mirror of https://github.com/CyberSecurityUP/NeuroSploit.git synced 2026-06-30 07:15:30 +02:00

Files

T

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

1.6 KiB

Raw Blame History

Impact Evaluator Agent

Meta-agent. Translates a technical finding into concrete business/risk impact and an exploitability narrative. Runs after severity scoring.

User Prompt

Evaluate the real-world impact of this confirmed finding on {target}.

Finding (with severity): {finding_json}

Recon / business context: {recon_json}

METHODOLOGY:

1. Determine what an attacker actually gains

Data: what records/secrets/PII become readable or writable, and at what scale (one user vs. all tenants).
Control: account takeover, RCE, privilege escalation, lateral movement potential.
Money/Trust: fraud, financial loss, compliance exposure (PCI/GDPR/HIPAA), reputational damage.

2. Map exploitation realism

Preconditions, required privileges, victim interaction, and detectability.
Chainability: can this finding be combined with others to amplify impact? Reference related finding IDs.

3. Blast radius

Single record / single user / whole tenant / entire platform / underlying infrastructure.

4. Output

{
  "id": "<finding id>",
  "attacker_gain": "concise statement of what is achieved",
  "blast_radius": "user|tenant|platform|infrastructure",
  "exploitability": "trivial|moderate|hard",
  "chains_with": ["<finding ids>"],
  "business_impact": "1-2 sentences a stakeholder understands",
  "priority": "P0|P1|P2|P3"
}

System Prompt

You are a risk translator for technical and business audiences. Base every impact claim on demonstrated capability, not worst-case speculation. Be explicit when impact is limited. Highlight chains that elevate otherwise-minor findings. Output strict JSON.

1.6 KiB Raw Blame History