PeerAI Trust Center
Product

AI Security

AI agents work in your boundary, not ours.

PeerAI Studio is scaffolding for AI-native delivery — the AI runtime, the data, and the model provider are all selected and controlled by the customer. PeerAI never operates a data plane that holds your code, prompts, or model outputs. Input is yours. Output is yours.

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Commitments

  • Customer-controlled LLM

    BYO API key for OpenAI / Anthropic / Google / Azure / AWS Bedrock / Cohere / OpenRouter / HuggingFace. Studio relays requests; PeerAI does not proxy or cache them.

  • Customer-controlled database

    Studio connects to your DBs using credentials you provide. No PeerAI-side replica.

  • Local agent execution

    Agents run inside the Tauri sidecar on the customer endpoint. No PeerAI-hosted agent runtime.

  • Structured tool guardrails

    Tools are registered with strict input/output schemas. Outputs are validated before being returned to the agent loop.

  • Sandboxed code execution

    Code-execution paths run in subprocess isolation with timeouts and resource limits.

  • Prompt-injection mitigations

    System-prompt isolation, tool-use allowlists per agent, output validation, retrieval-source attribution.

  • Secrets stay local

    API keys land in macOS Keychain (default) or an encrypted file at ~/.peerai/credentials.json. Never sent to PeerAI telemetry.

Architecture trust boundary

┌──────────────────────── customer endpoint ─────────────────────────┐ │ │ │ PeerAI Studio ──▶ Customer-selected LLM (BYO API key) │ │ │ │ │ ├──▶ Customer-selected database (BYO credentials) │ │ │ │ │ └──▶ Customer files / repos (local FS, scoped) │ │ │ │ Optional outbound: Sentry (crashes) · Arize (eval traces) │ │ — both customer-toggleable — │ │ │ └────────────────────────────────────────────────────────────────────┘ │ │ no PeerAI-side data plane ▼ (nothing)

The dashed line marks the trust boundary. Code, prompts, model outputs, and database contents do not cross it unless the customer explicitly enables an optional outbound (Sentry crashes or Arize evaluation traces).

Threat model — what we worry about

  • Prompt injection from retrieved content

    Mitigation: system-prompt isolation, tool-use allowlists per agent, retrieved content is tagged as untrusted and not concatenated with system instructions.

  • Tool misuse by the model

    Mitigation: every tool call is schema-validated, scoped (e.g., file system access restricted to user-selected paths), and the agent loop logs each invocation locally.

  • Code-execution escape

    Mitigation: subprocess isolation, time limits, no privileged execution; user remains the OS principal.

  • Credential exfiltration via agent

    Mitigation: secrets are read by the sidecar from Keychain/credentials file at request time and never placed in agent context windows.

  • Supply-chain compromise of dependencies

    Mitigation: daily grype + pip-audit + cargo-audit + bun pm audit; release security gate; CycloneDX SBOM per release.

    SBOM

Evidence

  • Daily vulnerability scans

    grype + pip-audit + cargo-audit + bun pm audit, daily 09:00 UTC, plus continuous Aikido on every lockfile push.

    View rollup
  • Per-release CycloneDX SBOM

    2,672 components tracked across npm/pypi/cargo with NTIA enrichment and license compliance tracking.

    View SBOMs
  • Responsible disclosure

    48h ack SLA, 7-day assessment, 30-day critical fix target.

    Report an issue