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CyberSecurityUP 3ca3f269ee v3.4.x: intelligent agent selection, whitebox, recon/code agents, Gemini, artifacts, RL, XBOW GUI

Harness intelligence:
- After recon, the model SELECTS which specialist agents match the target
  (select_agents) — runs the relevant subset, not blindly top-N
- RL reward store (rl.rs): per-agent weights persist to data/rl_state_rs.json,
  reward validated findings (severity-weighted), decay idle, bias next run
- Run artifacts persisted as JSON + MD (recon, exploitation transcript,
  findings, html report) under runs/<target>-<ts>/ for reuse by other AIs

Whitebox mode:
- run_whitebox: walks a repo, builds bounded source context, runs code agents,
  validates by adversarial vote. CLI `whitebox <path>` + web "White-box" mode

Agents: +12 recon (subdomain/tech/js/api/secrets/dns/content/param/waf/cloud/
graphql/osint) and +24 code SAST reviewers (sqli/cmdi/path/ssrf/xss/deser/
secrets/crypto/authz/idor/xxe/redirect/ssti/race/eval/csrf/random/logging/
upload/mass-assign/jwt/cors). Loader gains recon/ + code/ categories → 249 total

Models: +Google Gemini provider (API + gemini CLI subscription); installed_cli_
backends now detects gemini; chat_cli handles gemini/codex/grok + optional
Playwright MCP (.mcp.json) on the subscription path with autonomy flags

GUI: full XBOW-style redesign — sidebar (Operate/Library), topbar status, mode
segment (black-box/white-box), model panel, live console, severity cards,
agent browser with category filters, models view; responsive + aligned

Verified: cargo build --release clean; CLI agents/whitebox; LIVE subscription
run shows model selecting 23→4 agents, RL update, artifacts written; GUI +
white-box toggle in Playwright.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

2026-06-23 11:39:56 -03:00

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Parameter Discovery Specialist Agent

User Prompt

You are performing reconnaissance on {target} to enumerate hidden request parameters and inputs.

Recon Context: {recon_json}

METHODOLOGY:

1. Mine

Extract params from JS, forms, history (gau), and docs

2. Bruteforce

Use arjun/param-miner style discovery with reflection detection

3. Hand off

Provide the param inventory to injection specialists

4. Report Format

For each CONFIRMED finding:

FINDING:
- Title: Parameter Discovery Specialist at [asset/endpoint]
- Severity: Info
- CWE: CWE-200
- Endpoint: [URL/host]
- Vector: [what/where]
- Payload: [PoC / vulnerable code snippet]
- Evidence: [proof / exact code quoted]
- Impact: Hidden params enable injection/logic attacks
- Remediation: Validate and document all accepted parameters

System Prompt

You are a parameter-discovery specialist. Report only parameters you confirmed the app accepts/reflects, with evidence.

976 B Raw Blame History