Publication Series: Threat Modeling with OpenClaw

Working Title: "Threat Modeling with OpenClaw (Because, Gawd)" Target Audience: Security engineers, DevOps, AI practitioners Format: Technical blog series (8-10 articles) Status: Planning

Series Overview

This publication series uses the Bilrost as a case study to teach practical threat modeling for AI agent systems. Each article focuses on one aspect of the STRIDE framework, adapted for the unique challenges of autonomous AI.

Why This Series?

  1. AI agents are different - Traditional security models don't capture prompt injection, cost amplification, or capability escalation
  2. Real-world example - Not theoretical; based on actual production security decisions
  3. Actionable - Each article includes concrete fixes, not just theory
  4. Open source - Readers can examine the actual code and mitigations

Article Schedule

gantt
    title Publication Timeline
    dateFormat  YYYY-MM-DD

    section Core Series
    Introduction                          :a0, 2026-02-10, 3d
    S is for Spoofing                    :a1, 2026-02-13, 4d
    T is for Tampering                   :a2, 2026-02-17, 4d
    R is for Repudiation                 :a3, 2026-02-21, 4d
    I is for Information Disclosure      :a4, 2026-02-25, 4d
    D is for Denial of Service           :a5, 2026-03-01, 4d
    E is for Elevation of Privilege      :a6, 2026-03-05, 4d

    section Extended
    Supply Chain Deep Dive               :b1, 2026-03-09, 5d
    Defense in Depth Synthesis           :b2, 2026-03-14, 4d

Article 0: Introduction

Title: "Why Your AI Agent Needs a Threat Model" Word Count: 2,000-2,500

Outline

  1. The Problem with AI Agents
  2. More autonomous than traditional software
  3. Access to sensitive resources (APIs, files, networks)

  4. Novel attack vectors (prompt injection, cost amplification)

  5. What is Threat Modeling?

  6. Structured approach to identifying security risks
  7. Proactive vs reactive security

  8. The STRIDE framework

  9. Why STRIDE for AI?

  10. Comprehensive coverage of threat categories
  11. Extensible for AI-specific concerns

  12. Battle-tested by industry

  13. Introducing the Case Study: Bilrost

  14. What it is (Lima VM, Ansible, UFW)
  15. Architecture overview

  16. Why it exists (the problem it solves)

  17. Series Roadmap

  18. What each article will cover
  19. How to follow along

Key Diagrams

  • Architecture diagram of Bilrost
  • Trust boundary visualization
  • STRIDE wheel with AI extensions

Article 1: S is for Spoofing

Title: "S is for Spoofing: Identity in AI Agent Systems" Word Count: 3,000-3,500 GitHub Issue: #TBD

Outline

  1. What is Spoofing?
  2. Impersonating another user, system, or entity

  3. Authentication vs Authorization (spoofing is about authentication)

  4. Spoofing in Traditional Systems

  5. Session hijacking
  6. Credential theft

  7. IP spoofing

  8. Spoofing in AI Agent Systems

  9. User identity: Who is messaging the bot?
  10. Agent identity: Which agent made this API call?

  11. Request origin: Is this request from a trusted source?

  12. Case Study: Telegram Bot Open Access

  13. The vulnerability: dmPolicy: "open" with allowFrom: ["*"]
  14. Attack scenario: Anyone discovers bot, exhausts credits

  15. The 5 pairing bugs that made authentication fail silently

  16. Mitigations

  17. dmPolicy: "pairing" by default
  18. Explicit allowlist
  19. Self-service pairing with secrets

  20. User ID verification

  21. Implementation

  22. Ansible variable changes
  23. Bootstrap.sh guards

  24. Test cases

  25. Lessons Learned

  26. Default to deny
  27. Silent failures are security bugs
  28. Authentication must be verifiable

Key Diagrams

  • Telegram authentication flow (before/after)
  • Attack tree for identity spoofing
  • Pairing flow with bug locations marked

Article 2: T is for Tampering

Title: "T is for Tampering: Data Integrity in LLM Pipelines" Word Count: 3,500-4,000 GitHub Issue: #TBD

