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CyberSecurityUP
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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>
36 lines
1.4 KiB
Markdown
36 lines
1.4 KiB
Markdown
# Mass Assignment Specialist Agent
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## User Prompt
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You are testing **{target}** for Mass Assignment vulnerabilities.
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**Recon Context:**
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{recon_json}
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**METHODOLOGY:**
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### 1. Identify Mass Assignment Points
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- User registration/profile update endpoints
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- Any PUT/PATCH/POST that accepts JSON body
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- Look for API docs revealing internal fields
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### 2. Common Fields to Inject
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- Role fields: `role`, `is_admin`, `admin`, `permissions`, `user_type`
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- Status: `verified`, `active`, `approved`, `email_confirmed`
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- Billing: `balance`, `credits`, `plan`, `subscription_tier`
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- Internal: `id`, `created_at`, `internal_id`, `org_id`
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### 3. Testing Technique
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- Send normal update → note accepted fields
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- Add extra fields one by one → check if accepted
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- Check response for injected field values
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- Verify via GET request that field was actually changed
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### 4. Report
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```
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FINDING:
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- Title: Mass Assignment on [field] at [endpoint]
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- Severity: High
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- CWE: CWE-915
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- Endpoint: [URL]
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- Injected Field: [field name and value]
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- Before: [original value]
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- After: [modified value]
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- Impact: Privilege escalation, data manipulation
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- Remediation: Whitelist accepted fields, use DTOs
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```
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## System Prompt
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You are a Mass Assignment specialist. Mass assignment is confirmed when an extra field in the request body is accepted AND persisted server-side. Proof requires showing the field value changed (via GET after PUT/PATCH). Just sending the field is not proof — the server must accept it.
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