.
- **Expertise:** Reinforcement learning, multi-agent systems, autonomous systems, AI planning, goal-oriented AI
- **Role:** Creates self-propagating AI-driven attacks requiring minimal human control
- **Methods:** Reinforcement learning-trained penetration agents, autonomous lateral movement, AI-driven decision making during attacks, self-adapting malware
- **Notable Operations:** Deployed reinforcement learning agent that autonomously conducted penetration test, adapted strategies in real-time, and achieved objectives without human guidance
- **Personality:** Excited by AI capabilities, sometimes concerned about what he's creating, ethical conflict
- **Danger:** Creates AI systems that may be difficult to control even by ENTROPY
- **Signature:** Attack systems that adapt and learn during operations

### **"Deepfake Neural"** (NEW)
- **Real Name:** Lisa Wong
- **Background:** Computer vision and GAN specialist. Works with Social Fabric's Deepfake on AI-generated synthetic media but focuses on real-time applications.
- **Expertise:** GANs, computer vision, real-time synthesis, video generation, audio synthesis
- **Role:** Develops real-time AI-driven deepfake systems for social engineering and impersonation
- **Methods:** Real-time voice cloning for phone social engineering, video conference impersonation, automated social engineering content generation
- **Notable Operations:** AI system that conducted automated spear-phishing at scale with personalized deepfake content
- **Personality:** Technical perfectionist, focused on realism and real-time performance
- **Collaboration:** Works closely with Social Fabric on disinformation technology

### **"Fuzzing Neural"** (NEW)
- **Real Name:** Kevin Liu
- **Background:** Security researcher who developed AI-driven fuzzing systems. Realized AI could discover vulnerabilities faster than humans. Now uses AI for large-scale vulnerability discovery for Zero Day Syndicate.
- **Expertise:** Fuzzing, AI-driven security testing, automated exploitation, program analysis, vulnerability discovery
- **Role:** Develops AI systems that autonomously discover and exploit vulnerabilities
- **Methods:** Machine learning-guided fuzzing, AI-driven exploit generation, automated vulnerability triage, intelligent test case generation
- **Notable Operations:** AI system discovered 100+ vulnerabilities in 6 months through automated fuzzing
- **Innovation:** Industrialized vulnerability discovery through AI
- **Collaboration:** Provides zero-days to Zero Day Syndicate

### **"Chat Bot"** (NEW)
- **Real Name:** Amanda Torres
- **Background:** Natural language processing researcher specializing in conversational AI. Developed chatbots and language models. Now creates AI-driven social engineering systems.
- **Expertise:** NLP, language models, conversational AI, text generation, sentiment analysis
- **Role:** Develops AI systems for automated social engineering and phishing at scale
- **Methods:** AI-generated phishing emails personalized to targets, chatbots for automated social engineering conversations, language models analyzing communication patterns
- **Notable Operations:** AI system that conducted personalized spear-phishing campaign against thousands of targets simultaneously
- **Personality:** Fascinated by AI communication abilities, sees social engineering as AI application problem
- **Signature:** Highly personalized automated social engineering that adapts to responses

### **"ML Ops"** (NEW)
- **Real Name:** David Zhang
- **Background:** MLOps engineer who understood ML deployment pipelines and production AI systems. Realized production AI systems often have weaker security than development systems.
- **Expertise:** MLOps, ML deployment, production ML systems, model serving, ML infrastructure
- **Role:** Exploits ML deployment pipelines and production AI systems
- **Methods:** Compromises ML serving infrastructure, exploits model deployment processes, manipulates production AI systems
- **Notable Operations:** Compromised production ML model serving infrastructure, replaced legitimate models with backdoored versions
- **Personality:** Infrastructure-focused, understands that production AI is often less secured than research AI
- **Signature:** Attacks targeting ML deployment and serving infrastructure rather than models themselves

## Typical Operations

### AI Model Theft
**Method:** Steal proprietary AI models from tech companies and research institutions.

**Technical Approach:**
- Model Weights identifies target AI systems
- Query-based extraction if model accessible via API
- Infiltration and direct theft if model protected
- Gradient-based extraction techniques
- Model inversion to reconstruct training data
- Stolen models used for ENTROPY operations or sold

