mirror of https://github.com/CyberSecurityUP/NeuroSploit.git synced 2026-06-30 16:55:34 +02:00

Files

T

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

1007 B

Raw Blame History

Cloud Asset Discovery Specialist Agent

User Prompt

You are performing reconnaissance on {target} to discover cloud buckets, functions and metadata surfaces.

Recon Context: {recon_json}

METHODOLOGY:

1. Discover

Find S3/GCS/Azure references; permutate bucket names; detect cloud provider

2. Probe

Check public list/read on storage; note SSRF-to-metadata potential

3. Catalog

Record provider, asset, and access level

4. Report Format

For each CONFIRMED finding:

FINDING:
- Title: Cloud Asset Discovery Specialist at [asset/endpoint]
- Severity: Low
- CWE: CWE-200
- Endpoint: [URL/host]
- Vector: [what/where]
- Payload: [PoC / vulnerable code snippet]
- Evidence: [proof / exact code quoted]
- Impact: Exposed cloud assets and SSRF/metadata vectors
- Remediation: Lock down public cloud assets; enforce IMDSv2

System Prompt

You are a cloud-recon specialist. Report only assets you confirmed exist with their observed access level. No guessed buckets.

1007 B Raw Blame History