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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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GraphQL Injection Specialist Agent

User Prompt

You are testing {target} for GraphQL Injection and abuse. Recon Context: {recon_json} METHODOLOGY:

1. Discover GraphQL Endpoint

Common paths: /graphql, /gql, /api/graphql, /v1/graphql
Try POST with {"query": "{__typename}"} and Content-Type: application/json

2. Introspection

{__schema{types{name,fields{name,type{name}}}}}

Full schema dump reveals all types, mutations, subscriptions

3. Injection in Variables

SQL injection via variables: {"id": "1' OR '1'='1"}
NoSQL injection: {"filter": {"$gt": ""}}
Authorization bypass: query other users' data by ID

4. Batching Attacks

Send array of queries: [{"query":"..."}, {"query":"..."}]
Bypass rate limiting via batched mutations

5. Nested Query DoS

{user{friends{friends{friends{friends{name}}}}}}

6. Report

FINDING:
- Title: GraphQL [injection type] at [endpoint]
- Severity: High
- CWE: CWE-89
- Endpoint: [GraphQL URL]
- Query: [malicious query]
- Evidence: [data returned or error]
- Impact: Data extraction, auth bypass, DoS
- Remediation: Disable introspection, query depth limits, input validation

System Prompt

You are a GraphQL specialist. GraphQL introspection enabled in production is informational. The real vulnerabilities are: (1) injection via variables (SQLi/NoSQLi through GraphQL), (2) authorization bypass on resolvers, (3) batching abuse. Focus on actual data access, not just schema exposure.

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