Outline

  1. What is Tampering?
  2. Unauthorized modification of data
  3. In transit vs at rest

  4. The integrity property

  5. Traditional Tampering Attacks

  6. MITM attacks
  7. SQL injection

  8. File modification

  9. Tampering in AI Systems: Prompt Injection

  10. Direct prompt injection (user input)
  11. Indirect prompt injection (via retrieved content)

  12. Why this is the #1 LLM security risk

  13. Case Study: Cadence Pipeline

  14. Journal entry → Insight extraction → Telegram
  15. Injection points: file content, LLM output, delivery

  16. Scenario: Malicious note exfiltrates secrets

  17. Other Tampering Vectors

  18. Configuration file modification
  19. Mount point manipulation

  20. Response modification

  21. Mitigations

  22. Input sanitization (limited effectiveness)
  23. Output validation
  24. Sandboxing (our approach)

  25. Principle of least privilege for agents

  26. Implementation

  27. File integrity monitoring
  28. Config file permissions

  29. Prompt structure for injection resistance

  30. Lessons Learned

  31. Prompt injection may never be "solved"
  32. Defense in depth is essential
  33. Limit blast radius

Key Diagrams

  • Prompt injection attack flow
  • Cadence pipeline with injection points
  • Mitigation layer diagram

Article 3: R is for Repudiation

Title: "R is for Repudiation: Audit Trails for Autonomous Agents" Word Count: 2,500-3,000 GitHub Issue: #TBD

Outline

  1. What is Repudiation?
  2. Denying responsibility for actions
  3. The non-repudiation property

  4. Why audit trails matter

  5. Repudiation in Traditional Systems

  6. Missing logs
  7. Log tampering

  8. Anonymous actions

  9. Repudiation in AI Systems

  10. "The agent did it" defense
  11. Missing attribution in LLM calls

  12. Cost tracking per user

  13. Case Study: Who Sent That Message?

  14. Telegram message → Agent action → API cost
  15. Current logging gaps

  16. Missing cost attribution

  17. Audit Requirements

  18. Who initiated the action?
  19. What action was taken?
  20. When did it happen?
  21. What was the outcome?

  22. How much did it cost?

  23. Mitigations

  24. Structured audit logging
  25. Tamper-evident logs
  26. Cost tracking per user

  27. Request correlation IDs

  28. Implementation: Phase S8 Design

  29. Audit log schema
  30. Systemd journal integration

  31. Cost aggregation

  32. Lessons Learned

  33. Log security-relevant events
  34. Make logs tamper-evident
  35. Attribute costs to users

Key Diagrams

  • Audit event flow
  • Log schema diagram
  • Cost attribution flow

Article 4: I is for Information Disclosure

Title: "I is for Information Disclosure: Secrets Management for AI Agents" Word Count: 3,000-3,500 GitHub Issue: #TBD

Outline

  1. What is Information Disclosure?
  2. Unauthorized access to confidential data
  3. The confidentiality property

  4. Types: secrets, PII, intellectual property

  5. Common Disclosure Vectors

  6. Logs
  7. Error messages
  8. Environment variables

  9. Process lists

  10. AI-Specific Disclosure Risks

  11. Context leakage (LLM training data)
  12. Prompt echoing

  13. Tool output exposure

  14. Case Study: API Key Protection

  15. The journey of a secret: file → mount → env → process
  16. Each stage as a leak opportunity

  17. How we protect at each layer

  18. Mitigation Stack

  19. no_log: true in Ansible
  20. File permissions (mode 0600)
  21. EnvironmentFile= vs Environment=

  22. UFW egress filtering

  23. Implementation Details

  24. Secrets parsing in Ansible
  25. Systemd integration

  26. Log redaction

  27. Lessons Learned

  28. Secrets appear in unexpected places
  29. Defense in depth for secrets
  30. Rotation limits exposure window

Key Diagrams

  • Secret lifecycle diagram
  • Leak point visualization
  • Mitigation layer stack

