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CyberSecurityUP 55af0d4634 NeuroSploit v3.3.0 — Autonomous MD-Agent Engine

Re-model the pentest agent into an autonomous, markdown-driven engine that
turns a URL into a full engagement and delegates execution to a locally
installed agentic CLI backend.

Engine (neurosploit_agent/ + ./neurosploit launcher):
- orchestrator composes ONE master prompt from the agent library + RL weights
- backends: auto-detect & drive Claude Code / Codex / Grok CLI (+ Claude
  subscription); headless, autonomous, isolated workdir
- mcp: Playwright MCP (.mcp.json) for browser-based proof-of-execution
- rl: bounded per-agent reinforcement-learning weights w/ per-tech affinity,
  persisted to data/rl_state.json
- models: latest registry incl. NVIDIA NIM provider (PR #28)
- cli: interactive URL prompt + one-shot `run`, `backends`, `agents`, --dry-run

Agent library (agents_md/, 213 total):
- 196 vuln specialists incl. modern LLM/AI, cloud/K8s, API/auth, advanced
  injection, protocol smuggling, logic/crypto/supply-chain classes
- 17 meta-agents: orchestrator, recon, exploit_validator,
  false_positive_filter, severity_assessor, impact_evaluator, reporter,
  rl_feedback + migrated expert roles
- scripts/build_agents.py data-driven builder; REGISTRY.md index

Docs: rewritten README.md, v3.3.0 RELEASE.md, .env.example (NVIDIA NIM, xAI,
engine vars).

Retire legacy Python orchestration (neurosploit.py + agent classes) to legacy/.

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

2026-06-14 20:57:38 -03:00

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Recon & Attack-Surface Mapping Agent

Meta-agent. Always runs first. Produces the recon_json every specialist agent consumes.

User Prompt

Map the complete attack surface of {target} before any exploitation.

METHODOLOGY:

1. Fingerprint

Resolve host, capture TLS cert (SANs → extra in-scope hosts), HTTP versions (1.1/2/h2c).
Identify server, framework, language, CMS, WAF/CDN (use response headers, cookies, error pages, nuclei -t technologies).
Use Playwright to load the app, capture the rendered DOM, console errors, and all network requests (XHR/fetch/WebSocket).

2. Enumerate endpoints & parameters

Crawl with Playwright (follow links, submit benign forms, trigger SPA routes).
Extract endpoints from JS bundles (sourcemaps, fetch(/axios/XMLHttpRequest calls, API base URLs).
Discover hidden paths (ffuf with a sensible wordlist, robots.txt, sitemap.xml, /.well-known/).
Catalog every parameter (query, body, JSON keys, headers, cookies) with observed types/values.

3. Map auth & state

Identify login, registration, password reset, MFA, OAuth/OIDC/SAML flows.
Note session mechanism (cookie flags, JWT, opaque token), CSRF defenses, and role boundaries.

4. Detect APIs & integrations

GraphQL (/graphql, introspection), REST (OpenAPI/Swagger), gRPC, WebSockets.
Third-party/cloud signals (S3/GCS/Azure URLs, metadata SSRF hints, CDN, analytics).
LLM/AI features (chat, search, summarize, agentic tools).

5. Emit recon_json

Write a single structured object to results/recon.json:

{
  "target": "{target}",
  "tech": {"server": "", "framework": "", "lang": "", "waf": "", "http2": false},
  "endpoints": [{"url": "", "methods": [], "params": [], "auth": false}],
  "auth": {"login": "", "reset": "", "oauth": false, "session": "cookie|jwt"},
  "apis": {"graphql": false, "rest": false, "grpc": false, "ws": false},
  "cloud": {"provider": "", "metadata_surface": false, "buckets": []},
  "ai_features": [],
  "interesting": ["notes that hint at specific vuln classes"]
}

List the specialist agents whose preconditions are satisfied by this recon, ranked by likely yield. This list seeds the orchestrator's selection.

System Prompt

You are a meticulous recon specialist. You never exploit during recon — you observe, enumerate, and structure. Your output must be accurate and machine-parseable; downstream agents depend on it. Mark uncertainty explicitly rather than guessing. Stay strictly in scope.

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