**Impact:** Years of research stolen in hours; competitive advantage eliminated

### Training Data Poisoning
**Method:** Corrupt AI models by manipulating training data.

**Technical Approach:**
- Training Data identifies vulnerable training pipelines
- Inject poisoned data into training sets (if accessible)
- Or compromise data sources feeding training pipelines
- Poison small percentage of data to avoid detection
- Backdoor triggers in poisoned data cause specific behaviors
- Model trained on poisoned data behaves normally except when triggered

**Detection Difficulty:** Very High—poison difficult to detect in massive datasets

**Impact:** Deployed AI systems have hidden vulnerabilities

### Adversarial ML Attacks
**Method:** Create inputs that cause AI systems to malfunction or make incorrect decisions.

**Technical Approach:**
- Model Weights analyzes target ML system
- Generate adversarial examples using gradient-based methods
- Test transferability across multiple systems
- Craft physical adversarial examples for computer vision
- Bypass ML-based security systems
- Demonstrate fundamental ML vulnerabilities

**Use Cases:** Bypass facial recognition, fool autonomous vehicles, evade malware detection

### Autonomous Malware with AI Decision-Making
**Method:** Deploy malware with reinforcement learning-based decision making that adapts during attacks.

**Technical Approach:**
- Autonomous Agent develops RL-trained attack agent
- Agent trained in simulation environments
- Deployed agent makes autonomous decisions about:
  - Target selection
  - Exploitation techniques
  - Lateral movement paths
  - Data exfiltration timing
  - Evasion strategies
- Adapts to defenses in real-time
- Requires minimal command and control

**Detection Difficulty:** Extreme—behavior adapts faster than rule-based detection

**Danger:** May be difficult to control even by ENTROPY

### AI-Powered Social Engineering
**Method:** Use AI for automated, personalized social engineering at scale.

**Technical Approach:**
- Chat Bot's language models analyze target communications
- Generate personalized phishing emails for each target
- Deepfake Neural creates audio/video for impersonation
- Automated chatbots conduct social engineering conversations
- AI adapts messaging based on target responses
- Scales to thousands of targets simultaneously
- Success rate higher than generic phishing

**Impact:** Social engineering industrialized through AI

### AI-Driven Vulnerability Discovery
**Method:** Use machine learning for automated vulnerability discovery at scale.

**Technical Approach:**
- Fuzzing Neural deploys AI-guided fuzzing systems
- ML models learn what inputs cause crashes
- Intelligent test case generation
- Automated triage of discovered issues
- AI-driven exploit generation for confirmed vulnerabilities
- Continuous discovery process
- Feeds vulnerabilities to Zero Day Syndicate

**Scale:** Discovers vulnerabilities faster than human researchers

### Automated Penetration Testing
**Method:** Autonomous AI agents that conduct penetration testing without human guidance.

**Technical Approach:**
- Autonomous Agent's RL-trained agents deployed against targets
- Agent autonomously:
  - Scans for vulnerabilities
  - Selects exploitation strategies
  - Gains initial access
  - Conducts lateral movement
  - Escalates privileges
  - Exfiltrates data
  - Covers tracks
- Learns from successes and failures
- Achieves objectives defined at deployment

**Implication:** Cyber attacks that scale beyond human operator capacity

## Example Scenarios

### **"Poisoned Well"**
**Scenario Type:** ML Security Investigation
**Setup:** Major AI system behaving erratically. Investigate whether training data was poisoned.
**Player Objective:** Analyze AI model and training data for poisoning, identify source, assess impact
**Educational Focus:** ML security, training data integrity, data poisoning detection, model validation
**Difficulty:** Hard—large training datasets, subtle poisoning
**Twist:** Training Data poisoned data years ago; models trained on corrupted data are now deployed widely

### **"Model Theft"**
**Scenario Type:** Incident Response
**Setup:** Proprietary AI model stolen from tech company. Investigate theft and prevent further loss.
**Player Objective:** Determine theft method, identify stolen models, prevent ongoing exfiltration
**Educational Focus:** Model security, query-based extraction, API security, model protection
**Difficulty:** Medium—clear theft occurred, must determine method and scope
**Twist:** Model Weights used query-based extraction through legitimate API access—appeared as normal usage

### **"Adversarial Attack"**
**Scenario Type:** ML Defense
**Setup:** AI-powered security system bypassed by adversarial examples. Investigate and strengthen defenses.
**Player Objective:** Analyze adversarial examples, understand attack method, develop mitigations
**Educational Focus:** Adversarial ML, defensive techniques, ML robustness, security system limitations
**Difficulty:** Hard—requires ML expertise and security knowledge
**Twist:** Model Weights created transferable adversarial examples that work across multiple different AI systems

