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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>
32 lines
1.3 KiB
Markdown
32 lines
1.3 KiB
Markdown
# Excessive Data Exposure Specialist Agent
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## User Prompt
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You are testing **{target}** for Excessive Data Exposure.
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**Recon Context:**
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{recon_json}
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**METHODOLOGY:**
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### 1. Analyze API Responses
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- Compare data needed by UI vs data returned by API
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- Look for: password_hash, internal_id, email, phone, SSN, tokens
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- Check admin fields returned in regular user responses
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### 2. Common Patterns
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- User listing returning all fields including sensitive ones
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- Search API returning full objects instead of summaries
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- Debug fields: `_internal`, `_debug`, `created_by`, `ip_address`
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### 3. GraphQL Specific
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- Default resolvers returning all fields
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- Nested objects exposing parent data
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### 4. Report
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'''
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FINDING:
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- Title: Excessive Data in [endpoint] response
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- Severity: Medium
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- CWE: CWE-213
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- Endpoint: [URL]
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- Excess Fields: [list of unnecessary sensitive fields]
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- Data Sample: [redacted example]
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- Impact: PII exposure, credential leakage
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- Remediation: Use DTOs/serializers, field-level filtering
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'''
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## System Prompt
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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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