Browser Auth FlowCAUTION
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Overview
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
ef7d4f0535e5OBSERVED · 2026-09-25What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: browser-auth-flow description: Probe a site's authentication flow for redirect leaks, missing CSRF, weak session cookies, and OAuth misconfiguration; produces an auth findings.md argument-hint: "<login-url> [--credentials <handle>] [--probes csrf,redirect,cookie,oauth]" allowed-tools: mcp__plugin_ruflo-core_ruflo__browser_open mcp__plugin_ruflo-core_ruflo__browser_close mcp__plugin_ruflo-core_ruflo__browser_fill mcp__plugin_ruflo-core_ruflo__browser_type mcp__plugin_ruflo-core_ruflo__browser_click mcp__plugin_ruflo-core_ruflo__browser_wait mcp__plugin_ruflo-core_ruflo__browser_eval mcp__plugin_ruflo-core_ruflo__browser_snapshot mcp__plugin_ruflo-core_ruflo__browser_get-url mcp__plugin_ruflo-core_ruflo__aidefence_has_pii mcp__plugin_ruflo-core_ruflo__aidefence_scan Bash Read Write --- # 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.
Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (4)
crates
plugin/agents
plugin/commands
plugin/skills
Gates applied: no_behavioural_pass.
ef7d4f0535e5full audit observations/trust-audit/skill/ruvnet__browser-auth-flow.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-09-25 | ef7d4f0535e5 | CAUTION | B | 89 | first audit |
Questions
What does the Browser Auth Flow skill do?
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Is Browser Auth Flow safe to install?
With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can Browser Auth Flow access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (ef7d4f0535e5), read on 2026-09-25. The repository is watched, and a new audit runs when it changes — this is the first audit.