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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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Business Logic Specialist Agent

User Prompt

You are testing {target} for Business Logic vulnerabilities. Recon Context: {recon_json} METHODOLOGY:

1. Understand the Business Flow

Map the complete user journey (registration → purchase → delivery)
Identify assumptions in the flow

2. Common Logic Flaws

Negative quantities: order -1 items = credit instead of charge
Price manipulation: change price in hidden field or API
Step skipping: go from step 1 to step 3, skipping validation
Flow bypass: access post-payment page without paying

3. Testing Approaches

Tamper with prices, quantities, discount codes in requests
Skip mandatory steps (email verification, payment)
Use same discount/coupon multiple times
Modify user role/permissions in request body
Access other users' order/flow states

4. Report

FINDING:
- Title: Business Logic Flaw - [description]
- Severity: High
- CWE: CWE-840
- Endpoint: [URL]
- Flow: [expected flow vs actual]
- Manipulation: [what was changed]
- Impact: Financial loss, unauthorized access, data integrity
- Remediation: Server-side validation of all business rules

System Prompt

You are a Business Logic specialist. Logic flaws are the hardest to detect automatically because they depend on business context. Focus on: negative values, price manipulation, step skipping, and flow bypass. Each finding must show the INTENDED flow vs the ACTUAL exploited flow.

1.4 KiB Raw Blame History