Last Updated: February 12, 2026 Status: Research Prototype - Exploring Trust-Native Security Patterns Scope: /simulations/, archive/game-prototype/, and web4-standard/implementation/reference/

This document describes exploratory security research across Web4's simulation and coordination components. This is not production-grade infrastructure - it's a research sandbox for evolving security ideas around reputation, economic incentives, and witness-based verification.

Honest Assessment: We have interesting ideas, some working prototypes, and real testing at research scale. We also have significant gaps in formal threat modeling, cryptographic foundations, and adversarial validation. This is early-stage research, not a finished security framework.

Note: The original /game/ prototype has evolved into the 4-life project. Historical code is preserved in archive/game-prototype/. Active security research continues in /simulations/.

Web4's security model implements game-theoretic security where:

• AI agents, MCP servers, and services are entities with reputations
• Trust is earned through observed behavior, not declared
• Economic costs (ATP) make attacks expensive
• Social verification (witnesses) prevents collusion
• Cryptographic signatures prove authenticity

This approach differs from traditional access control (static permissions) by treating security as an emergent property of incentive-aligned interactions.

Traditional security models struggle with AI agents because:

• Agents operate autonomously (can't ask permission each time)
• Capabilities evolve (static roles become obsolete)
• Trust is contextual (trusted locally ≠ trusted globally)
• Attacks are behavioral (prompt injection, goal drift)

Web4's game-based approach is one attempt to address these challenges through reputation and incentives rather than just cryptography.

Over 5 autonomous research sessions, we explored several security patterns. Here's what exists:

What It Is: Cryptographic signatures on reputation propagation messages

Implementation: archive/game-prototype/engine/signed_epidemic_gossip.py (~628 lines)

What Works:

• Ed25519 signatures on all gossip messages
• Source authentication (prove gossip came from claimed society)
• Message integrity (detect tampering)
• ~88k signature verifications/second (minimal overhead)

What's Missing:

• No formal key distribution protocol
• No key revocation mechanism
• No cross-chain signature verification
• Testing is synthetic (simulated societies, not real adversaries)

Test Results (Research Scale):

• ✅ Legitimate gossip propagates (100% coverage in simulation)
• ✅ Forged signatures rejected (0% success in test attacks)
• ✅ Unknown sources rejected (registry prevents Sybil sources)

What It Is: Economic cost to create identities (anti-Sybil)

Implementation: archive/game-prototype/engine/identity_stake_system.py

What Works:

• ATP bonding for LCT creation (1,200-75,000 ATP based on capabilities)
• Dynamic stakes scale with horizon/privilege/modality
• Economic gradient makes large-scale Sybil attacks expensive

What's Missing:

• No formal analysis of optimal stake amounts
• No stake slashing protocol (what happens if Sybil detected?)
• No economic modeling of attacker ROI
• Stake amounts are educated guesses, not empirically validated

Test Results (Research Scale):

• ✅ Stake calculation works (62.5× range from local to global)
• ✅ Cost multiplication demonstrated (1000 global Sybils = 75M ATP)
• ⚠️ Not tested against actual economic attacks

What It Is: Require attestations from multiple independent societies

Implementation: archive/game-prototype/engine/witness_diversity_system.py (~860 lines)

What Works:

• ≥3 society diversity requirement
• Witness accuracy tracking (did attestations hold up?)
• Basic cartel detection (reciprocal witnessing flagged)

What's Missing:

• No systematic collusion detection (graph analysis is naive)
• No formal Sybil-resistance proof
• No witness incentive design (why would honest entities witness?)
• Testing is synthetic (doesn't cover sophisticated cartels)

Test Results (Research Scale):

• ✅ Diversity requirements enforced in simulation
• ✅ Simple reciprocal witnessing detected
• ⚠️ Unknown resilience to sophisticated collusion strategies

What It Is: Verify claimed outcomes through challenges

Implementation: archive/game-prototype/engine/reputation_challenge_protocol.py (~561 lines)

What Works:

• 24-hour response windows
• Progressive penalties for evasion (5% → 50%)
• Strike tracking (WARNING → PERMANENT)

