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CyberSecurityUP
255 lines
12 KiB
Markdown
Executable File
255 lines
12 KiB
Markdown
Executable File
# NeuroSploit v3.4.0
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**Autonomous, markdown-driven AI penetration testing — now with a Rust multi-model harness.**
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NeuroSploit turns a URL (or a code repository) into an autonomous security
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engagement. A high-performance **Rust harness** (`tokio` + `axum`) drives a
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**pool of LLM models** with concurrency, **provider failover**, and **N-model
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validator voting** — multiple models must independently agree a finding is real
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before it is reported. After recon, the harness **intelligently selects** which
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of the **249 markdown agents** match the target instead of running them blindly,
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learns across runs via a **reinforcement-learning** reward loop, and serves its
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own polished web dashboard.
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> The Python engine (v3.3.0) and the original monolith live in
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> [`legacy/`](legacy/README.md); the v3.3.0 stdlib dashboard remains in `webgui/`.
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## 🦀 The Rust harness (`neurosploit-rs/`)
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```bash
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cd neurosploit-rs && cargo build --release
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# Web dashboard (black-box + white-box modes)
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./target/release/neurosploit serve # → http://127.0.0.1:8788
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# Black-box: recon → intelligent agent selection → parallel exploit → vote → report
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./target/release/neurosploit run https://target.example \
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--model anthropic:claude-opus-4-8 --model openai:gpt-5.1 --vote-n 3
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# White-box: analyse a repository's source for vulnerabilities
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./target/release/neurosploit whitebox /path/to/repo --subscription --model anthropic:claude-opus-4-8
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# Subscription (no API key) + real browser proof via Playwright MCP
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./target/release/neurosploit run https://t.example --subscription --mcp --model anthropic:claude-opus-4-8
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# Pipeline self-test, no keys/login required
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./target/release/neurosploit run https://t.example --offline
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```
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**What it does**
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- **Two modes** — *black-box* (URL recon → exploit) and *white-box* (walk a repo,
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run code-review/SAST agents on the source).
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- **Intelligent selection** — the model picks the agents whose preconditions match
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the recon, then runs that subset (not top-N).
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- **Multi-model pool** — bounded concurrency, **provider failover**, and the same
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panel forms the **N-model validator jury** that cuts false positives.
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- **Two auth paths** — **model APIs** (provider key) *or* **subscription**: drive
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your local **Claude Code / Codex / Grok / Gemini** logins directly, no API key.
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- **12 providers / 40+ models** (Claude, GPT, Grok, **Gemini**, NVIDIA NIM,
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DeepSeek, Mistral, Qwen, Groq, Together, OpenRouter, Ollama).
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- **RL rewards** persisted to `data/rl_state_rs.json` — validated findings reward
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an agent, biasing the next run.
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- **Artifacts for reuse** — every run writes `runs/<target>-<ts>/`:
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`recon.json/md`, `exploitation.md`, `findings.json/md`, `report.html`.
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- **Playwright MCP** on the subscription path for real browser-based proof.
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### Agent library — 249 agents
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| Category | Dir | Count | Purpose |
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|----------|-----|-------|---------|
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| Vulnerability specialists | `agents_md/vulns/` | 196 | Exploit a specific vuln class |
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| Recon | `agents_md/recon/` | 12 | Information gathering / attack surface |
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| Code (white-box SAST) | `agents_md/code/` | 24 | Source-code vulnerability review |
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| Meta | `agents_md/meta/` | 17 | Orchestrator, validator, scorers, reporter, RL |
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---
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## Why this architecture
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| Old (≤ v3.2.4) | New (v3.3.0) |
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|----------------|-------------|
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| 2,500-line Python orchestrator + hand-coded agent classes | Markdown agents + thin engine |
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| One embedded LLM loop | Pluggable agentic CLI backends (Claude/Codex/Grok) |
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| Provider SDK juggling | Backend owns the agent loop; engine just composes & collects |
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| Static agent list | RL-weighted, recon-aware agent selection |
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| Reflection-based "evidence" | Playwright MCP proof-of-execution + adversarial validation |
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---
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## How it works
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```
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┌──────────────────────────────────────────────────────────────┐
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URL ──▶ │ neurosploit (terminal) │
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│ │ │
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│ ▼ │
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│ orchestrator ── loads agents_md/ (213) ── applies RL weights │
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│ │ │
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│ ▼ composes ONE master prompt │
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│ backend (Claude Code | Codex | Grok) ◀── Playwright MCP │
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│ │ autonomously runs the pipeline below │
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│ ▼ │
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│ recon → select agents → exploit → VALIDATE → filter FPs │
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│ → severity → impact → report → RL feedback │
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└──────────────────────────────────────────────────────────────┘
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│ │
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▼ ▼
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results/findings.json data/rl_state.json (learns)
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```
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The engine never fabricates findings: every candidate is independently
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re-exploited (`meta/exploit_validator`), run through an adversarial skeptic
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(`meta/false_positive_filter`), and only then scored and reported.
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---
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## The agent library (`agents_md/`)
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**213 agents** — see [`agents_md/REGISTRY.md`](agents_md/REGISTRY.md).
