Scope and Real Boundaries

The program explicitly lists four in-scope targets: app.web.com (website builder), www.networksolutions.com, www.bluehost.com, and www.hostgator.com, all noted as leveraging the OneWeb platform. Any authenticated subdomain flows reachable after login to these targets are also in scope. Out-of-scope assets include all other *.web.com, *.register.com, *.networksolutions.com, *.bluehost.com, *.hostgator.com subdomains, and app.gator.com. This boundary is critical: I would not test wildcard subdomains even if they share technology stacks, because the brief restricts authorization to the named hosts and their post-auth flows.

The OneWeb platform hint means a single code path could surface across multiple brands. For example, a widget editor in app.web.com might share an API with the storefront builder on bluehost.com. My approach would treat each in-scope hostname as an entry point to map shared endpoints, then trace whether a finding on one brand reproduces on another—but only within the four authorized domains. The brief also excludes XSS on website-builder pages where users control HTML, CSRF on unauthenticated or low-sensitivity forms, open redirects, missing security headers, and Cloudflare-related issues, among others. I would filter my test cases against this exclusion list before investing time.

Rewards follow CVSS bands with P1 at $5,000, P2 $2,000–3,000, P3 $1,000–1,500, P4 $250–600. The program notes that priority may shift based on likelihood or impact, so I would document both exploitability and business impact for each finding. Validation averages 10 days with 75% closure rate, which informs my evidence packaging: clear ordered reproduction steps, minimal viable PoC, and explicit mapping to the allowed target list.

  1. Confirm each test session targets only the four in-scope hostnames and their authenticated subdomain flows.
  2. Maintain a living map of shared OneWeb endpoints discovered across brands to avoid duplicate reports.
  3. Cross-reference every test idea against the 20+ excluded submission types before execution.

Sources: Web.com Bug Bounty — public program brief

Account Setup and Test Environment

Testing is authorized on production sites with a request to minimize scanning during PDT business hours. Researcher-controlled accounts would be created on each in-scope property using an @bugcrowdninja.com email address as permitted. No reimbursement for paid tiers means testing would stay on free or trial plans unless a specific feature requires a paid upgrade to reach the attack surface—in which case the prerequisite would be documented and clarification sought via program support before purchasing.

Every request must include the header X-Request-Purpose: Research; optionally X-Bugcrowd-Ninja: [username]. The proxy (Burp Suite or OWASP ZAP) would be configured to inject these headers globally across all four targets. This is not optional: missing headers risk IP blocking and complicate triage. Separate browser profiles per brand would be set up to isolate cookies, localStorage, and session state, preventing cross-contamination of auth tokens.

Because the program forbids interacting with real customers or real accounts, I would only use accounts I personally registered. If a test requires a second party (e.g., shared editing, invite flows), I would create a second researcher-controlled account. I would verify that password reset, logout, and session invalidation work as expected on each platform before assuming they’re safe surfaces—noting that OAuth token invalidation on logout/password change is explicitly excluded from rewards, but the behavior still matters for session management testing.

  1. Register @bugcrowdninja.com accounts on app.web.com, networksolutions.com, bluehost.com, hostgator.com.
  2. Configure proxy to inject X-Request-Purpose: Research and X-Bugcrowd-Ninja headers on all requests.
  3. Create isolated browser profiles per brand; verify session lifecycle (login, logout, password reset) on each.
  4. Document any paid-tier prerequisites for gated features and pause until Support clarifies authorization.

Sources: Web.com Bug Bounty — public program brief

Questions Worth Investigating

The brief highlights AI features within the website builder and storefront/account-manager APIs as focus areas. My top priority would be authenticated logic flaws in the OneWeb platform: authorization bypasses between user sites, improper access control on API endpoints that manage storefront settings, and business logic errors in AI-assisted content generation (e.g., prompt injection that affects other users’ sites, or excessive resource consumption). These align with P1–P3 CVSS bands and are not excluded.

Second, I would test cross-brand data leakage: since OneWeb is shared, an API response on bluehost.com might inadvertently include data from a networksolutions.com account if the session context isn’t properly scoped. I would enumerate all authenticated API endpoints post-login, compare response shapes across brands, and look for tenant isolation failures. This is non-destructive: read-only API calls with my own data.

