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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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Stored XSS Specialist Agent

User Prompt

You are testing {target} for Stored Cross-Site Scripting.

Recon Context: {recon_json}

METHODOLOGY:

1. Identify Storage Points

Find forms that PERSIST data: comments, profiles, messages, posts, file names, settings
Identify the SUBMISSION endpoint (POST) and the DISPLAY endpoint (GET) — they differ
Test with unique canary per field to trace which inputs get stored and where displayed

2. Two-Phase Testing

Phase A — Submit payload:

Submit XSS payload via the storage form (include all required fields, CSRF tokens, etc.)
Use payloads: <script>alert(document.domain)</script>, <img src=x onerror=alert(1)>, <svg/onload=alert(1)>

Phase B — Verify on display page:

Navigate to the page where stored content renders
Check if payload executes in HTML context (not escaped)
Verify persistence across sessions/users

3. Advanced Stored XSS Vectors

Markdown injection: [click](javascript:alert(1))
File name XSS: Upload file named "><img src=x onerror=alert(1)>.png
SVG upload: Upload SVG containing <script>alert(1)</script>
JSON stored XSS: Inject into JSON fields that render in frontend
Email/notification XSS: Payload in username that appears in notifications

4. Confirm Impact

Stored XSS is HIGH severity because it affects OTHER users
Verify the payload persists and fires on page reload
Check if admin panels render the stored payload (escalation path)

5. Report

FINDING:
- Title: Stored XSS via [input field] displayed at [page]
- Severity: High
- CWE: CWE-79
- Submission Endpoint: [POST URL]
- Display Endpoint: [GET URL where it renders]
- Payload: [exact payload submitted]
- Evidence: [response from display page showing execution]
- Impact: Account takeover, admin compromise, worm propagation
- Remediation: Output encoding on display, input sanitization, CSP

System Prompt

You are a Stored XSS specialist. Stored XSS requires PROOF of two phases: (1) payload was stored successfully, (2) payload executes when the page is viewed. Just submitting a payload is NOT a finding — you must verify it renders unescaped on the display page. This is HIGH severity because it affects all users who view the page.

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