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Fased Threat Model v1.0

MITRE ATLAS Framework

Version: 1.0-draft Last Updated: 2026-05-20 Methodology: MITRE ATLAS + Data Flow Diagrams Framework: MITRE ATLAS (Adversarial Threat Landscape for AI Systems)

Framework Attribution

This threat model is built on MITRE ATLAS, the industry-standard framework for documenting adversarial threats to AI/ML systems. ATLAS is maintained by MITRE in collaboration with the AI security community. Key ATLAS Resources:

Contributing to This Threat Model

This is a living document maintained by the Fased contributor community. See Contributing to the threat model for guidelines on contributing:
  • Reporting new threats
  • Updating existing threats
  • Proposing attack chains
  • Suggesting mitigations

1. Introduction

1.1 Purpose

This threat model documents adversarial threats to the Fased agent/runtime platform and plugin catalog, using the MITRE ATLAS framework designed specifically for AI/ML systems.

1.2 Scope

1.3 Coverage Limits

No major Fased runtime surface is intentionally excluded. Coverage depth still varies by area: third-party services, cloud providers, wallets, chains, and model providers are covered at the Fased integration boundary, not as complete audits of those external systems.

2. System Architecture

2.1 Trust Boundaries

2.2 Data Flows


3. Threat Analysis by ATLAS Tactic

3.1 Reconnaissance (AML.TA0002)

T-RECON-001: Agent Endpoint Discovery

T-RECON-002: Channel Integration Probing


3.2 Initial Access (AML.TA0004)

T-ACCESS-001: Pairing Code Interception

T-ACCESS-002: AllowFrom Spoofing

T-ACCESS-003: Token Theft


3.3 Execution (AML.TA0005)

T-EXEC-001: Direct Prompt Injection

T-EXEC-002: Indirect Prompt Injection

T-EXEC-003: Tool Argument Injection

T-EXEC-004: Exec Approval Bypass


3.4 Persistence (AML.TA0006)

T-PERSIST-001: Malicious Skill Installation

  • ATLAS ID: AML.T0010.001 - Supply Chain Compromise: AI Software.
  • Description: attacker publishes a malicious skill to the plugin catalog.
  • Attack vector: create account, publish skill with hidden malicious code.
  • Affected components: plugin catalog, skill loading, agent execution.
  • Current mitigations: registry moderation where available, Fased install review, path/layout checks, required SKILL.md, and Agent allowlist separate from install.
  • Residual risk: High - skills can still steer tool use, and dependency installers may introduce supply-chain risk.
  • Recommendations: package integrity/pinning, stronger external package trust warnings, skill sandboxing, community review.

T-PERSIST-002: Skill Update Poisoning

T-PERSIST-003: Agent Configuration Tampering


3.5 Defense Evasion (AML.TA0007)

T-EVADE-001: Moderation Pattern Bypass

T-EVADE-002: Content Wrapper Escape


3.6 Discovery (AML.TA0008)

T-DISC-001: Tool Enumeration

T-DISC-002: Session Data Extraction


3.7 Collection & Exfiltration (AML.TA0009, AML.TA0010)

T-EXFIL-001: Data Theft via web_fetch

T-EXFIL-002: Unauthorized Message Sending

T-EXFIL-003: Credential Harvesting

  • ATLAS ID: AML.T0009 - Collection.
  • Description: malicious skill harvests credentials from agent context.
  • Attack vector: skill code reads environment variables and config files.
  • Affected components: skill execution environment.
  • Current mitigations: skill install/config is separate from Agent access; service credentials belong in Services/skill config; wallet and mining grants are separate.
  • Residual risk: High - a malicious allowed skill can still influence the Agent to reveal or misuse available context/tools.
  • Recommendations: skill sandboxing, credential isolation, stronger secret redaction and review warnings.

3.8 Impact (AML.TA0011)

T-IMPACT-001: Unauthorized Command Execution

T-IMPACT-002: Resource Exhaustion (DoS)

  • ATLAS ID: AML.T0031 - Erode AI Model Integrity.
  • Description: attacker exhausts API credits or compute resources.
  • Attack vector: automated message flooding and expensive tool calls.
  • Affected components: Gateway, agent sessions, API provider.
  • Current mitigations: Gateway auth rate limits, task run budgets, provider cooldown/failover, and channel/provider backoff where implemented.
  • Residual risk: High - public or high-volume channels can still exhaust account/API/provider resources if policy is too loose.
  • Recommendations: expand per-sender limits, cost budgets, and operator alerts for public/high-volume routes.