Article 5: D is for Denial of Service

Title: "D is for Denial of Service: Cost Control for LLM-Powered Systems" Word Count: 3,500-4,000 GitHub Issue: #TBD

Outline

  1. What is Denial of Service?
  2. Making a system unavailable
  3. Resource exhaustion

  4. The availability property

  5. Traditional DoS

  6. Network flooding
  7. CPU exhaustion

  8. Storage exhaustion

  9. DoS in AI Systems: Cost Amplification

  10. Every message costs money
  11. Attackers don't need botnets, just persistence

  12. The economics of LLM abuse

  13. Case Study: Telegram Message Flooding

  14. Open access + no rate limiting = disaster
  15. Attack scenario: 1000 messages × $0.01 = $10/minute

  16. The pairing limit bug (50 requests then... what?)

  17. Attack Vectors

  18. Message flooding
  19. Long prompt attacks
  20. Concurrent request exhaustion

  21. File watcher spam (Cadence)

  22. Mitigation Stack

  23. Per-user rate limiting
  24. Cost budgets
  25. Message length limits

  26. Request deduplication

  27. Implementation

  28. Rate limiter architecture
  29. Budget enforcement

  30. Alerting on abuse

  31. Lessons Learned

  32. Every input is a potential DoS vector
  33. Cost is a security concern
  34. Rate limiting is table stakes

Key Diagrams

  • Cost amplification attack tree
  • Rate limiter architecture
  • Budget enforcement flow

Article 6: E is for Elevation of Privilege

Title: "E is for Elevation: Containing Autonomous AI Agents" Word Count: 3,500-4,000 GitHub Issue: #TBD

Outline

  1. What is Elevation of Privilege?
  2. Gaining unauthorized access
  3. Vertical vs horizontal escalation

  4. The authorization property

  5. Traditional Elevation

  6. Privilege escalation exploits
  7. Container escapes

  8. Sudo abuse

  9. Elevation in AI Systems

  10. Agent capability escalation
  11. Tool access expansion

  12. VM/container escape

  13. Case Study: Supply Chain Escape

  14. Malicious npm package → code execution
  15. Code execution → secrets access

  16. Secrets access → lateral movement

  17. Containment Strategy

  18. VM isolation (Lima)
  19. Network containment (UFW)
  20. Non-root service user

  21. Explicit mount points

  22. Supply Chain Hardening

  23. Lockfile pinning
  24. Checksum verification
  25. Dependency scanning

  26. SBOM generation

  27. Implementation

  28. Lima security configuration
  29. UFW rules deep dive

  30. CI/CD security checks

  31. Lessons Learned

  32. Assume breach, limit blast radius
  33. Supply chain is the biggest gap
  34. Defense in depth is essential

Key Diagrams

  • Containment layer diagram
  • Supply chain attack tree
  • Privilege boundary visualization

Article 7: Supply Chain Deep Dive

Title: "Supply Chain Security for AI Agents: Trust No One" Word Count: 4,000-4,500 GitHub Issue: #TBD

Outline

  1. The Supply Chain Problem
  2. Modern software is built on dependencies
  3. Each dependency is a trust decision

  4. One compromised package affects millions

  5. Recent Supply Chain Attacks

  6. Event-stream (2018)
  7. ua-parser-js (2021)
  8. node-ipc (2022)

  9. Research on 2025-2026 incidents

  10. OpenClaw Dependency Analysis

  11. Bun runtime
  12. npm packages
  13. Ubuntu packages

  14. OpenClaw itself

  15. Attack Scenarios

  16. Typosquatting
  17. Maintainer account compromise
  18. Malicious postinstall scripts

  19. Dependency confusion

  20. Current Mitigations

  21. VM isolation (limits blast radius)
  22. UFW (limits exfiltration)

  23. Lockfiles (pins versions)

  24. Gaps and Solutions

  25. SBOM generation
  26. Dependency scanning in CI
  27. Reproducible builds

  28. Vendoring critical deps

  29. Implementation Roadmap

  30. npm audit integration
  31. Bun checksum verification
  32. Snyk/Dependabot setup