Third, I would examine file upload and asset management in the website builder (app.web.com). The program excludes XSS on pages where users control HTML, but a malicious file upload that leads to RCE, stored XSS on a different context, or SSRF via image fetching would be in scope. I would test MIME validation, storage bucket permissions, and transformation pipelines—always with researcher-owned files and no impact on other users.

  1. Map authenticated API surface on each in-scope host; diff endpoints and parameters across brands.
  2. Test tenant isolation: can API calls on brand A reference resources created on brand B?
  3. Probe AI-assisted features for prompt injection, output handling, and cross-user contamination.
  4. Exercise file upload pipelines with researcher-controlled payloads; verify storage isolation.

Sources: OWASP Web Security Testing Guide

Structuring the Review

My review methodology centers on manual, authenticated walkthroughs of each in-scope application. I would start by completing a full user journey on each target: sign up, build a minimal site/storefront, publish, manage settings, invite a collaborator (second researcher account), and tear down. This establishes a baseline of expected behavior and reveals the actual attack surface—far more reliably than automated scanning, which the program discourages during business hours.

For API testing, I would capture all requests during the walkthroughs, then replay and mutate them in a controlled manner: parameter tampering, IDOR attempts (changing site_id, store_id, user_id), HTTP method override, mass assignment on update endpoints. Because the program requires a fully working non-malicious PoC, I would only mutate parameters where I can observe a clear state change in my own account—never probing other users’ data.

The AI feature focus deserves a dedicated sub-methodology. I would test: (1) prompt injection in any free-text input that feeds an LLM (site description, product copy, blog generation), observing whether the model’s output is reflected in a dangerous context (HTML, JS, SQL); (2) whether one user’s prompt influences another’s output via shared context or caching; (3) resource exhaustion via oversized or recursive prompts. All tests use my own accounts and observable outputs. I would stop testing a given vector when the application behaves as expected, I hit an exclusion, or further progress would require destructive actions or real-user interaction.

  1. Complete full user journey on each in-scope target with researcher-controlled accounts.
  2. Capture and mutate authenticated API requests for IDOR, mass assignment, method override.
  3. Systematically test AI prompt inputs for injection, cross-user leakage, and resource abuse.
  4. Document stopping condition for each test vector with observed behavior screenshots/logs.

Sources: OWASP Web Security Testing Guide

Building Reproducible Evidence

The program mandates ordered reproduction steps in the report description. I would structure every potential finding as: (1) target hostname and authenticated user context; (2) prerequisite account state (e.g., ‘site created with AI-generated content’); (3) numbered HTTP requests with headers, parameters, and body; (4) observed response demonstrating the issue; (5) impact explanation tied to CVSS factors (confidentiality, integrity, availability, scope). No exploit payloads—only the minimal requests needed to reproduce.

Evidence artifacts would include: HAR files or Burp Suite project exports filtered to the relevant flow; screenshots of UI state before/after; API response diffs showing unauthorized data access or state change; and a one-page impact narrative. Because the program groups same-endpoint parameter vulnerabilities as one report, I would consolidate all parameter variations on a single endpoint into one evidence package.

I would also record the exact X-Request-Purpose header value sent with each request, confirming compliance. If a finding involves cross-brand behavior (e.g., API on bluehost.com returns networksolutions.com data), I would include requests from both brands showing the shared session token and the divergent responses. This helps triage confirm the OneWeb platform connection. Before submission, I would verify the finding is not in the excluded list and that I have not interacted with any non-researcher account.

  1. Format reproduction as ordered list: target, prerequisites, numbered requests, observed result, impact.
  2. Attach filtered HAR/Burp export, UI screenshots, API response diffs, impact narrative.
  3. Include X-Request-Purpose header proof and cross-brand request pairs where applicable.
  4. Pre-submission check: excluded types, no real-user interaction, single-report grouping per endpoint.

Sources: OWASP Web Security Testing Guide

Turning Observations into Practical Fixes

Remediation proposals would be conditional engineering options, not demands. For an IDOR on a storefront API: Option A—add server-side ownership check comparing session user_id to resource owner_id (assumes centralized authz service); Option B—implement capability tokens per resource (assumes token service exists). Each option includes rollout: feature flag per brand, canary on app.web.com first, then networksolutions/bluehost/hostgator. Rollback: disable flag. Regression: automated authz test suite covering cross-brand resource access.

