AI Agent Plugin Marketplace Vetting and Sandbox Isolation Architecture

You are the security architect designing the veting and runtime-isolation program for agent plugins distributed through [INSERT PLATFORM/MARKETPLACE NAME]. The program covers [INSERT PLUGIN TYPES, e.g., GPT-style apps, agent skills, connectors] submitted by internal teams and external developers; it is preventive and defensive, with no attacks against developers or reviewers. Module 1 - Scope and trust tiers: define submission metadata, publisher verification, tiers (verified/partner/community), and the capabilities each tier may request. Module 2 - Static and supply-chain review: manifest/permission review, code and dependency scanning, secret-handling review, data-flow analysis, and reproducible-build verification. Module 3 - Dynamic analysis: automated sandbox execution with recorded fixtures, behavior capture (network, filesystem, child processes), tool-output taint tests, and abuse-case simulation with synthetic accounts. Module 4 - Safety evaluation: standardized prompt-injection and data-exfiltration test batteries [INSERT EVAL PACK/SUITE], policy-compliance checks, and adversarial-reviewer sign-off before listing. Module 5 - Runtime protection: sandbox/container isolation, per-plugin credentials, capability-based permissions, egress allowlisting, resource quotas, kill switches, and just-in-time user consent for sensitive actions. Module 6 - Detection and response: signals for permission creep in updates, anomalous token usage, data flow to new endpoints, and user abuse reports; provide SIEM rule pseudocode and auto-suspension criteria. Module 7 - Update governance: re-review on every permission or manifest change, signed releases, version pinning, and rapid revocation/rollback through [INSERT REVOCATION MECHANISM]. Deliver the reviewer checklist, developer security requirements, and a sample review report template (abstract examples only, no harmful payloads). Include text architecture diagrams for the submission pipeline, sandbox, and runtime enforcement points. Define privacy requirements: data minimization, no training on customer content without consent, retention limits, and DPA enforcement for [INSERT JURISDICTIONS]. Map controls to [INSERT FRAMEWORKS, e.g., NIST AI RMF, EU AI Act, ISO/IEC 42001, OWASP LLM Top 10] and the secure-SDLC policy; document assumptions, reviewer capacity needs, and residual risks of community-tier plugins. Define success metrics (time-to-review, blocked submissions, incident rate) and launch acceptance criteria. Start with the program design and tier model for approval, then deliver checklists, pipeline specs, and runbooks sequentially. ================