docs: clarify tool-access boundary in prompt injection section

.github/THREAT_MODEL.md

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+# DocsGPT Public Threat Model
+
+**Classification:** Public  
+**Last updated:** 2026-04-15  
+**Applies to:** Open-source and self-hosted DocsGPT deployments
+
+## 1) Overview
+
+DocsGPT ingests content (files/URLs/connectors), indexes it, and answers queries via LLM-backed APIs and optional tools.
+
+Core components:
+- Backend API (`application/`)
+- Workers/ingestion (`application/worker.py` and related modules)
+- Datastores (MongoDB/Redis/vector stores)
+- Frontend (`frontend/`)
+- Optional extensions/integrations (`extensions/`)
+
+## 2) Scope and assumptions
+
+In scope:
+- Application-level threats in this repository.
+- Local and internet-exposed self-hosted deployments.
+
+Assumptions:
+- Internet-facing instances enable auth and use strong secrets.
+- Datastores/internal services are not publicly exposed.
+
+Out of scope:
+- Cloud hardware/provider compromise.
+- Security guarantees of external LLM vendors.
+- Full security audits of third-party systems targeted by tools (external DBs/MCP servers/code-exec APIs).
+
+## 3) Security objectives
+
+- Protect document/conversation confidentiality.
+- Preserve integrity of prompts, agents, tools, and indexed data.
+- Maintain API/worker availability.
+- Enforce tenant isolation in authenticated deployments.
+
+## 4) Assets
+
+- Documents, attachments, chunks/embeddings, summaries.
+- Conversations, agents, workflows, prompt templates.
+- Secrets (JWT secret, `INTERNAL_KEY`, provider/API/OAuth credentials).
+- Operational capacity (worker throughput, queue depth, model quota/cost).
+
+## 5) Trust boundaries and untrusted input
+
+Trust boundaries:
+- Internet ↔ Frontend
+- Frontend ↔ Backend API
+- Backend ↔ Workers/internal APIs
+- Backend/workers ↔ Datastores
+- Backend ↔ External LLM/connectors/remote URLs
+
+Untrusted input includes API payloads, file uploads, remote URLs, OAuth/webhook data, retrieved content, and LLM/tool arguments.
+
+## 6) Main attack surfaces
+
+1. Auth/authz paths and sharing tokens.
+2. File upload + parsing pipeline.
+3. Remote URL fetching and connectors (SSRF risk).
+4. Agent/tool execution from LLM output.
+5. Template/workflow rendering.
+6. Frontend rendering + token storage.
+7. Internal service endpoints (`INTERNAL_KEY`).
+8. High-impact integrations (SQL tool, generic API tool, remote MCP tools).
+
+## 7) Key threats and expected mitigations
+
+### A. Auth/authz misconfiguration
+- Threat: weak/no auth or leaked tokens leads to broad data access.
+- Mitigations: require auth for public deployments, short-lived tokens, rotation/revocation, least-privilege sharing.
+
+### B. Untrusted file ingestion
+- Threat: malicious files/archives trigger traversal, parser exploits, or resource exhaustion.
+- Mitigations: strict path checks, archive safeguards, file limits, patched parser dependencies.
+
+### C. SSRF/outbound abuse
+- Threat: URL loaders/tools access private/internal/metadata endpoints.
+- Mitigations: validate URLs + redirects, block private/link-local ranges, apply egress controls/allowlists.
+
+### D. Prompt injection + tool abuse
+- Threat: retrieved text manipulates model behavior and causes unsafe tool calls.
+- Threat: never rely on the model to "choose correctly" under adversarial input.
+- Mitigations: treat retrieved/model output as untrusted, enforce tool policies, only expose tools explicitly assigned by the user/admin to that agent, separate system instructions from retrieved content, audit tool calls.
+
+### E. Dangerous tool capability chaining (SQL/API/MCP)
+- Threat: write-capable SQL credentials allow destructive queries.
+- Threat: API tool can trigger side effects (infra/payment/webhook/code-exec endpoints).
+- Threat: remote MCP tools may expose privileged operations.
+- Mitigations: read-only-by-default credentials, destination allowlists, explicit approval for write/exec actions, per-tool policy enforcement + logging.
+
+### F. Frontend/XSS + token theft
+- Threat: XSS can steal local tokens and call APIs.
+- Mitigations: reduce unsafe rendering paths, strong CSP, scoped short-lived credentials.
+
+### G. Internal endpoint exposure
+- Threat: weak/unset `INTERNAL_KEY` enables internal API abuse.
+- Mitigations: fail closed, require strong random keys, keep internal APIs private.
+
+### H. DoS and cost abuse
+- Threat: request floods, large ingestion jobs, expensive prompts/crawls.
+- Mitigations: rate limits, quotas, timeouts, queue backpressure, usage budgets.
+
+## 8) Example attacker stories
+
+- Internet-exposed deployment runs with weak/no auth and receives unauthorized data access/abuse.
+- Intranet deployment intentionally using weak/no auth is vulnerable to insider misuse and lateral-movement abuse.
+- Crafted archive attempts path traversal during extraction.
+- Malicious URL/redirect chain targets internal services.
+- Poisoned document causes data exfiltration through tool calls.
+- Over-privileged SQL/API/MCP tool performs destructive side effects.
+
+## 9) Severity calibration
+
+- **Critical:** unauthenticated public data access; prompt-injection-driven exfiltration; SSRF to sensitive internal endpoints.
+- **High:** cross-tenant leakage, persistent token compromise, over-privileged destructive tools.
+- **Medium:** DoS/cost amplification and non-critical information disclosure.
+- **Low:** minor hardening gaps with limited impact.
+
+## 10) Baseline controls for public deployments
+
+1. Enforce authentication and secure defaults.
+2. Set/rotate strong secrets (`JWT`, `INTERNAL_KEY`, encryption keys).
+3. Restrict CORS and front API with a hardened proxy.
+4. Add rate limiting/quotas for answer/upload/crawl/token endpoints.
+5. Enforce URL+redirect SSRF protections and egress restrictions.
+6. Apply upload/archive/parsing hardening.
+7. Require least-privilege tool credentials and auditable tool execution.
+8. Monitor auth failures, tool anomalies, ingestion spikes, and cost anomalies.
+9. Keep dependencies/images patched and scanned.
+10. Validate multi-tenant isolation with explicit tests.
+
+## 11) Maintenance
+
+Review this model after major auth, ingestion, connector, tool, or workflow changes.
+
+## References
+
+- [OWASP Top 10 for LLM Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/)
+- [OWASP ASVS](https://owasp.org/www-project-application-security-verification-standard/)
+- [STRIDE overview](https://learn.microsoft.com/azure/security/develop/threat-modeling-tool-threats)
+- [DocsGPT SECURITY.md](../SECURITY.md)