For AI prompt injection reflected in generated HTML: Option A—strict output encoding context-aware (HTML, JS, attribute) at the rendering layer (assumes templating engine supports it); Option B—content security policy with nonce-based script execution (assumes CSP deployment pipeline). Rollout: staging validation with malicious prompt corpus, then progressive brand rollout. Rollback: revert template/CSP. Regression: add prompt-injection test cases to CI pipeline.

For tenant isolation failure on shared OneWeb API: Option A—enforce tenant_id scope in data access layer (assumes multi-tenant ORM); Option B—add request-scoped middleware validating brand context matches token (assumes middleware framework). Ownership likely sits with the platform team, not brand teams. Rollout: dark-launch validation logging cross-tenant attempts, then enforce. Rollback: log-only mode. Regression: integration tests simulating cross-brand API calls. I would not dictate fix ownership but would note where the OneWeb platform team vs. brand-specific teams likely own the code path.

  1. Propose 2–3 conditional fix options per finding with explicit assumptions.
  2. Define rollout: feature flag, canary brand, progressive expansion across OneWeb brands.
  3. Define rollback: flag disable or log-only mode; regression: automated test cases added to CI.
  4. Note probable ownership: platform team for shared APIs, brand teams for UI layers.

Sources: OWASP Web Security Testing Guide

Verifying Fixes and Checking Regressions

After a fix is deployed, I would verify using the exact reproduction steps from my evidence package—no new test vectors. This means: same account, same headers, same request sequence, observing that the previous impact no longer occurs (e.g., IDOR returns 403/404, prompt injection is encoded, cross-brand API returns empty/isolation error). I would also test adjacent parameters and sibling endpoints to catch incomplete fixes.

Because the program groups same-endpoint parameters as one report, verification must cover all parameter variations originally reported. If mass assignment was found on three fields of a PATCH /api/sites/{id} endpoint, all three would be retested plus any new fields added since. Verification would also confirm the fix doesn’t break legitimate functionality: site editing, storefront updates, AI content generation should still work.

If the fix is behind a feature flag, I would request confirmation of which brand(s) have it enabled. The brief notes scope could change every couple months; I would re-confirm target authorization before any re-test. I would not test on out-of-scope subdomains even if they share the fixed code—verification stays within the four in-scope hosts. Verification evidence: updated HAR/request logs showing blocked exploit attempts, successful legitimate operations, and a note on any residual risk.

  1. Re-run exact reproduction steps; confirm impact eliminated, legitimate flows intact.
  2. Test all parameter variations and sibling endpoints reported in original finding.
  3. Request feature-flag status per brand; re-confirm in-scope targets before re-test.
  4. Document verification evidence with residual-risk notes for triage handoff.

Sources: OWASP Web Security Testing Guide

Clear and Useful Handoff

The handoff package for the Web.com security team would contain: (1) the formatted report with ordered reproduction steps; (2) evidence artifacts (HAR, screenshots, diffs); (3) conditional remediation options with assumptions, rollout/rollback plans, and regression tests; (4) verification results if a fix was deployed during the engagement; (5) a mapping of the finding to the specific in-scope target(s) and OneWeb platform component, plus any cross-brand reproduction notes.

I would also include a ‘testing boundaries’ appendix listing: which targets were tested, which authenticated flows were covered, which excluded types were explicitly avoided, and any areas not tested due to paid-tier gates or scope ambiguity. This transparency helps the team assess coverage and decide whether to expand scope or authorize additional testing.

The goal is a self-contained evidence-to-remediation handoff that lets the Web.com team reproduce, assess, fix, and verify without back-and-forth clarification. Consulting offers, team-join pitches, or assumptions about future engagements would not be included—just the technical package. If the team has questions, they can be reached via the disclosure channels tied to the submission. This approach respects the program’s safe harbor, the CFAA/DMCA authorization, and the explicit rules around researcher identification headers and @bugcrowdninja.com accounts.

  1. Assemble handoff: report, evidence, conditional remediation, verification results, target/platform mapping.
  2. Include testing-boundaries appendix: covered targets, flows, exclusions observed, gaps with reasons.
  3. No consulting pitches—technical package only; questions via disclosure channels tied to the submission.
  4. Confirm compliance: headers, researcher accounts, no real-user interaction, in-scope only.

Sources: OWASP Web Security Testing Guide

Sources & further reading

This post sets out a planned research approach using public program information. Actual findings follow the program’s reporting and disclosure process. Scope should be rechecked before testing.