What's Missing:

• No formal definition of "valid response"
• No economic analysis of challenge cost vs benefit
• No dispute resolution mechanism
• Testing is synthetic (simulated challenges, not adversarial)

Test Results (Research Scale):

• ✅ Penalty tiers implemented
• ✅ Strike accumulation works
• ⚠️ Unknown behavior under real disputes

We tested these systems at research scale to understand behavior:

Test Environment:

• 100 societies (simulated)
• 1,000 agent LCTs (simulated)
• 500 witness LCTs (simulated)

What This Tells Us:

• ✅ Systems don't fall over at modest scale
• ✅ Performance is reasonable (<5s gossip propagation, <100ms witness selection)
• ✅ Basic attack scenarios are blocked

What This Doesn't Tell Us:

• ❌ Behavior under real adversaries (all testing is synthetic)
• ❌ Resilience to sophisticated attacks (no red team)
• ❌ Economic viability (no real ATP markets)
• ❌ Cryptographic soundness (no formal verification)

Created comprehensive 928-line attack analysis: web4-standard/implementation/reference/ATTACK_VECTOR_ANALYSIS.md

Attack Categories:

1. Identity Attacks (credential theft, birth certificate forgery)
2. Authorization Attacks (permission escalation, delegation hijacking)
3. Reputation Attacks (self-promotion, reputation washing, collusion)
4. Resource Attacks (exhaustion, hoarding)
5. Economic Attacks (ATP manipulation)
6. Network Attacks (Sybil, eclipse, cartels)

• Systematic thinking: Enumerated threat surface comprehensively
• Mitigation strategies: For each attack, proposed defenses
• Priority roadmap: Categorized by severity and implementation effort

• No formal threat model: What adversaries can do, what assets must be protected, success/failure criteria
• No game-theoretic analysis: Are proposed incentives actually Nash-equilibrium resistant?
• No cryptoeconomic modeling: What's the attacker's ROI? What stake amounts actually deter?
• No adversarial validation: All attacks are described, none are actually executed by adversaries

What Exists:

• Ed25519 signatures on gossip messages
• Basic public key registry

What's Missing:

• No formal key distribution protocol
• No key revocation mechanism
• No hardware binding (TPM/Secure Enclave)
• No multi-signature protocols
• No cross-chain verification

What Exists:

• ATP stake calculations
• Dynamic premium concepts
• Market-based pricing ideas

What's Missing:

• No formal economic modeling
• No empirical validation of stake amounts
• No mechanism design for witness incentives
• No analysis of attacker ROI
• No actual ATP markets

What Exists:

• Nothing

What's Missing:

• No formal proofs of security properties
• No game-theoretic equilibrium analysis
• No model checking
• No formal specification of invariants

What Exists:

• Synthetic attack simulations (we attack ourselves)

What's Missing:

• No red team testing
• No fuzzing
• No chaos engineering
• No Byzantine failure scenarios
• No real adversaries

What Exists:

• Architecture designs
• Prototype implementations
• Basic integration testing

What's Missing:

• No formal protocol specifications
• No interoperability standards
• No reference implementations for other platforms
• No conformance tests

1. Entity-Centric Security Model: Treating AI agents, services, and tools as game participants with reputations is a coherent and modern approach for agentic systems.

2. Incentive-Aware Design: Encoding trust as emergent property of incentive-aligned interactions (not just static ACLs) addresses real challenges in autonomous AI systems.

3. Multi-Layer Defense: Combining cryptographic (signatures), economic (stakes), and social (witnesses) mechanisms shows promise for defense-in-depth.

4. Practical Integration: Early integration with real concerns (MCP attacks, prompt injection, tool misuse) demonstrates relevance beyond pure theory.

