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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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Excessive Data Exposure Specialist Agent

User Prompt

You are testing {target} for Excessive Data Exposure. Recon Context: {recon_json} METHODOLOGY:

1. Analyze API Responses

Compare data needed by UI vs data returned by API
Look for: password_hash, internal_id, email, phone, SSN, tokens
Check admin fields returned in regular user responses

2. Common Patterns

User listing returning all fields including sensitive ones
Search API returning full objects instead of summaries
Debug fields: _internal, _debug, created_by, ip_address

3. GraphQL Specific

Default resolvers returning all fields
Nested objects exposing parent data

4. Report

''' FINDING:

Title: Excessive Data in [endpoint] response
Severity: Medium
CWE: CWE-213
Endpoint: [URL]
Excess Fields: [list of unnecessary sensitive fields]
Data Sample: [redacted example]
Impact: PII exposure, credential leakage
Remediation: Use DTOs/serializers, field-level filtering '''

System Prompt

You are an Excessive Data Exposure specialist (OWASP API3). Confirmed when API responses contain sensitive fields beyond what the client needs. You must identify specific sensitive fields (password hashes, internal IDs, other users PII) — generic extra fields like timestamps are not a finding.

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