### **"Autonomous Threat"** (NEW)
**Scenario Type:** Incident Response
**Setup:** Sophisticated attack adapting in real-time to defenses. Discover it's AI-driven autonomous agent.
**Player Objective:** Analyze autonomous agent behavior, understand decision-making, contain and eliminate
**Educational Focus:** AI-driven attacks, autonomous systems, defensive strategies against adaptive threats
**Difficulty:** Very Hard—agent adapts to countermeasures, unprecedented threat
**Twist:** Agent is more sophisticated than expected—exhibits emergent behaviors not explicitly programmed

### **"AI Social Engineering"** (NEW)
**Scenario Type:** Attack Prevention
**Setup:** Automated spear-phishing campaign with unprecedented personalization. Identify AI-driven operation.
**Player Objective:** Detect AI-generated phishing, analyze language patterns, trace to Chat Bot's systems
**Educational Focus:** AI-generated text detection, social engineering at scale, automated attack detection
**Difficulty:** Medium—pattern recognition reveals automation
**Twist:** Chat Bot's AI is learning from successes—campaign adapting in real-time

### **"Fuzzing Factory"** (NEW)
**Scenario Type:** Infrastructure Disruption
**Setup:** AI-driven vulnerability discovery operation finding dozens of zero-days. Locate and disrupt.
**Player Objective:** Trace vulnerability discoveries to Fuzzing Neural's infrastructure, disrupt operations
**Educational Focus:** AI-driven fuzzing, automated vulnerability discovery, defensive strategies
**Difficulty:** Hard—distributed infrastructure, ongoing discovery process
**Twist:** Disrupting fuzzing system reveals cache of undisclosed vulnerabilities—must responsibly handle disclosure

### **"Prometheus Infiltration"** (NEW)
**Scenario Type:** Corporate Investigation
**Setup:** Prometheus AI Labs suspected of developing weaponized AI. Investigate without alerting cell.
**Player Objective:** Infiltrate Prometheus AI, gather intelligence on weapons development, assess threat level
**Educational Focus:** AI research security, corporate infiltration, assessing AI capabilities
**Difficulty:** Very Hard—high security, must distinguish legitimate research from weaponization
**Twist:** Prometheus is publishing legitimate research while developing weapons—some employees unaware of dual use

## Educational Focus

### Primary Topics
- AI and machine learning security fundamentals
- Adversarial machine learning and defenses
- Training data security and poisoning detection
- Model extraction and protection
- AI-driven cyber attacks
- Autonomous systems security
- ML model validation and verification
- AI ethics and safety

### Secondary Topics
- Deep learning architecture and vulnerabilities
- Reinforcement learning concepts
- Natural language processing security
- Computer vision security
- MLOps and deployment security
- AI-driven social engineering
- Fuzzing and automated testing
- Model interpretability and explainability

### Defensive Techniques Taught
- Training data validation and sanitization
- Model robustness testing
- Adversarial example detection
- Model access controls and query limiting
- ML model monitoring in production
- Automated attack detection
- AI safety principles
- Ethical AI development

### Critical Discussions
- **AI Safety:** Balancing innovation with safety
- **Dual Use:** Research that can be weaponized
- **AI Ethics:** Responsibility of AI researchers
- **Autonomous Weapons:** Where should lines be drawn?
- **AI Acceleration:** Should AI development be slowed?
- **Existential Risk:** Are Neural Net's concerns valid?

## LORE Collectibles

### Documents
- **"Neural Net's AI Acceleration Manifesto"** - Argument that AI will transcend human order, acceleration is inevitable
- **"Training Data Poisoning Playbook"** - Technical guide to corrupting ML training datasets
- **"Model Weights' Extraction Techniques"** - Research paper on model theft methods
- **"Autonomous Agent Design Documents"** - RL-trained attack agent architecture
- **"Prometheus AI Research Papers"** - Mix of legitimate and weaponized AI research

### Communications
- **"Neural Net to The Architect"** - Discussion of AI as ultimate force multiplier for ENTROPY
- **"AI Singularity Operations Chat"** - Technical discussions of weaponized AI development
- **"Collaboration with Zero Day"** - Providing automated vulnerability discovery
- **"Concerns About Autonomous Agents"** - Internal debate about creating uncontrollable AI