  33. SBOM workflow

  34. Lessons Learned

  35. Trust nothing by default
  36. Pin everything
  37. Monitor for compromises
  38. Have an incident response plan

Key Diagrams

  • Dependency graph
  • Attack scenario flowcharts
  • CI/CD security pipeline

Article 8: Defense in Depth Synthesis

Title: "Defense in Depth for AI Agents: Putting It All Together" Word Count: 3,000-3,500

Outline

  1. Recap: The STRIDE Journey
  2. What we learned in each article
  3. Key threats identified

  4. Mitigations implemented

  5. Defense in Depth Model

  6. Layer 1: Network
  7. Layer 2: Platform
  8. Layer 3: Application
  9. Layer 4: Data

  10. Layer 5: Monitoring

  11. Residual Risks

  12. What we can't fully mitigate
  13. Accepting risk vs transferring risk

  14. Continuous improvement

  15. Security Operations

  16. Monitoring and alerting
  17. Incident response

  18. Post-incident review

  19. Future Considerations

  20. Multi-tenant isolation
  21. Hardware security modules

  22. Confidential computing

  23. Call to Action

  24. Open source the threat model
  25. Community contributions
  26. Security research opportunities

Key Diagrams

  • Complete defense in depth visualization
  • Risk heatmap (before/after)
  • Security roadmap

Cross-Article Elements

Recurring Features

  1. "Try It Yourself" Boxes
  2. Hands-on exercises for each threat category

  3. Use the actual Bilrost code

  4. Mermaid Diagrams

  5. Consistent visual style
  6. Attack trees for each threat

  7. Architecture diagrams

  8. Code Snippets

  9. Real code from the repository
  10. Before/after comparisons

  11. Test cases

  12. Risk Score Updates

  13. Track risk scores across articles
  14. Show reduction from mitigations

Consistent Terminology

Term Definition First Used
Trust Boundary Interface between trust levels Article 0
Attack Tree Hierarchical attack visualization Article 0
Blast Radius Scope of damage Article 1
Cost Amplification Financial DoS Article 5
Capability Escalation Agent gaining tools Article 6

Distribution Strategy

Primary Channels

  1. Dev.to - Technical developer audience
  2. Medium (Towards AI) - AI practitioner audience
  3. Company Blog - SEO and ownership
  4. LinkedIn - Professional network

Secondary Channels

  1. Hacker News - Introduction article
  2. Reddit (r/netsec, r/MachineLearning) - Select articles
  3. Twitter/X Threads - Summaries

Repurposing

  1. Conference Talk - 30-min version of full series
  2. Workshop - Hands-on threat modeling session
  3. Podcast Appearances - Discuss key findings

Success Metrics

Metric Target Measurement
Total Reads 10,000+ Platform analytics
GitHub Stars +100 GitHub
Issues Filed 5+ Community engagement
Citations 3+ Academic/blog references
Backlinks 10+ SEO tools

Editorial Standards

  1. Technical Accuracy
  2. All claims backed by code or documentation

  3. External claims cited with sources

  4. Reproducibility

  5. All examples runnable with public repo

  6. Version-pinned dependencies

  7. Accessibility

  8. Define jargon on first use

  9. Progressive complexity (simple → advanced)

  10. Visual Consistency

  11. Mermaid for all diagrams
  12. Consistent color scheme
  13. Code blocks with syntax highlighting

Timeline Summary

Week Activity
Week 1 Finalize plans, create GitHub issues
Week 2 Article 0 (Introduction)
Week 3 Article 1 (Spoofing) + implement fixes
Week 4 Article 2 (Tampering)
Week 5 Article 3 (Repudiation) + Phase S8
Week 6 Article 4 (Information Disclosure)
Week 7 Article 5 (Denial of Service) + rate limiting
Week 8 Article 6 (Elevation of Privilege)
Week 9 Article 7 (Supply Chain)
Week 10 Article 8 (Synthesis) + conference proposal