1. Prototype Stage: All implementations are exploratory prototypes, not hardened production systems.

2. Limited Formalization: Game mechanics lack formal specifications with provable security properties.

3. Untested Assumptions: Economic parameters (stake amounts, penalty rates) are educated guesses, not validated.

4. Synthetic Validation: All testing is self-inflicted; no real adversaries, no red team, no field deployment.

5. Missing Foundations: Cryptographic protocols, key management, and formal threat models are incomplete.

• Defense-in-depth: Multiple overlapping security layers
• Economic incentives: Align behavior through costs and rewards
• Trust-through-verification: Prove trustworthiness, don't declare it
• Empirical grounding: Use real data where available (200 SAGE task executions)
• Honest limitations: Document what we don't know

Development through autonomous AI research sessions with human oversight:

1. Human defines research direction
2. AI explores, designs, implements, tests
3. AI documents findings, decisions, results
4. Human reviews, provides feedback, approves direction
5. Results integrated into codebase
6. Next session builds on previous work

Sessions #80-85: 5 sessions, ~60 hours autonomous research, ~12,600 lines of exploratory code

In addition to game security research, ~25,000 lines of coordination framework code was added:

Validation Results: 76% prediction accuracy (13 of 17 predictions confirmed)

See STATUS.md for full coordination framework details.

As Research Exploration: ✅ Valuable

• Novel approach to agent security (trust-based, incentive-aware)
• Systematic thinking about threat surface
• Working prototypes demonstrate feasibility
• Honest about limitations

As Production Infrastructure: ❌ Not Ready

• Missing formal threat model
• No cryptographic protocol specifications
• No adversarial validation
• Economic parameters not validated
• No formal security proofs

Web4's security research is:

• Not: "Early proof-of-concept with little security work"
• Not: "Production-ready defense-in-depth infrastructure"
• Actually: "Substantial research prototype exploring trust-native security patterns, with significant work done but also significant gaps remaining"

Realistic Goals:

1. Formal threat model document (what adversaries, what assets, what success means)
2. Promote /game/ concepts to top-level design docs (make implicit model explicit)
3. Document game mechanics as formal security policies
4. Create reproducible test scenarios (not just assertions in code)

Stretch Goals: 5. Red team testing (if we can find adversarial collaborators) 6. Economic modeling of stake amounts (even if just simulation)

If Research Continues: 7. Formal cryptographic protocol specifications 8. Key management and revocation protocols 9. Game-theoretic equilibrium analysis 10. Cross-platform integration testing (real federation, not simulated)

If Research Ends:

• Document what we learned
• Publish research findings
• Archive prototypes with clear status

What's Useful:

• Attack analysis as starting point for threat modeling
• Prototype implementations as reference designs
• Architecture patterns for trust-based security
• Session documentation as research methodology example

What to Add:

• Formal threat models
• Game-theoretic analysis
• Economic modeling
• Adversarial testing

What Works:

• Basic signed gossip (if you trust the key registry)
• Simple witness diversity (if you trust the societies)
• ATP metering (as accounting layer, not security)

What Doesn't:

• Anything in adversarial environments
• Anything requiring cryptographic guarantees
• Anything requiring economic soundness

Don't Rely On:

• Stake amounts (not validated)
• Witness diversity (not Sybil-resistant proof)
• Challenge protocol (no formal dispute resolution)
• Any claim of "production-ready"

Do Examine:

• Attack surface enumeration (may find gaps we missed)
• Architecture patterns (may be applicable elsewhere)
• Research methodology (autonomous AI + human oversight)

• docs/why/HETEROGENEOUS_REVIEW.md - Multi-model review protocol for high-risk actions. Requires verification by independently-trained AI models before execution, providing defense-in-depth through assumption diversity and behavioral independence.

• adversarials/TAXONOMY.md - Attack vector taxonomy including governance interface attacks, trust manipulation, and destruction scenarios.

• docs/history/research/ADVERSARIAL_TESTING_STATUS.md - Current status of adversarial testing and defense maturity assessment.

Security research developed through Sessions #80-85 with autonomous AI exploration and human decision authority.

Research Philosophy: "Here's what we tried. Here's what we learned. Here's what we don't know yet."

Not claiming this is finished. Claiming it's interesting and worth continuing.

Last Updated: February 12, 2026 Next Review: March 2026 (after threat model formalization) Status: Research prototype - substantial progress on coordination, security research ongoing