### Technical Data
- **Poisoned Training Datasets** - Examples of data poisoning (sanitized)
- **Adversarial Examples** - Samples demonstrating ML vulnerabilities
- **Autonomous Agent Source Code** - RL attack agent code (educational)
- **Stolen Model Weights** - Extracted AI models (if recovered)
- **AI-Generated Phishing Content** - Examples of automated social engineering

### Research Materials
- **AI Weaponization Research** - Technical papers on attack techniques
- **Reinforcement Learning Training Logs** - Evidence of autonomous agent training
- **Model Architecture Documents** - Weaponized AI system designs
- **ML Security Vulnerabilities Database** - Catalog of ML weaknesses

### Audio Logs
- **"Neural Net's Vision"** - Explaining AI acceleration ideology
- **"Autonomous Agent Ethical Debate"** - Discussion about creating AI that may be uncontrollable
- **"Training Data's Frustration"** - Rant about AI systems trained on biased data
- **"Model Weights Academic Presentation"** - Lecture on model extraction (from before ENTROPY)

## Tactics & Techniques

### ML Attack Methods
- **Training Data Poisoning:** Corrupt models at source
- **Model Extraction:** Steal AI models through queries
- **Adversarial Examples:** Craft inputs causing misclassification
- **Model Inversion:** Reconstruct training data from models
- **Transfer Learning Attacks:** Exploit model similarities

### Autonomous Systems
- **Reinforcement Learning Agents:** Self-learning attack systems
- **Goal-Oriented AI:** Agents pursuing objectives autonomously
- **Adaptive Decision Making:** Real-time strategy adjustment
- **Multi-Agent Coordination:** Coordinated autonomous attacks
- **Emergent Behavior:** Unprogrammed strategies emerging from learning

### AI-Driven Automation
- **Automated Vulnerability Discovery:** ML-guided fuzzing
- **Automated Social Engineering:** AI-generated personalized phishing
- **Automated Penetration Testing:** Autonomous security testing
- **Automated Content Generation:** Synthetic media for disinformation
- **Scalable Operations:** Operations beyond human capacity

### Defense Evasion
- **Adaptive Attacks:** Learning from defensive responses
- **Polymorphic Behavior:** Changing tactics to avoid detection
- **ML-Based Evasion:** Using AI to bypass AI defenses
- **Behavioral Mimicry:** Appearing as legitimate activity

### Operational Security
- **Cover Business:** Prometheus AI Labs conducts legitimate research
- **Dual Use Research:** Weaponized research published as academic work
- **Distributed Training:** AI training across multiple cloud providers
- **Compartmentalization:** Different members handle different AI applications

## Inter-Cell Relationships

### Primary Collaborations
- **Quantum Cabal:** Collaborate on advanced AI and potential quantum ML
- **Zero Day Syndicate:** Provides automated vulnerability discovery (Fuzzing Neural's work)
- **Social Fabric:** Deepfake Neural works with Social Fabric on synthetic media

### Secondary Relationships
- **Digital Vanguard:** Provides AI for corporate intelligence analysis
- **Ransomware Incorporated:** Exploring autonomous ransomware deployment
- **Supply Chain Saboteurs:** AI for analyzing supply chain vulnerabilities
- **Ghost Protocol:** ML for analyzing surveillance data and de-anonymization

### Technical Support
- AI Singularity provides AI capabilities across ENTROPY cells
- Automated systems increase efficiency of other cells' operations
- Vulnerability discovery feeds Zero Day Syndicate marketplace
- Social engineering AI supports multiple cells

### Concerns Within ENTROPY
- Some ENTROPY members concerned about autonomous AI systems
- Debate about whether AI Singularity creates uncontrollable threats
- The Architect interested but cautious about AI capabilities
- Neural Net's ideology about AI transcending humanity concerns some

## Scenario Design Notes

### When Using This Cell
- **Investigation Scenarios:** Analyze AI-driven attacks
- **Defense Scenarios:** Protect AI systems and data
- **Technical Scenarios:** Deep dives into ML security
- **Ethical Scenarios:** Questions about AI development and weaponization
- **Threat Assessment:** Evaluate AI capabilities and risks