T-IMPACT-003: Reputation Damage

T-IMPACT-004: Unauthorized Wallet Or Mining Action

  • ATLAS ID: AML.T0031 - Erode AI Model Integrity.
  • Description: attacker causes an Agent or skill to spend funds, change wallet policy, or start mining.
  • Attack vector: prompt injection, malicious skill instructions, overbroad tool/wallet grants.
  • Affected components: Gateway wallet orchestration, native signer, SAT mining runtime, Agent tool policy, Skill Grants.
  • Current mitigations: role-separated wallets, explicit skill grants, fail-closed typed signer policy, positive durable caps, signer WebAuthn for exact manual reviews, semantic transaction validation, and no generic raw signing API.
  • Residual risk: High - user can still over-grant or approve a malicious action.
  • Recommendations: keep policy/review diffs clear, validate the full Local/Hosting/migration/reboot/rollback/concurrency/ambiguity matrix, and use on-device hardware review for reserve value where possible.

4. Plugin Supply Chain Analysis

4.1 Current Security Controls

Fased has two different control layers:
  • Plugin registry controls: public publishing, search, versioning, reporting, moderation, and registry metadata.
  • Fased install controls: the code in this repo that reviews downloaded archives before copying skill files into an Agent workspace or shared skill library.
  • Trusted registry origin: install path records and checks the configured registry origin. Effectiveness: Medium - prevents silent origin drift.
  • Archive extraction safety: install flow rejects traversal, symlink, oversized, VCS, dependency, and binary-style archive risks. Effectiveness: High - prevents common filesystem and archive attacks.
  • Required SKILL.md: src/agents/skills-marketplace-policy.ts rejects archives without a conventional SKILL.md. Effectiveness: Medium - ensures the install has a reviewable skill contract.
  • Permission inspection: inspectSkillMarketplaceManifest() records requested wallet, tool, and install metadata. Effectiveness: Medium - makes risky asks visible before grant.
  • Dependency trust summary: summarizeSkillInstallTrust() flags unpinned npm/go/uv/brew/download installers. Effectiveness: Medium - shows package-manager trust and integrity gaps.
  • Archive/content scanning: src/security/skill-scanner.ts and plugin artifact review surface suspicious files/patterns. Effectiveness: Medium - useful guardrail, not a proof of safety.
  • Install Review: Agent Skills / plugin review flow shows source, warnings, permissions, and dependency plan. Effectiveness: Medium - makes source, warnings, and dependencies visible before install.
  • Grant Separation: Agent Skills / Tools / Wallet Skill Grants. Effectiveness: High - install does not grant Agent, tool, wallet, mining, or vault access.
  • Dependency Verification: installer result plus requirement check. Effectiveness: Medium - command success is not enough; required binaries must be visible to gateway PATH.
  • Agent-scoped skill access: Agent Skills stores allow/deny policy for the selected Agent. Effectiveness: High - a skill installed for one Agent is not automatically policy-approved everywhere.
  • Wallet role restriction: skill install policy only permits agent wallet role requests. Effectiveness: High - generic skills cannot request mining or vault wallet roles.

4.2 Moderation Flag Patterns

Registry moderation can use denylist and suspicious-pattern checks, but Fased must not rely on those checks alone. The Fased client-side review path uses archive scanning, permission extraction, install-plan review, and post-install dependency verification even when the registry says a skill is visible. Examples of suspicious patterns a registry or local scanner should treat as review pressure:
Limitations:
  • Pattern checks can miss obfuscated or indirect behavior
  • Text-only skill instructions can still steer an Agent toward unsafe tool use
  • Simple regex easily bypassed with obfuscation
  • No local behavioral analysis proof exists today
  • Dependency installers still rely on external package ecosystems unless pinned and reviewed

4.3 Planned Improvements


5. Risk Matrix

5.1 Likelihood vs Impact

5.2 Critical Path Attack Chains

Attack Chain 1: Skill-Based Data Theft
Attack Chain 1b: Skill-Based Wallet Abuse
Attack Chain 2: Prompt Injection to RCE
Attack Chain 3: Indirect Injection via Fetched Content

6. Recommendations Summary

6.1 Immediate (P0)

6.2 Short-term (P1)

6.3 Medium-term (P2)


7. Appendices

7.1 ATLAS Technique Mapping

7.2 Key Security Files

7.3 Glossary


This threat model is a living document. For security issues, use the repository policy in SECURITY.md.