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ruvnet/ruflo689 installs

browser-auth-flow

Probe a site's authentication flow for redirect leaks, missing CSRF, weak session cookies, and OAuth misconfiguration; produces an auth findings.md

How do I install this agent skill?

npx skills add https://github.com/ruvnet/ruflo --skill browser-auth-flow
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The skill performs security auditing of authentication flows using interactive browser tools. It exhibits a potential indirect prompt injection vulnerability surface because it processes untrusted web application data, but it includes appropriate security guidelines and boundary recommendations.

  • Socketpass

    No alerts

  • Snykwarn

    Risk: MEDIUM · 1 issue

What does this agent skill do?

Browser Auth Flow

Adversarial probe of a site's authentication. Drives the login flow once, records the trajectory, then runs a configurable set of probes against the captured artifacts and live page. Output is a structured findings.md inside the RVF container.

When to use

  • Pre-deployment audit of a new auth flow.
  • Investigating a suspected token leak or redirect issue.
  • Establishing a baseline for ongoing regression checks.

Steps

  1. Open a recorded session via browser-record.

  2. Drive the auth flow as in browser-login (credentials come from --credentials <handle> referencing browser-cookies if the run is a re-auth probe).

  3. Run probes:

    • csrf: inspect the login POST in the trajectory; verify a same-origin token field is present and non-empty.
    • redirect: watch browser_get-url after each nav for cross-origin redirects with auth state in the URL or fragment. Flag any token-bearing URL that crosses an origin boundary.
    • cookie: walk document.cookie via browser_eval. For each cookie, check Secure, HttpOnly, SameSite, expiry, and entropy of the value. Flag missing flags or short tokens. Pass each through aidefence_scan to flag PII embedded in cookie values.
    • oauth: if the flow involves a third-party provider, capture the authorization request, verify state and nonce are present and high-entropy, verify redirect_uri matches the registered callback domain.
  4. Quarantine any token / credential / PII captured during probing — it stays inside the RVF container's findings, never returns to the model unredacted (aidefence_is_safe gate from browser-extract applies if you read the findings back).

  5. Write findings.md with one section per probe, severity rating per finding, and a verdict (pass / warn / fail).

  6. Index the session in browser-sessions with tag: auth-probe so future audits compare against it.

Caveats

  • This skill probes; it does not exploit. Do not chain follow-up requests using a captured token.
  • Credentials must come from a vaulted handle or interactive entry. Never hardcode them in the field map.
  • Some probes require multiple page loads. Trajectory step count for an auth probe typically lands at 15–40 steps; budget accordingly.
  • The output is structured for human review. Do not auto-act on findings without surfacing them to the user first.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/ruvnet/ruflo/browser-auth-flow">View browser-auth-flow on skillZs</a>