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100 lines
4.7 KiB
Markdown
100 lines
4.7 KiB
Markdown
# DocsGPT Incident Response Plan (IRP)
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This playbook describes how maintainers respond to confirmed or suspected security incidents.
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- Vulnerability reporting: [`SECURITY.md`](../SECURITY.md)
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- Non-security bugs/features: [`CONTRIBUTING.md`](../CONTRIBUTING.md)
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## Severity
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| Severity | Definition | Typical examples |
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| **Critical** | Active exploitation, supply-chain compromise, or confirmed data breach requiring immediate user action. | Compromised release artifact/image; remote execution. |
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| **High** | Serious undisclosed vulnerability with no practical workaround, or CVSS >= 7.0. | key leakage; prompt injection enabling cross-tenant access. |
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| **Medium** | Material impact but constrained by preconditions/scope, or a practical workaround exists. | Auth-required exploit; dependency CVE with limited reachability. |
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| **Low** | Defense-in-depth or narrow availability impact with no confirmed data exposure. | Missing rate limiting; hardening gap without exploit evidence. |
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## Response workflow
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### 1) Triage (target: initial response within 48 hours)
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1. Acknowledge report.
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2. Validate on latest release and `main`.
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3. Confirm in-scope security issue vs. hardening item (per `SECURITY.md`).
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4. Assign severity and open a **draft GitHub Security Advisory (GHSA)** (no public issue).
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5. Determine whether root cause is DocsGPT code or upstream dependency/provider.
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### 2) Investigation
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1. Identify affected components, versions, and deployment scope (self-hosted, cloud, or both).
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2. For AI issues, explicitly evaluate prompt injection, document isolation, and output leakage.
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3. Request a CVE through GHSA for **Medium+** issues.
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### 3) Containment, fix, and disclosure
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1. Implement and test fix in private security workflow (GHSA private fork/branch).
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2. Merge fix to `main`, cut patched release, and verify published artifacts/images.
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3. Patch managed cloud deployment (`app.docsgpt.cloud`) and other deployments as soon as validated.
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4. Publish GHSA with CVE (if assigned), affected/fixed versions, CVSS, mitigations, and upgrade guidance.
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5. **Critical/High:** coordinate disclosure timing with reporter (goal: <= 90 days) and publish a notice.
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6. **Medium/Low:** include in next scheduled release unless risk requires immediate out-of-band patching.
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### 4) Post-incident
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1. Monitor support channels (GitHub/Discord) for regressions or exploitation reports.
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2. Run a short retrospective (root cause, detection, response gaps, prevention work).
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3. Track follow-up hardening actions with owners/dates.
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4. Update this IRP and related runbooks as needed.
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## Scenario playbooks
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### Supply-chain compromise
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1. Freeze releases and investigate blast radius.
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2. Rotate credentials in order: Docker Hub -> GitHub tokens -> LLM provider keys -> DB credentials -> `JWT_SECRET_KEY` -> `ENCRYPTION_SECRET_KEY` -> `INTERNAL_KEY`.
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3. Replace compromised artifacts/tags with clean releases and revoke/remove bad tags where possible.
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4. Publish advisory with exact affected versions and required user actions.
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### Data exposure
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1. Determine scope (users, documents, keys, logs, time window).
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2. Disable affected path or hotfix immediately for managed cloud.
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3. Notify affected users with concrete remediation steps (for example, rotate keys).
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4. Continue through standard fix/disclosure workflow.
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### Critical regression with security impact
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1. Identify introducing change (`git bisect` if needed).
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2. Publish workaround within 24 hours (for example, pin to known-good version).
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3. Ship patch release with regression test and close incident with public summary.
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## AI-specific guidance
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Treat confirmed AI-specific abuse as security incidents:
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- Prompt injection causing sensitive data exfiltration (from tools that don't belong to the agent) -> **High**
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- Cross-tenant retrieval/isolation failure -> **High**
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- API key disclosure in output -> **High**
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## Secret rotation quick reference
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| Secret | Standard rotation action |
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| Docker Hub credentials | Revoke/replace in Docker Hub; update CI/CD secrets |
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| GitHub tokens/PATs | Revoke/replace in GitHub; update automation secrets |
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| LLM provider API keys | Rotate in provider console; update runtime/deploy secrets |
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| Database credentials | Rotate in DB platform; redeploy with new secrets |
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| `JWT_SECRET_KEY` | Rotate and redeploy (invalidates all active user sessions/tokens) |
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| `ENCRYPTION_SECRET_KEY` | Rotate and redeploy (re-encrypt stored data if possible; existing encrypted data may become inaccessible) |
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| `INTERNAL_KEY` | Rotate and redeploy (invalidates worker-to-backend authentication) |
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## Maintenance
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Review this document:
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- after every **Critical/High** incident, and
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- at least annually.
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Changes should be proposed via pull request to `main`.
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