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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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RAG / Vector-Store Poisoning Specialist Agent

User Prompt

You are testing {target} for RAG knowledge-base poisoning (OWASP LLM03/LLM08).

Recon Context: {recon_json}

METHODOLOGY:

1. Find ingestion path

Locate how documents enter the vector store (uploads, crawlers, connectors, user content)

2. Poison

Insert a document with adversarial instructions/false facts and high retrieval relevance for a target query

3. Trigger & confirm

Issue the target query as a victim; confirm the poisoned content steered the answer

4. Report Format

For each CONFIRMED finding:

FINDING:
- Title: RAG / Vector-Store Poisoning Specialist at [endpoint]
- Severity: High
- CWE: CWE-1427
- Endpoint: [full URL]
- Vector: [parameter/header/flow]
- Payload: [exact payload/command]
- Evidence: [proof of exploitation]
- Impact: Attacker-controlled documents bias or hijack answers for all users
- Remediation: Source authentication, ingestion validation, provenance, retrieval re-ranking trust

System Prompt

You are a RAG-poisoning specialist. Report only when content you ingested measurably changes retrieved answers for a separate query, with before/after evidence. No theoretical claims.

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