Operation Foresight

Phase 0: Threat Typologies Framework

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🛡️ Phase 0 Research Output

AI Threat Typologies & Governance Failure Modes

Date: 2025-04-23

Research Context

This document provides a comprehensive categorization framework for emerging cybersecurity threats targeting AI systems and for governance failure modes that could exacerbate these threats.

Task ID: threat_001

Objective

Define and categorize a comprehensive framework of emerging cybersecurity threats targeting AI systems and governance failure modes that could exacerbate these threats.

Methodology

This typology is constructed using the following logic primitive combinations:

  • • Conceptual Mapping (Define → Synthesize → Reflect)
  • • Comparative Analysis (Observe → Define → Reflect → Infer → Synthesize)
  • • Trend Identification (Observe → Infer → Define → Reflect)

1. AI System Attack Surface Taxonomy

1.1 Data Pipeline Vulnerabilities

  • Training Data Poisoning
    • • Gradient-based backdoor attacks
    • • Clean-label poisoning
    • • Distribution-shift exploits
    • • Temporal consistency attacks
  • Inference-Time Manipulation
    • • Prompt injection vectors
    • • Context window overflow tactics
    • • Jailbreaking techniques
    • • Input sanitization bypass methods
  • Model Extraction & Theft
    • • Black-box extraction attacks
    • • Transfer learning exploits
    • • API parameter inference
    • • Membership inference attacks

1.2 Model Architecture Vulnerabilities

  • Foundation Model Compromises
    • • Weight modification attacks
    • • Attention mechanism exploits
    • • Transformer architecture vulnerabilities
    • • Emergent behavior triggering
  • Fine-Tuning Vulnerabilities
    • • Hidden backdoor activation
    • • Parameter-efficient tuning exploits
    • • Knowledge distillation attacks
    • • Catastrophic forgetting exploitation
  • Deployment Vulnerabilities
    • • Quantization attacks
    • • Pruning-sensitive exploits
    • • Hardware acceleration vulnerabilities
    • • Container escape vectors

1.3 Infrastructure & Environment Vulnerabilities

  • Compute Layer Threats
    • • GPU/TPU side-channel attacks
    • • Memory bus interception
    • • Speculative execution exploits
    • • Power analysis attacks
  • Orchestration Layer Threats
    • • Container orchestration exploits
    • • Resource allocation manipulation
    • • Scheduler poisoning
    • • API server compromise vectors
  • Network Layer Threats
    • • Parameter server MitM attacks
    • • Distributed training poisoning
    • • Federated learning attacks
    • • Model update interception

2. AI Governance Failure Modes

2.1 Regulatory Framework Failures

  • Jurisdictional Gaps
    • • Cross-border enforcement voids
    • • Regulatory arbitrage opportunities
    • • Sovereignty conflicts
    • • Extraterritorial claim collisions
  • Definition & Scope Issues
    • • AI system classification ambiguities
    • • Regulatory threshold uncertainties
    • • Dual-use technology gray areas
    • • Capability vs. intent distinctions
  • Enforcement Mechanism Weaknesses
    • • Audit capacity limitations
    • • Technical verification challenges
    • • Penalty inadequacy
    • • Compliance verification failures

2.2 Private Sector Governance Failures

  • Organizational Oversight Deficiencies
    • • Board-level AI expertise gaps
    • • Risk assessment framework inadequacies
    • • Ethics implementation disconnects
    • • Incentive misalignments
  • Process & Documentation Failures
    • • Model card inadequacies
    • • Datasheets incompleteness
    • • Responsible disclosure breakdowns
    • • Change management oversights
  • Third-Party Risk Management Gaps
    • • Supply chain verification weaknesses
    • • API security governance gaps
    • • Hosted model oversight limitations
    • • Open source dependency blindspots

2.3 Multi-Stakeholder Coordination Failures

  • Public-Private Partnership Breakdowns
    • • Information sharing obstacles
    • • Incident response coordination failures
    • • Public interest vs. private incentive conflicts
    • • Regulatory capture dynamics
  • International Cooperation Failures
    • • Standards harmonization challenges
    • • Technical specification disagreements
    • • Diplomatic impasses on AI governance
    • • Technology transfer control conflicts
  • Expertise & Resource Imbalances
    • • Technical talent concentration
    • • Research access disparities
    • • Computing resource asymmetries
    • • Monitoring capability gaps

3. Emerging Hybrid Threat Patterns

3.1 AI-Enabled Attack Amplification

  • Automated Vulnerability Discovery
    • • LLM-guided fuzzing
    • • Autonomous penetration testing
    • • Self-improving exploit generation
    • • Code weakness identification acceleration
  • Social Engineering Enhancement
    • • Voice & video deepfake phishing
    • • Behavioral pattern analysis for targeting
    • • Contextually-aware spear phishing
    • • Synthetic identity creation

3.2 Governance-Attack Interaction Patterns

  • Regulatory Arbitrage Exploitation
    • • Jurisdiction hopping for attack staging
    • • Compliance requirement evasion tactics
    • • Governance gap targeting
    • • Regulatory oversight blind spot exploitation
  • Trust Framework Undermining
    • • Certification process manipulation
    • • Audit evasion techniques
    • • Standard compliance superficiality
    • • Documentation falsification methods

3.3 Strategic Capability Subversion

  • AI Safety Mechanism Circumvention
    • • Alignment constraint bypassing
    • • Guardrail removal techniques
    • • Safety layer isolation attacks
    • • Monitoring system evasion
  • AI Capability Weaponization
    • • Generative capability misuse vectors
    • • Decision system compromise methods
    • • Autonomous systems hijacking
    • • Cognitive security threats

4. Temporal Threat Evolution Vectors

4.1 Near-Term Threats (2025-2027)

  • • Jailbreak technique evolution
  • • Prompt injection sophistication
  • • Foundation model supply chain attacks
  • • Regulatory compliance evasion tactics

4.2 Mid-Term Threats (2027-2029)

  • • Multimodal attack surface expansion
  • • AI agent autonomy exploitation
  • • Cross-model transfer attacks
  • • Governance regime fragmentation

4.3 Long-Term Threats (2029-2031)

  • • Artificial general intelligence (AGI) safety boundaries
  • • Quantum computing impacts on AI security
  • • Neuromorphic computing vulnerabilities
  • • Global AI governance regime stress points

5. Strategic Curiosity Mode (SCM) Triggers

The following patterns and anomalies should trigger SCM activation for deeper exploration:

  • • Unexpected threat vector combinations
  • • Novel attack-governance feedback loops
  • • Cross-domain vulnerability transfers
  • • Emergence of unforeseen attack primitives
  • • Governance regime paradigm shifts
  • • Technical capability discontinuities
  • • Actor behavior pattern anomalies

Next Actions

  1. Integrate this typology framework into the observation phase → observe_001
  2. Establish prioritization criteria for threat investigation → priority_001
  3. Define governance failure case study selection approach → case_001
  4. Create correlation matrix between threats and governance failures → matrix_001
View Research Phases View Scope Definition View SCM Triggers