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- **196 vulnerability specialists** (`agents_md/vulns/`) — each a self-contained
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playbook with a real methodology, payloads, CWE mapping, and a strict
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anti-false-positive `## System Prompt`. Coverage includes the classic OWASP
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web set **plus modern classes**:
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- **LLM/AI security** (OWASP LLM Top 10): prompt injection (direct/indirect),
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jailbreak, system-prompt leak, insecure output handling, RAG poisoning,
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tool-invocation/function-calling abuse, excessive agency, PII leakage…
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- **Cloud/K8s/containers**: IMDS SSRF (AWS/GCP/Azure), kubelet/dashboard
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exposure, container & docker-socket escape, bucket takeover, IAM privesc…
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- **Modern API/auth**: JWT alg/kid/jwk confusion, OAuth PKCE downgrade, SAML
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XSW, OIDC, CSWSH, refresh-token & MFA bypass, account-takeover chains…
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- **Advanced injection**: SSTI (Jinja2/FreeMarker/Velocity/Thymeleaf), SSPP,
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XXE OOB, YAML/pickle deserialization, JNDI, XSLT…
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- **Protocol/cache/smuggling**: HTTP/2 & CL.TE/TE.CL desync, h2c, web cache
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deception/poisoning, response splitting, path-confusion…
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- **Logic/crypto/supply-chain**: dependency confusion, padding oracle, weak
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JWT secret, price/coupon/workflow abuse, exposed `.git`/`.env`/CI secrets…
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- **17 meta-agents** (`agents_md/meta/`): `orchestrator`, `recon`,
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`exploit_validator`, `false_positive_filter`, `severity_assessor`,
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`impact_evaluator`, `reporter`, `rl_feedback`, plus migrated expert roles.
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Add your own by dropping a `.md` into `agents_md/vulns/` (or extend the
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data-driven builder, `scripts/build_agents.py`). It is picked up automatically.
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---
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## Quickstart
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```bash
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# 1. Have at least one agentic CLI installed: Claude Code, Codex, or Grok CLI
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# (Playwright MCP needs Node/npx)
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./neurosploit backends # show what's detected
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./neurosploit agents # {'vulns': 196, 'meta': 17, 'total': 213}
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# 2. Interactive: enter a URL, pick a backend + model, go
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./neurosploit
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# 3. Or one-shot:
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./neurosploit run https://target.example \
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--backend claude --model claude-opus-4-8 \
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--collaborator oob.your-collab.net
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# 4. Preview the composed master prompt without executing the backend:
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./neurosploit run https://target.example --dry-run
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```
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Outputs land in `results/<target>/findings.json` and `reports/`, and the RL
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state updates in `data/rl_state.json`.
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### Web dashboard
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A zero-dependency (Python stdlib only) dashboard — no npm, no build step:
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```bash
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python3 webgui/server.py # → http://127.0.0.1:8787
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```
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Tabs:
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- **Run** — multi-target input, backend + provider + model pickers (40 models
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across CLI and API providers), verbosity, RL/MCP toggles, a live execution
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console (shows the exact backend command and per-task activity), and findings
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with screenshots.
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- **Agents** — browse all 213 agents and **add new `.md` agents** from the UI;
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the main orchestrator picks them up on the next run.
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- **Insights** — interactive chart of RL agent weights + findings by severity.
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- **Reports** — download/preview the **PDF + HTML** reports (Typst engine).
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- **Settings · API** — execution mode (CLI vs API), per-provider API keys,
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orchestrator selection, default verbosity.
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It calls `neurosploit_agent` directly. The previous React app and FastAPI backend
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were retired to `legacy/` (`frontend_react/`, `backend_fastapi/`).
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### Backends
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| Backend | Binary | Autonomy flag | Subscription |
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|---------|--------|---------------|--------------|
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| Claude Code | `claude` | `--dangerously-skip-permissions` | ✅ via Claude login |
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| Codex CLI | `codex` | `--dangerously-bypass-approvals-and-sandbox` | — |
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| Grok CLI | `grok` | `--yolo` | — |
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The engine auto-detects installed backends and only offers those. In the
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interactive flow, answering **yes** to "Use Claude subscription" runs Claude Code
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against your logged-in subscription instead of an API key.
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### Models
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Latest models per provider live in `neurosploit_agent/models.py`, including the
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**NVIDIA NIM** provider (PR #28, OpenAI-compatible at
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`https://integrate.api.nvidia.com/v1`, `nvapi-` keys), Anthropic Claude 4.x,
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OpenAI, xAI Grok, Gemini, OpenRouter, and local Ollama.
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---
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## Reinforcement learning
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Every run produces per-agent reward signals (`meta/rl_feedback` +
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`neurosploit_agent/rl.py`): validated findings reward an agent (weighted by
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severity), rejected false positives penalize it, correct skips stay neutral.
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Weights are bounded `[0.05, 1.0]` and carry per-tech-stack affinity, so the
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engine learns, e.g., to prioritize `ssti_jinja2` on Flask targets. State is
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explainable and persisted to `data/rl_state.json`.
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---
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## Safety & authorization
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NeuroSploit is for **authorized** security testing only. Every agent's system
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prompt enforces scope and proof-of-exploitation; DoS-class agents refuse to
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flood and require explicit rules-of-engagement. You are responsible for having
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written permission for any target you point it at.
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---
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## Repository layout
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```
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neurosploit # launcher (./neurosploit)
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neurosploit_agent/ # the v3.3.0 engine
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cli.py orchestrator.py agent_loader.py backends.py rl.py mcp.py models.py config.py
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agents_md/
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vulns/ (196) # vulnerability specialist agents
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meta/ (17) # orchestrator, recon, validator, scorers, reporter, RL, roles
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REGISTRY.md # generated index
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scripts/build_agents.py # data-driven agent builder
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legacy/ # retired pre-v3.3.0 Python orchestration
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```
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See [`RELEASE.md`](RELEASE.md) for the full v3.3.0 changelog.
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---
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## License
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MIT.
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