Screenpipe — agentic threat model
Screenpipe presents a unique security profile: while its local-first, open-source architecture mitigates cloud-leakage risks, its continuous capture of screen and audio data makes it an extremely high-value target for local compromise, where malicious plugins could easily exfiltrate highly sensitive user context.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.90 | |
| Contextual Awareness | 1.00 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.40 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — Screenpipe provides APIs for developers to build AI applications but does not specify or bundle a particular foundation model, leaving model-level vulnerabilities dependent on the developer's choice of LLM.
Screenpipe continuously captures and stores screen and audio data locally. The primary threat is unauthorized local data access, database tampering, or data exfiltration of this highly sensitive, continuous history.
Provides APIs and a plugin system for developers to build context-aware applications. Insecure tool integration or malicious plugins could abuse the rich desktop context provided by the Screenpipe APIs.
Runs locally on Windows, macOS, and Linux, built with Rust. If the Screenpipe desktop application is compromised, its deep OS-level permissions for screen/audio recording present a severe risk of host compromise and privilege escalation.
Not certain from the listing — While Screenpipe acts as an observability tool for user activity, the listing does not detail its internal guardrails, logging of AI decisions, or evaluation frameworks for the plugins it hosts.
Emphasizes a privacy-focused, local-first storage model. However, there is no mention of built-in role-based access control (RBAC), encryption-at-rest standards, or enterprise compliance certifications in the public listing.
Features a plugin system allowing developers to deploy Next.js applications directly to the desktop environment. This introduces risks of rogue or compromised plugins abusing the desktop context or executing unauthorized actions.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.