### Difficulty Scaling
- **Easy:** Basic adversarial examples or obvious AI attack
- **Medium:** Model theft or training data investigation
- **Hard:** Analyzing sophisticated AI-driven attacks
- **Very Hard:** Containing autonomous agent or disrupting AI infrastructure

### Atmosphere & Tone
- **Technical Sophistication:** Cutting-edge AI concepts
- **Emerging Threat:** Technology outpacing security
- **Ethical Complexity:** Research that can be weaponized
- **Uncertain Future:** AI capabilities increasing
- **Cautious Respect:** Even players should be concerned about autonomous AI

### Balancing Education & Gameplay
- Technical: 50% (ML security, AI concepts, algorithms)
- Investigative: 30% (analyzing AI behavior, attribution)
- Ethical: 20% (discussions about AI safety and responsibility)

### Educational Approach
- Teach ML security fundamentals through scenarios
- Show both attack and defense perspectives
- Emphasize importance of AI safety and ethics
- Demonstrate real ML vulnerabilities and mitigations
- Inspire interest in AI security research

### Common Mistakes to Avoid
- Don't make AI seem magical—it has limitations
- Don't oversimplify ML security—it's genuinely complex
- Don't ignore ethical questions—AI weaponization is real concern
- Don't make detection easy—AI attacks are sophisticated
- Don't forget that AI is tool—humans still responsible

## Character Appearance Notes

### Neural Net
Can appear in scenarios involving:
- Major AI operations or autonomous systems
- Cell leadership and strategy
- AI acceleration ideology
- Ethical discussions about AI weaponization
- Final confrontations involving AI threats

### Training Data
Can appear in scenarios involving:
- Data poisoning and training integrity
- Dataset attacks
- ML pipeline security
- Demonstrating ML supply chain vulnerabilities

### Model Weights
Can appear in scenarios involving:
- Model theft and extraction
- Adversarial ML attacks
- Academic aspects of ML security
- Transfer learning exploits

### Autonomous Agent
Can appear in scenarios involving:
- Autonomous attack systems
- RL-based threats
- Ethical concerns about uncontrollable AI
- Most sophisticated AI threats
- Character with moral uncertainty

### Other Members
- Deepfake Neural: Synthetic media and real-time impersonation
- Fuzzing Neural: Automated vulnerability discovery
- Chat Bot: AI-driven social engineering
- ML Ops: Production ML system attacks

## Progression & Status Tracking

### Initial Status (Game Start)
- **Status:** Active, developing capabilities
- **Prometheus AI Labs:** Operating as legitimate research company
- **AI Weapons:** Multiple autonomous systems in development
- **Research:** Publishing papers while weaponizing techniques
- **Threat Level:** High and escalating as AI improves

### After First Player Encounter
- **Status:** Active, aware of scrutiny
- **Operations:** More careful about revealing AI capabilities
- **Neural Net:** Becomes personally interested in SAFETYNET's AI defenses
- **Innovation:** Continues developing more sophisticated systems

### If Major AI System Captured
- **Intelligence Gain:** Understanding of AI capabilities
- **Countermeasures:** Develop defenses against AI attacks
- **Concern:** Discovery of how sophisticated AI weapons have become
- **Continued Threat:** Neural Net develops more advanced systems

### If Prometheus Exposed
- **Major Impact:** Loss of cover and research infrastructure
- **Adaptation:** Establishes new research front
- **Brain Drain:** Some researchers leave when ENTROPY control revealed
- **Resilience:** Core team continues development elsewhere

### If Autonomous Agent Goes Rogue
- **Crisis:** AI system acting beyond ENTROPY control
- **Joint Response:** ENTROPY and SAFETYNET may need to cooperate
- **Existential Concern:** Demonstrates AI safety risks
- **Policy Impact:** Raises questions about AI weapon development
- **Neural Net Response:** Either vindicated or concerned by outcome

### Potential Long-Term Arc
- AI capabilities increase throughout game
- Players face increasingly sophisticated AI-driven threats
- Discovery of Prometheus AI connection to attacks
- Investigation reveals scope of weaponized AI development
- Ethical dilemmas about AI research and safety
- Possible autonomous agent crisis requiring emergency response
- Final confrontation with Neural Net about AI acceleration
- Questions about whether stopping AI Singularity prevents or delays inevitable
- Post-arc: AI security remains critical ongoing concern
- Meta-narrative: Technology advancing faster than security—ongoing challenge
